Authra: Three-Trust Fabric
Presence • Performance • Provenance
Whitepaper (V4.4)
Executive Summary
Authra is a decentralized physical infrastructure network (DePIN) that weaves a Three-Trust Fabric for the physical internet:
Presence (provable location and time), Performance (end-user Quality-of-Experience), and Provenance (device and pipeline integrity + chain/network health).
Authra converts everyday devices into signed, privacy-preserving sensors. Devices contribute signed measurements at the edge; and at the protocol layer, a sequencer orders batches on an Arbitrum Orbit L3 (optimistic rollup); state commitments are posted and become final after the fault-dispute window (BoLD-style). AnyTrust DAC provides low-cost data availability with Rollup DA fallback. An AI ‘immune system’ provides plausibility checks, cross-validation, and privacy-preserving behavioral fingerprints to keep data honest at scale; only high-confidence reports influence rewards and enterprise analytics. This fabric underpins telecom SLAs, regulator audits, smart-city reliability, Web3 oracles, and defense resilience—using privacy-preserving, verifiable data and fiat-first commercial rails.
Who it serves: contributors and developers (TruePing), protocol participants (Authra validators), and enterprises/governments (Terrascient).Together, these components establish a global “trust layer” for physical network data – offering reliable, real-time insights into where devices are and how well networks perform, with cryptographic proofs ensuring data integrity. Authra is positioned as both a piece of public infrastructure and a powerful enterprise intelligence layer.
Three-Trust Fabric (Presence • Performance • Provenance):
Authra’s trust model centers on three independent, composable assurances:
• Presence — Multi-signal Proof-of-Presence (GNSS + cellular/Wi-Fi/BT coherence) with TEE-backed device attestation and optional precision modes.
• Performance — Continuous QoE telemetry (latency, jitter, throughput, reliability, uptime) with anti-gaming filters, cohort coherence, and SLO-oriented aggregates.
• Provenance — Cryptographic lineage for every datum: signed payloads, batch commitments (Merkle), public anchoring on Orbit L3, delayed-inbox force-inclusion, and verifiable replayable audit trails.
This separation lets buyers consume “where,” “how well,” and “how we know” as distinct, audit-ready artifacts—without new hardware or privacy compromises.
Problem & Opportunity
Organizations today face critical blind spots in network intelligence. Telecom providers may claim high uptime, yet customers still encounter outages; governments invest billions in broadband but lack independent verification of coverage; enterprises suffer from location fraud and false service claims . Existing monitoring solutions rely on synthetic data center probes or self-reported metrics that either miss the user’s actual experience or can be easily spoofed. The rise of autonomous systems and smart infrastructure has made trustworthy real-world data even more essential. Authra addresses this gap by crowdsourcing ground-truth data from millions of smartphones and validating each data point via secure hardware and distributed consensus, so it cannot be faked. Everyday users (“contributors”) are rewarded with Authra’s native token $ATRX for participating, while enterprises and governments gain access to an unprecedented live map of network quality and device presence that they can trust for critical decisions . This creates a virtuous cycle: more users provide more verified data, improving coverage and insight, which in turn attracts greater enterprise usage and value flowing back to the ecosystem .
Solution Overview
Authra’s architecture is designed for end-to-end security, privacy, and scalability. The lightweight TruePing mobile app (and SDK) runs on user devices to gather latency, bandwidth, and location proofs with minimal battery impact (<3% daily) . Data from many users is aggregated and sequenced on Authra’s Arbitrum Orbit (Nitro) chain; state commitments are periodically posted upstream to Arbitrum One and settled on Ethereum L1 with a fraud-proof window. Disputes are handled via permissionless BoLD validation. In AnyTrust mode, is attested by a DAC for lower fees; we retain a clean migration path to Rollup mode if stricter DA is required.
This creates a public, auditable trail of network performance and presence records without exposing personal information (raw GPS coordinates or identities are never stored on-chain). Finally, the Terrascient platform presents this intelligence via dashboards, APIs, and reports tailored for enterprise and government use, including predictive analytics powered by built-in AI modules . All sensitive data handling follows global best practices for privacy and security (e.g. GDPR privacy-by-design, SOC 2 controls, FedRAMP for government cloud) .
Key Differentiators:
Authra’s Three-Trust Fabric fuses (1) Presence, (2) Performance, and (3) Provenance (device attestation, risk-weighted rewards, and Chain-Health telemetry) to provide verifiable, enterprise-grade network intelligence. Unlike single-purpose decentralized networks that address only connectivity or only location, Authra provides three complementary proofs in one unified platform . Crucially, no new hardware is required – Authra harnesses the billions of smartphones already in use, avoiding any capital expense for custom sensors . The use of cryptographic device attestation (via smartphone secure enclaves) and multi-party validator consensus makes data integrity verifiable by anyone, bringing a blockchain-like level of trust to real-world events . In effect, Authra becomes a “truth oracle” for connectivity and location – delivering value to telecom carriers, cloud providers, logistics companies, smart cities, defense agencies, and any domain where digital services meet physical reality . By combining Web3 token incentives with real enterprise demand, Authra aims to bootstrap a planetary-scale sensor network and establish a new standard for infrastructure intelligence .
This whitepaper details Authra’s chain strategy and architecture, tokenomics, AI-powered TrustMesh intelligence, platform components, and its approach to compliance and security. We also illustrate a pivotal use case (telecom SLA verification) to demonstrate how Authra provides unique value in practice. The presentation is designed for a global audience of technical experts, enterprise decision-makers, investors, and regulators – balancing technical depth with business and policy clarity.
INTRODUCTION:
The Need for Verifiable Network Intelligence
Modern economies run on network connectivity, yet reliable data about network performance and device presence remains surprisingly hard to obtain. Telecom operators publish coverage maps and uptime statistics, but these are often taken on faith and can be inaccurate or overstated. Regulators and governments subsidize broadband rollouts and enforce service obligations, but lack independent tools to verify that citizens truly receive the promised quality of service . Enterprises that rely on connectivity – from cloud service providers to logistics firms – often have to trust carrier reports or deploy expensive monitoring hardware, and they remain vulnerable to location-based fraud (e.g. a driver claiming to be at a delivery point when they are not, or a user spoofing location to gain access to region-locked services) . The advent of autonomous vehicles, IoT sensors, and remote work means trustworthy real-world data (like “Is this device really where it claims?” or “Is the network actually performing as expected for users on the ground?”) is more critical than ever .
However, existing solutions have limitations. Traditional network monitoring platforms (e.g. Cisco ThousandEyes, Catchpoint) deploy probes in data centers or enterprise sites, capturing core network metrics but often missing the last-mile perspective of actual end users on mobile and residential networks . Crowdsourced apps like Ookla Speedtest or Opensignal gather user-generated data, but they typically rely on user altruism or curiosity, without strong incentives, and they lack verifiability – data can potentially be spoofed or may not meet rigorous evidence standards . Decentralized projects have tried to tackle pieces of this puzzle (Helium for crowdsourced wireless coverage, FOAM and XYO for location proofs, etc.), but none combine user-level network performance data with robust presence verification in one platform . Moreover, many such projects required dedicated hardware or did not gain enterprise adoption, limiting their impact .
Authra was conceived to address these gaps by creating a global, decentralized network of smartphone-based sensors that feed into a tamper-proof public ledger, making the data self-verifying. The goal is to provide a source of truth for network performance and presence that is as trusted and neutral as a financial blockchain ledger, yet focused on real-world infrastructure metrics. By doing so, Authra unlocks numerous opportunities: regulators can objectively audit telecom SLAs, enterprises can get real-time performance intelligence without deploying new hardware, and new applications (like smart contracts that react to physical-world events) become possible with high assurance.
The following sections describe Authra’s architecture and approach in detail – from the blockchain layer and token economics that incentivize participation, to the AI systems that enhance data quality, to the user and enterprise interfaces that ensure wide adoption – all engineered to deliver a secure, scalable, and compliant network intelligence platform.
Authra Platform Overview and Architecture
tl;dr: Chain Strategy: Single Orbit, Modular Compliance
• One global Orbit L3, anchored to Arbitrum One → Ethereum for security and liquidity.
• Modular compliance at the contract layer (Global, Enterprise, Regulated modes) rather than chain splits.
• AnyTrust DA primary; Rollup fallback for stricter assurance.
• Timeboost (opt-in) and BoLD fraud proofs; censorship resistance via Delayed Inbox.
The Three-Layer Global Trust Fabric (architecture view)
Layer 1 — Device Integrity (auditable, sovereign-ready):
Only attested and whitelisted devices contribute proofs. Runtime checks and red-team adversarial testing harden the edge. We publish open verifier tools and proof harnesses so third parties can independently validate device attestations and signature flows. (Pre-NDA: we omit attestation policies, device class whitelists, and rejection thresholds.)
Layer 2 — Presence + Performance Proofs (Authra’s wedge):
A single pipeline produces dual proofs: Proof-of-Presence (PoP) via multi-signal coherence (GNSS, cellular, Wi-Fi, Bluetooth) and Quality-of-Experience (QoE) via lightweight, adaptive measurements. Each record is TEE-signed on-device, aggregated into Merkle batches, and committed on-chain for public audit.
Precision modes (optional): For high-assurance contexts (e.g., SLAs, defense, fraud cases), validators may request GNSS-RTK–enhanced artifacts verified against third-party correction networks (e.g., base-station feeds) to achieve sub-meter confidence. Default PoP remains multi-signal GNSS+RF for broad coverage. As a soft presence corroborant—never as the primary proof—the app may record privacy-preserving BLE encounter sketches in background to strengthen visit/footfall confidence where OS allows.
Layer 3 — Resilient Transport (defense-grade continuity):
Proofs propagate even in degraded networks via offline buffering, delay-tolerant bundling, and optional mesh relays where permitted. When connectivity returns, bundles are sequenced and anchored; if censorship is suspected, users retain Delayed Inbox force-inclusion to the parent chain. (Roadmap controls and mesh policies are disclosed under NDA.)
Authra’s platform is composed of three core layers, each tailored to a segment of the user base but working in unison:
Authra Core Blockchain (Protocol Layer): A dedicated blockchain network optimized for recording proof attestations and managing the $ATRX token economy. This layer handles data validation via an Optimistic rollup execution (Arbitrum Nitro), sequenced blocks with fraud-proof security; AnyTrust DAC for DA by default, with Rollup-mode fallback and anchors proofs on-chain, and enforces the rules for rewards, staking, and governance. It is the trust engine that guarantees each reported data point is verified by hardware attestation and consensus agreement before being accepted .
TruePing (Edge & Developer Layer): The bridge between the raw data collection and the consumers of that data . TruePing has two facets:
A mobile app installed by contributors (end-users) on their smartphones, which runs passive network tests and presence checks in the background. It turns network pings and location check-ins into a passive income stream for users, rewarded in $ATRX.
A developer API & SDK that exposes the verified data to third-party applications and services via simple endpoints (e.g. for querying latency in a location, or verifying a device’s presence) . This allows telecom operators, web services, or even smart contracts to easily integrate Authra’s “proof feeds” into their systems, effectively making Authra “Stripe for network verification”.
Standards Interop (Passpoint/OpenRoaming): TruePing can issue Passpoint/OpenRoaming profiles so users roam onto trusted Wi-Fi automatically with EAP-based authentication (e.g., TTLS/AKA/AKA’). This yields privacy-preserving association/roam telemetry (success/fail, dwell, handoffs) that feeds our QoE and SLA analytics. We’ll publish an interop matrix for MNO/MVNO/venue partners and operate standards-compliant IdP/RADIUS infrastructure. No chain changes required—only app profile provisioning and partner onboarding.
Terrascient (Enterprise Intelligence Layer): The analytics platform and dashboard for enterprise and government users. Terrascient provides rich visualization, reporting, and predictive analytics on top of Authra’s data – think of it as “Palantir for infrastructure intelligence”. It can be deployed in a secure cloud or even on-premises (air-gapped) for sensitive clients, aligning with FedRAMP and other standards for handling sensitive data . Terrascient translates the deluge of raw proofs and metrics into actionable insights through trend analysis, anomaly alerts, and AI-driven forecasting, all accessible through an intuitive interface or via APIs. Enterprise-grade UX without requiring Web3 savvy.
Each component targets a specific audience in their “comfort zone”: everyday contributors interact through a gamified mobile app, validators and crypto participants engage at the blockchain protocol level, developers tap into APIs/SDKs, and enterprises/regulators use a polished web platform. This separation of concerns creates an end-to-end pipeline from crowdsourced data → on-chain validation → consumable insights , ensuring that every stakeholder sees value in a form that’s useful to them.
Below, we dive deeper into Authra’s chain strategy and architecture, how it achieves compliance and scalability, the security model, and the design decisions that make the network both robust and adaptable.
Blockchain Design & Chain Strategy
Authra operates on a dedicated blockchain network designed to maximize data integrity, scalability, and regulatory flexibility. The strategy is to maintain one unified global chain (the “Orbit” chain) rather than fragmenting into multiple regional chains, while using modular features to handle diverse compliance requirements .
Unified Orbit Chain: All Authra activity is settled on a single main chain (anchored periodically to Ethereum for security) . This unification ensures:
Shared Security and Liquidity: All participants (nodes, users, investors) support the same token and ledger, preventing fragmentation of $ATRX liquidity or duplicative infrastructure.
Developer Simplicity: There is one common chain configuration (sequencer, bridge, and DA/DAC), one SDK, and one integration path globally—partners don’t need to target multiple networks.
Operational Efficiency: The team maintains and upgrades one network, resulting in lower costs and faster iteration than managing many siloed chains . Features and fixes roll out network-wide, and the system’s full network effect is realized globally rather than in isolated pockets.
Base: Arbitrum Orbit (Nitro) L3
Settlement: Arbitrum One (L2) → Ethereum L1
Execution: EVM + Stylus (Rust/C) enabled at genesis for high-perf code paths.
Data availability (DA):
Primary DA: AnyTrust (DAC) at L3 (custom gas in $ATRX; minimal fees). Rollup mode remains our Plan B (L1 calldata/blobs) if DAC trust is questioned or a regulator requires stricter DA; migration is a planned regenesis with dual-run & state snapshot. DAC policy: publish member list, quorum (e.g., M-of-N signatures), rotation cadence, and failure procedures in docs.
Fall-back / Plan B: Flip to Rollup mode (Ethereum calldata/blobs via L2) if DAC trust is questioned or regulator requires stricter DA. (Operationally this is a planned “regenesis” migration with dual-run & state snapshot.) Finality and withdrawals follow the parent chain’s challenge period; users retain censorship-resistant “force-inclusion” via the Delayed Inbox.
AnyTrust Rationale & Policy: AnyTrust DA (cost & UX) with Rollup fallback. We use AnyTrust DA at L3 to keep per-proof fees dramatically lower and inclusion near-real-time by posting commitments on-chain while an independent, geographically dispersed DAC attests to data retention. We publish member list, M-of-N quorum, rotation cadence, and failure procedures, plus per-batch signature sets and signer liveness. If quorum degrades or stricter assurance is required, the chain falls back to Rollup mode (on-chain DA) as the default safety stance. Over time, governance broadens DAC membership (including community-run signers) and evaluates Orbit-compatible DA alternatives as they mature.
Sequencing & ordering:
Start with single primary sequencer + hot standby, move to multi-sequencer with Timeboost auctions when we open order-flow (policy controlled by governance; bids in $ATRX). Timeboost is chain-opt-in, supports arbitrary ERC-20 for bids.
Validation & disputes:
(“Arbitrum Timeboost” – optional): The sequencer can auction priority inclusion for fair ordering with economic finality.
(“Arbitrum BoLD” – fraud proofs): Permissionless challengers can dispute invalid state transitions; disputes ultimately resolve on Ethereum, inherit the L2/L1 guarantees.
Explicitly document the Delayed Inbox / force-inclusion path and expose tooling in our docs/UI (force-include after ~24h). Practically, if the sequencer fails to include transactions, users (or relayers) can submit them via the Orbit delayed inbox; after the force-inclusion window elapses, they must be processed per canonical ordering.
Sequencer, Challenger, and Infrastructure Policy (Best Practices)
Inclusion & Censorship-Resistance: The sequencer commits to publish a canonical mempool policy and MUST include any valid transaction within the force-inclusion window. Repeated censorship or failure to include valid transactions triggers automatic rotation procedures. CLI + UI documented for delayed-inbox submission; target ≤24h force-include window.
Liveness & Rotation: A standby sequencer (and/or committee) is designated with on-chain failover conditions (e.g., N consecutive missed intervals). Rotation events, along with the hash of the last processed inbox message, are posted on-chain. ≥99.9% sequencer availability; automatic failover after N missed intervals.
Challenger Set: Any party may run a challenger for BoLD fraud proofs. Minimum hardware specs and reference Docker images will be documented; slashing or bonding parameters for misbehavior (spam challenges) are set by governance pre-mainnet.
DA Provider Policy (AnyTrust DAC): DAC members sign availability certificates; honest majority assumption and key-rotation cadence published. If DA commitments fail (e.g., insufficient signatures), rollup mode activation is the default fallback.
Logging & Transparency: Sequencer and DAC publish audit logs of inclusion, timestamps, and signature sets. Weekly attestation reports, Merkle-rooted inclusion/latency logs and DAC signature sets committed on-chain.
Parameter Governance: All thresholds (e.g., rotation intervals, inbox windows) are set via governance with public RFCs; emergency council may enact temporary overrides with on-chain timelock and mandatory post-mortems.
Authra Chain-Health Oracle (new). We continuously compute and publish an integrity score for the chain and DA path so operators, users, and integrators can automate mitigations and audits.
Endpoint: GET /api/v1/oracle/chain_health → { score: 0–100, as_of, components: { sequencer_uptime, inclusion_latency_p50/p95, delayed_inbox_depth, delayed_inbox_tti, dac_quorum_freshness, anchor_lag, open_disputes, challenge_resolution_time } }
Stream: WS /stream/chain_events (sequencer failover, DAC quorum dips/stalls, force-include activations, anchor commits, fraud-proof lifecycle).
Computation: Weighted composite of (i) sequencer availability and inclusion latency, (ii) delayed-inbox queue depth/time-to-force-include, (iii) AnyTrust DAC quorum freshness (M-of-N signer liveness, last cert age), (iv) L3→L2/L1 anchor lag, and (v) active dispute/BoLD metrics.
Governance: Weights and alert thresholds are parameters governed on-chain; sub-thresholds trigger documented actions (e.g., sequencer rotation, DAC escalation, Rollup fallback).
Verifiability: Oracle snapshots post a Merkle root of underlying logs the paper already commits to publishing, enabling third-party recomputation and audit.
Resilient Transport (Layer 3)
Offline/DTN mode: Devices buffer and sign proofs locally; gateways bundle and submit upon connectivity restoration.
Mesh-aware relay (optional/controlled): Where policy allows, nearby devices relay commitments (no PII) to increase chance of inclusion during partial outages.
Audit continuity: Bundle headers include sequence hints and time bounds so auditors can verify no gaps in inclusion after delayed submission.
Non-Consensus Roles (community-run, bountyable)
Scanners. Log/metrics scanners that watch inclusion latency, DAC signer liveness, delayed-inbox depth, and abnormal error rates across the data pipeline and contracts. Emit signed reports to the Chain-Health Oracle and raise alerts.
Indexers. Public, stateless indexer nodes that maintain low-latency query caches (REST/GraphQL) for search/analytics; not part of consensus.
Bridges/Relayers. Attested relayers that publish cross-chain proof bundles (e.g., L3→L2/L1 proof roots, QoE oracle updates) with replay protection and rate limits. Subject to slashing/bounties per governance policy.
(All features described at capability level; no sensitive parameters.)
Why this mix:
AnyTrust gives us custom gas in $ATRX and dramatically cheaper fees (critical for phone-sourced micro-tx) while BoLD + parent-chain settlement + force-inclusion preserve credible neutrality and liveness. If/when a regulator demands stricter DA, we have a clean Rollup-mode Plan B with well-understood cost/assurance trade-offs.
Modular Compliance Framework: Instead of separate blockchains for different jurisdictions, Authra builds compliance controls into the smart contract layer of the single chain. Devices, data contributors, or enterprise clients can be tagged with compliance designations that govern their participation, such as:
Global (Open) – the default permissionless regime for general users.
Enterprise (KYB-verified) – participants verified via Know-Your-Business processes, suitable for commercial deployments.
Regulated (Encrypted/Government) – high-security mode for government or defense use, with additional encryption or restrictions .
These modes are enforced by on-chain logic (for example, certain data from Regulated devices might be auto-encrypted or only accessible to permissioned viewers), allowing Authra to serve multiple regulatory needs on one chain without splitting the network . In practice, this means an enterprise or government user can participate in Authra’s global network but with custom safeguards – e.g., data from a defense client’s devices might be stored off-chain or only as zero-knowledge proofs on-chain. This approach provides flexibility akin to having separate networks, but retains the benefits of one unified ecosystem .
Off-Chain Data and Regional Gateways: To further accommodate data residency and privacy laws, Authra leverages traditional infrastructure at the edges:
Regional API Gateways enforce local rules before data hits the blockchain. For example, an EU gateway could filter or anonymize data to meet GDPR requirements (like stripping precise location or personal metadata) .
Encrypted Off-Chain Storage allows raw telemetry or sensitive information to remain in-region (EU, US, APAC, etc.), while only hashed references or zero-knowledge proofs are recorded on-chain . This ensures compliance with data localization laws and sensitive deployment needs (DoD, government clouds) without requiring separate blockchains per region .
Selective Data Commitment: Only necessary proof data (often in hashed form) is committed to the ledger with only hashes or ZK proofs going on-chain.
Bulk raw data can reside in secure databases or cloud storage, reducing on-chain bloat and exposure . If needed, verifiers can request the raw data out-of-band and check it against the on-chain hash to audit authenticity, while personal data never leaves the region’s storage.
Through these mechanisms, Authra achieves compliance parity with strict regulations (GDPR, defense security classifications, Chinese data localization rules, etc.) while still running a single global network . This is a key architectural choice to balance regulatory alignment with network unity.
Execution, Sequencing & Dispute Game (Orbit)
Optimistic Rollup on Orbit: Authra runs as an Arbitrum Orbit L3: ordering is performed by a sequencer (initially a primary with hot standby), and state transitions execute on Nitro. Safety derives from optimistic fraud proofs (Arbitrum BoLD) that allow any permissionless challenger to dispute an invalid state transition; disputes resolve on the parent chain, inheriting L2/L1 guarantees. Users retain censorship resistance via the Delayed Inbox “force-inclusion” path if the sequencer withholds transactions. Timeboost priority auctions remain chain-opt-in and, if enabled, are paid in $ATRX per governance policy. The consensus process works as follows:
Batching of Proofs: TruePing submissions are aggregated off-chain into Merkle trees; blocks commit the Merkle root for hundreds or thousands of proofs rather than each report individually. This preserves verifiability (any proof can be checked against the on-chain root) while keeping throughput high and fees low; commitments are posted to Authra L3 and anchored upstream to Arbitrum One and Ethereum L1.
Anchoring to Ethereum L2: Authra deploys as an Arbitrum Orbit L3 that settles to Arbitrum One (L2), which itself settles to Ethereum (L1). This gives us Ethereum-grade security properties with significantly lower fees and faster confirmations, while keeping a straightforward path to L1 data availability if/when required.
Roles & incentives: At the chain layer we speak of sequencers and challengers “(not BFT validators)”. Challengers post bonds and are rewarded for successful fraud proofs; spam or malicious challenges are penalized per governance parameters. Operational policies for inclusion, liveness/rotation, logging, and DAC data-availability attestations remain as specified elsewhere in this section.
Non-Consensus Node Roles (bountied): To harden ingestion and improve interoperability, Authra defines non-consensus roles with bounties:
Scanner — log/metrics scanners watching pipeline health, anomaly spikes, replayability.
Indexer — low-latency chain/index services backing queries, heatmaps, and forensic drill-downs.
Bridge/Relayer — cross-chain feeds of summarized commitments and SLA signals.
These roles do not affect safety (optimistic proofs handle safety) but expand resilience and developer utility.
Authra Chain-Health Oracle (Governance/Operations)
We publish a 0–100 Chain-Health score that continuously reflects operational integrity and liveness:
Inputs (non-exhaustive):
Sequencer uptime and inclusion latency
Delayed inbox depth and time-to-force-include
DAC quorum freshness (AnyTrust)
Anchor lag to parent chains (L3→L2/L1)
Active disputes and resolution times
Programmatic outputs:
/oracle/chain_health → current score and components
/stream/chain_events → live events (sequencer failover, DAC quorum dips, force-include triggers)
Operational SLOs and triggers:
Score <70 → alert and investigation
Score <50 → automatic sequencer rotation to hot standby
Score <30 → rollup fallback activation; public post-mortem
SLO-Gated Rollout
Minimum gates:
• Device DAU ≥ 250k with median ≥3 accepted proofs/device/day
• Chain-Health ≥85 for ≥95% of time
• Forced-inclusion ≤24h consistently
• Public API p95 latency <800ms per region
Capacity policy:
• Autoscale to ≥2× rolling p95 load; burstable to ≥3× during incidents
• Sequencer failover RTO ≤60s, RPO ≤1 block
• 72-hour offline buffering on devices and at edges
Smart Contracts and Interoperability
Authra’s contracts live on our Arbitrum Orbit L3 (“Authra L3”) with execution on Nitro and settlement to Arbitrum One (L2), which in turn settles to Ethereum L1. Each finalized batch records its Merkle root and metadata on the parent chain, preserving an auditable trail while keeping per-proof costs low. $ATRX is the native utility token on Authra L3 (and the gas token in AnyTrust mode). For external liquidity and integrations, we use the canonical Arbitrum bridge to enable $ATRX representations on Arbitrum One and, when needed, on Ethereum L1. This keeps the core app-chain sovereign and low-fee, while allowing the token and data feeds to interoperate with the broader Ethereum ecosystem. The contracts also implement the burn-and-mint economics (detailed later in Tokenomics) whereby enterprise usage fees result in token burns and new token issuance for rewards follows a schedule .
Authra’s chain is EVM-compatible to facilitate interoperability with the broader blockchain ecosystem. This means developers can write integrations in Solidity and the $ATRX token can readily interact with other Ethereum-based platforms if needed. Although Authra primarily runs on its own chain, we plan to enable cross-chain bridges for $ATRX – for example, deploying a mirrored $ATRX token on Ethereum mainnet or other popular L2s (Arbitrum, Base) so that users can move liquidity into DeFi pools or exchanges . This way, while Authra’s core operations remain on a performant custom network, the token and data feeds aren’t siloed – they can plug into existing crypto infrastructure, maximizing liquidity and integration options.
Scalability Decisions: The system is architected to scale horizontally at each layer:
The ingestion service (which collects data from devices and prepares batches) can run on multiple servers across regions behind load balancers . It’s stateless aside from writing to a database, so we can spin up more instances as data volume grows.
Databases (for raw data and analytics) can be sharded by geography or time. We use time-series optimized stores (e.g. TimescaleDB for recent data, ClickHouse for large-scale analytics) that are proven to handle billions of records by scaling out in clusters .
With a sequencer-based pipeline, Authra L3 can sustain high transaction throughput on Orbit. Because proofs are aggregated into Merkle batches, the rate of batch posts to the parent chain remains moderate and is measured in seconds (not per-transaction). We target a batch cadence in the single-digit to low-tens per minute under load; economic finality then follows the parent chain’s challenge window and L1 settlement. This is comfortably within the capacity of Arbitrum One (L2) and Ethereum L1 anchoring.
This design choice – heavy lifting off-chain, lightweight commitments on-chain – allows Authra to target an ingest rate of over 100,000 data points per second (across the network) while keeping on-chain throughput manageable .
End-to-end latency is optimized by pipeline design: from a device submitting a proof to that proof being included in a finalized batch and available via API, we target under 2 seconds in the common case, from submission to being queryable via API. This ensures near-real-time responsiveness for applications like outage detection or dynamic traffic routing.
Finality & Latency on Orbit
Inclusion latency (L3): transactions normally included within seconds by the sequencer.
Economic confirmation (L3/L2): batch postings to Arbitrum One target single-digit to low-tens per minute under load.
Settlement finality: withdrawals and dispute resolution follow the parent chain’s challenge window; ultimate safety derives from Ethereum L1 via Arbitrum’s BoLD fraud proofs.
Censorship resistance: users retain the Delayed Inbox force-inclusion path if the sequencer withholds transactions
We continuously monitor performance metrics (signature verification times, DB write speeds, consensus round durations) and can adjust parameters or scale components to avoid bottlenecks . For instance, if sustained throughput requires more parallelism, we will deploy additional Orbit app-chains (domain-sharded L3s) bridged via Arbitrum One, rather than layering extra consensus tiers at L3.
Alternate Architecture Considerations: In designing the chain, we evaluated various approaches. We considered using the Cosmos SDK with Tendermint and independent bridging to Ethereum, or frameworks like Optimism’s OP Stack for a rollup solution . We opted for an Arbitrum Orbit–based appchain because Orbit provides production-proven Nitro execution, low fees, Timeboost (optional priority sequencing), BoLD fraud proofs, and a mature AnyTrust/rollup DA pathway that fits Authra’s enterprise/government data-integrity goals. This route is similar to other successful DePIN projects that use app-specific chains anchored to Ethereum for trust. It gives us flexibility in development and sovereignty (we control our validator set and can implement custom modules), but still an easy path to interoperate with Ethereum’s ecosystem. If in the future an even more efficient or compliant framework emerges, Authra’s modular design would allow migration or multi-chain support, but for now the single-chain-with-anchor strategy offers the best mix of performance, unity, and security.
Finally, Authra’s architecture supports private Orbit deployments. For customers that require isolation (e.g., defense, regulated critical infrastructure), we can stand up a private Orbit chain using AnyTrust (cheap DA, custom gas in $ATRX) or Rollup mode (L1 DA) under their governance, while keeping interoperability with the public Authra L3 via standard Arbitrum bridges. Terrascient and TruePing are built to interface with such instances. This lets a government or enterprise run Authra internally with the same verification model while retaining a clean path to public auditability and liquidity on Arbitrum One/Ethereum.
End-to-End Security and Privacy Model
Authra is engineered with a security-first and privacy-first mindset, knowing that users and enterprises will only embrace the system if they trust both the data and the data handling processes. We outline here how Authra secures data from device to blockchain, protects user privacy, and aligns with regulatory compliance requirements.
Device Security & Data Authenticity: Every data point in Authra (be it a latency measurement or a location claim) starts at a user’s device. We leverage smartphone Trusted Execution Environments (TEE) – e.g., Android StrongBox or ARM TrustZone, and the iOS Secure Enclave – to cryptographically sign measurements at the source . The TruePing app generates key pairs that reside in the TEE, meaning the private key never leaves the secure enclave. When the app collects a PoP or QoE datapoint, it computes a digital signature using that key. The signature (and an embedded device attestation, when available) proves that:
The data came from a genuine device’s sensors (not a modified app or emulator), and
The data was not altered in transit (any tampering would invalidate the signature).
Validators reject any data that isn’t properly signed by a known device key. This mechanism turns each phone into a secure witness, providing what is essentially a witness testimony that can be independently verified . If malware or a malicious user attempted to fake or modify the data, the cryptographic checks would fail and the network would discard the submission.
Transport Security: Data in transit from the TruePing app to Authra’s network is end-to-end encrypted. We use TLS with certificate pinning on the app, so that the app only communicates with authentic Authra servers/nodes and cannot be easily intercepted or redirected by man-in-the-middle attacks . This protects against eavesdropping or injection of false data during transmission. Additionally, proofs are typically small payloads (a few hundred bytes), which makes it feasible for the app to even use mixnets or TOR for higher anonymity in the future, though currently TLS is sufficient.
Network security (Orbit optimistic rollup): Authra runs as an Arbitrum Orbit L3 on Nitro. Safety derives from BoLD fraud proofs anchored to Ethereum: one honest challenger is sufficient to prevent finalization of invalid state; economic finality follows the parent chain’s challenge window. The active sequencer posts a governance-set bond in $ATRX and is subject to slashing/rotation on misbehavior; challengers post per-challenge bonds, earn rewards for valid disputes, and forfeit on spam. Censorship resistance is preserved via Delayed Inbox force-inclusion. AnyTrust DA provides low fees with a transparent, rotating DAC; if quorum degrades or stricter assurance is required, we fallback to Rollup (on-chain DA). At the application layer, TEE-signed, multi-signal proofs, anomaly detection, and Sybil controls make attacks costly and detectable; core contracts and ops undergo regular third-party audits and red-team tests.
Zero-Trust and Auditability: Authra’s philosophy aligns with zero-trust principles. Every layer verifies the layer below – devices don’t trust users, network doesn’t trust devices without attestation, enterprises don’t have to trust Authra’s team because they can audit the cryptographic proofs on-chain. Every proof recorded on Authra is auditable end-to-end. A verifier can (i) check the on-device signature and attestation artifact, (ii) confirm inclusion by the Authra L3 sequencer or submit via the Delayed Inbox for censorship-resistant force-inclusion after the governance-configured window, and (iii) rely on Arbitrum BoLD permissionless fraud proofs for correctness and on parent-chain (Arbitrum One → Ethereum) finality for settlement. This provides objective evidence without requiring trust in any single party, while preserving liveness under adverse conditions.
Finality timeline (at a glance):
T₀: included by sequencer (seconds) → T₀+W: force-inclusion window closes → Dispute window on parent chain per BoLD → Settlement finality on Ethereum L1.
Privacy by Design: Because Authra deals with potentially sensitive data (location and network usage), we have built privacy safeguards into the core design:
Data Minimization: We collect only the data needed for the service. For PoP, the app gathers an environmental “fingerprint” (nearby cell IDs, WiFi SSIDs hashed, etc.) and a coarse GPS location, rather than continuous precise tracking . The WiFi network names, for example, can be hashed so that the actual names (which might include personal info like “John’s Wifi”) are not stored – only a non-reversible hash used for matching . GPS coordinates can be quantized to a grid or truncated to 2-3 decimal places, which is enough to prove general location (within tens of meters) but not pinpoint an exact address . QoE metrics are just numbers (latency, signal strength) without content. We deliberately avoid collecting any payload data (no actual user communications or personal files are touched – only network performance metadata).
Pseudonymization: Users are not asked for name, email, phone number or any identity when using TruePing. Each user is represented by a pseudonymous crypto address or an app-specific user ID that has no real-world identifier attached . On-chain, data proofs are associated only with these pseudonymous device IDs or wallet addresses. This means even if someone scans the public ledger, they see contributions from address 0xABC.. but have no idea who that is in real life. The mapping of a device’s data to a personal identity is never stored by Authra.
Aggregated Exposure: When Authra sells data or provides it to enterprises via Terrascient, it is in aggregate form. A dashboard might show “20 devices experienced <5 Mbps speed in Area A this morning” or a heatmap of coverage – it does not expose individual user trajectories or histories. Raw detailed logs are kept internal and even there they are identified by device IDs, not names.
On-Chain Privacy: The blockchain only stores hashed commitments and high-level proofs, not raw location coordinates or IP addresses. In fact, our use of an L2 for anchoring means even the public record on Ethereum is just a hash of a batch – completely opaque without access to the off-chain data and keys.
Post-Quantum Cryptography (PQC) Migration (public overview): We publish a modular cryptography roadmap: today Ed25519; next hybrid signatures (Ed25519 + PQ scheme such as Dilithium/Falcon as libraries stabilize); eventual PQC-only for device and batch signatures. Verifier tools and compatibility harnesses are open so third parties can audit transitions. Hybrid (Ed25519+PQC) signatures will be accepted for device attestations and batch commitments as libraries mature, without requiring on-chain key changes. (Key sizes, curves, and rollout cadence are shared under NDA.)
Privacy-preserving primitives: We employ k-anonymity grids, hash-based environment fingerprints, and selective-reveal (ZK-ready) attestations so verifiers can check that a presence proof is valid without learning precise coordinates or identities. Parameters and ZK circuit details are provided under NDA.
Precision Presence (RTK optional): For high-assurance use-cases (SLAs/defense/fraud), validators may request RTK-enhanced presence claims. Authra integrates third-party GNSS correction feeds to achieve sub-meter accuracy on supported devices/regions. Claims carry RTK artifacts server-verified against corrections; default proofs remain multi-signal GNSS+RF without RTK. Precision mode is opt-in, policy-gated, and logged in provenance.
Soft Presence via Background BLE (optional corroboration): For retail/events footfall and venue corroboration (not primary PoP), TruePing can opportunistically log privacy-preserving BLE encounters in the vicinity to support visit/attendance inferences. iOS background limits and privacy rules are respected; BLE signals only corroborate network proofs and never substitute for primary Presence.
User Control and Consent: TruePing only operates with user consent. On install, the app explicitly asks for permission to access location and run diagnostics, explaining that the data will be anonymized and used for network quality mapping (in line with app store guidelines and privacy laws) . Users can pause data collection at any time via a simple toggle in the app (of course, pausing means they stop earning rewards during that time). They can also choose to only collect data under certain conditions – for example, only while on WiFi or only during daytime – using in-app settings . For transparency, users have access to their own contributed data: the app provides a personal dashboard of their recent reports (latency, coverage, etc.) and even allows exporting those records if desired . If a user decides to stop permanently, they can delete their account; this erases any keys on the device and stops any further collection. (Any data already aggregated on-chain remains as part of the global dataset, but it’s unlinkable to them and is essentially anonymous facts at that point.) This approach is in line with GDPR principles like right to withdraw consent and right of access, although strictly speaking the data we store isn’t personally identifiable. By proactively giving users control, we build trust and also compliance “by default.”
Enterprise-Grade Compliance: From day one, Authra has aimed to meet or exceed the compliance standards that enterprises and governments require:
We are implementing SOC 2 Type I/II controls for all cloud services (e.g., the TruePing backend, Terrascient cloud platform) . This involves formalizing policies for security, availability, and confidentiality – things like rigorous access control, continuous monitoring, encrypted databases, routine backups, and incident response plans . An independent auditor will verify these controls, giving enterprise customers confidence in the operational security of the Authra service.
For handling personal data, we align with GDPR and similar regulations (CCPA, etc.). Our privacy-by-design approach (minimization, pseudonymity) means we avoid creating personal data in most cases. Where we do handle user information (like email if a user provides one for notifications, or support), those are handled per GDPR guidelines (consent, purpose limitation, etc.). We also plan for data processing agreements and clear terms of service to clarify data usage.
For serving U.S. government clients, we target FedRAMP Moderate equivalence . This means if a government agency wants to use Authra (particularly Terrascient), we can deploy it in a GovCloud or their on-premises environment with all required NIST 800-53 controls implemented . Terrascient in a government context would run with single-tenant isolation, FIPS-140 validated encryption modules, multi-factor authentication, and audit logging enabled . In short, we strive to tick the boxes for regulatory compliance, making it as easy as possible for conservative organizations to adopt Authra without legal or policy roadblocks.
Recognizing that our technology (especially presence verification) could be considered dual-use, we are attentive to export controls like ITAR. We structure the project entities and share software in a way that avoids violating export regulations, and if needed will obtain licenses for certain collaborations . Internationally, we engage with local regulators to ensure our token reward system and data collection approach is acceptable (for example, clarifying that users explicitly opt in to share anonymized telemetry in exchange for tokens, analogously to existing crowd-sourcing apps, which is permitted in most jurisdictions).
Standards Interop (Passpoint/OpenRoaming): Authra issues Passpoint/OpenRoaming profiles so TruePing credentials can function as standards-compliant Wi-Fi roaming identities. This yields richer, privacy-preserving telemetry (association/roam success/failure, handoff quality) and reduces enterprise integration friction. Interop is delivered via RADIUS/IdP integrations and published profiles; no chain changes required.
Security Testing and Continuous Hardening: We don’t assume our design is perfect – we rigorously test it. The Authra team runs penetration tests and “red team” drills regularly. This includes:
Attempting to spoof location or QoE proofs with various hacks (GPS spoofing apps, sensor manipulation). Our multi-signal approach (cross-verifying cell, WiFi, GPS data) and TEE signatures are tested to ensure they catch these .
Simulating Sybil attacks where thousands of fake virtual devices try to join the network. We verify that our Sybil detection AI (discussed later) flags these and that consensus rules (e.g. requiring diverse sources for data confirmation) mitigate their impact .
Ensuring resilience to mobile OS changes – for example, if Android or iOS change background process policies or require new permissions, we adapt (we maintain close adherence to Google/Apple guidelines and have contingency to partner with popular apps to embed our SDK if standalone background services become too restricted).
Testing failure modes: if the blockchain is temporarily unreachable (say the L2 is down for maintenance), our infrastructure queues data and later commits it without loss. If connectivity is patchy, the device stores proofs until a connection is available.
All background collection respects OS energy and privacy policies; telemetry rates adapt dynamically and pause under low-power or restricted-activity conditions. By relentlessly testing and fortifying each component, Authra aims to be battle-tested and trustworthy even for the most security-conscious users. Our mantra has been “not your typical crypto project – we do things by the book”, meaning we combine blockchain innovation with the rigorous engineering and compliance standards of enterprise software.
Tokenomics ($ATRX) – Incentives and Economic Design
A cornerstone of Authra’s design is the $ATRX token, which powers the ecosystem’s incentives, security, and governance. Authra’s tokenomics are crafted to reward useful contributions, align stakeholder interests, and ensure long-term sustainability, avoiding the pitfalls seen in earlier networks (like speculative bubbles or “death spirals” where token value collapse undermines participation). This section details the utility of $ATRX, its distribution, emission and burn mechanics, and how it ties into real-world value.
Token Utility: $ATRX is a multi-purpose utility token at the heart of Authra:
Incentive Rewards: This is the currency in which contributors (end-user devices) and validators are paid for their service to the network. Instead of a centralized entity paying out in fiat for data (which would be unsustainable at scale), the protocol releases $ATRX from the pre-minted Community Rewards pool to reward users who provide valuable proofs and validators who secure the network. This gives participants a direct stake in the network’s success.
Staking for Security: Chain-layer bonds & challenges. The sequencer is bonded (governance-set bond in $ATRX) and may be rotated; challengers post bonds to submit BoLD fraud proofs. Successful challenges earn the challenger’s reward; spam or failed challenges are penalized. We do not introduce a separate BFT consensus layer; finality comes from the Orbit/Arbitrum dispute game and L1 settlement and safety derives from optimistic fraud proofs (BoLD), parent-chain settlement, and censorship-resistance via the delayed inbox.
Additionally, enterprise customers or heavy data consumers might stake tokens to get privileged access or higher API rate limits – for instance, staking a certain amount might grant a developer discounted query fees or priority access to data feeds . This “stake-for-access” model is akin to software licensing in Web3 form, ensuring large users have skin in the game.
Governance: Over time, $ATRX will serve as a governance token allowing the community to vote on protocol upgrades, parameter changes (like reward rates), and fund allocations for ecosystem development . Initially, the core team stewards the network, but the plan is to progressively decentralize decision-making. In a mature state, one token could equal one vote in a DAO-like governance system (with appropriate safeguards such as quorum requirements to prevent governance attacks) . This gives long-term token holders a say in Authra’s future and aligns everyone on growing the network’s value.
Burn Mechanism / Utility Sink: Importantly, $ATRX isn’t minted as rewards – it’s consumed when the network’s services are used. When enterprises pay for data access (either paying directly in $ATRX or via fiat that is converted to $ATRX), a portion of that payment leads to tokens being burned (destroyed) . For example, if a telecom company pays $10,000 for Authra data reports, the smart contract might use part of that to buy $ATRX on the market and burn them, or require the enterprise to spend tokens which then get burned . This burn creates a direct link between usage of the network and token value – as network adoption increases, token supply decreases relative to demand, which can support the token’s price. This mechanism is similar to Helium’s “data credits” model or Ethereum’s fee burn (EIP-1559), and it counterbalances token emissions.
Minting & Circulating Schedule: All supply is minted at TGE and held by the treasury and programmatic contracts; circulating supply follows a published unlock schedule. Initial free float targets ~40% at/near TGE (ecosystem, liquidity, market-making, and a portion of rewards), with the remainder vesting per program (contributors, partners, team, reserves). Unlocks are milestone-gated where applicable and transparently reported.
Single-Token Policy & Fee-to-Burn Band: Authra uses a single utility token ($ATRX) across rewards, fees and governance; there is no dual-token complexity. Net enterprise fees fund a weekly buy-and-burn within a 30–50% band (governance-tunable). Executions use TWAP to reduce market impact; a public burn dashboard reports totals and sources.
Proof-Weighted Rewards (utility over device count)
Rewards prioritize useful, independent, and diverse data rather than raw device totals.
The protocol weights proofs by:
Rarity: under-represented geographies/times earn more; oversampled grids earn less.
Independence: decorrelates collocated/coordinated devices; reduces collusion payoff.
Coherence: higher weight when multi-signal PoP agrees with nearby observations and historical baselines.
Weights are auditable (published formulas, hashed parameter sets on-chain) and tuned by governance. (Exact coefficients and anti-gaming thresholds are withheld pre-NDA.)
Integrity (risk-weighted rewards): Proofs from attested, non-rooted devices with strong history earn a 1.0× baseline. Devices flagged by Play Integrity/App Attest signals, root/jailbreak/emulator detection, or abnormal process/network behavior earn 0.1–0.5× until reputation improves. Repeated integrity failures lead to quarantine and manual re-attestation.
Risk-Weighted Rewards & Endpoint Gating
Rewards multipliers reflect device integrity and behavior:
| Device Status | Multiplier | Conditions |
|-----------------------|-----------:|----------------------------------------------------|
| Baseline | 1.0× | Attested, non-rooted, consistent history |
| Integrity Flags | 0.5× | Root/jailbreak/emulator indicators |
| Suspicious Patterns | 0.1× | Ensemble anomaly detection under review |
| Quarantine | 0.0× | Multiple violations; manual review |
Additional controls: dynamic staking indexed to expected earnings; proportional slashing to economic harm; 30-day reward locks enabling clawback; regional×time-bucket rate limits to deter automation bursts.
Ensemble-based anomaly routing: Validators consume an ensemble of on-device filters (TinyML), cloud models, and cross-validator coherence checks to down-weight or route suspicious proofs for further review. No single model is trusted; multiple independent detectors must agree before slashing or quarantine.
Supply & Allocations: $ATRX has a hard cap of 1,000,000,000 tokens, all pre-minted at TGE. Distribution at genesis remains: Community Rewards 40%, Team & Advisors 25%, Investors 15%, Treasury 15%, Market Liquidity & Partnerships 5%. Only a modest ~20–25% of supply circulates at launch; the rest releases gradually per published schedules. (Vesting tables and monthly unlock charts are appended.)
Emissions = time-release, not net new supply. Rewards are time-released from the pre-minted Community Rewards pool on a decaying schedule (epoch-based). No tokens are created beyond the 1B cap. Governance may tune epoch rates within preset bounds to maintain contributor and challenger APY health while aligning with utility/burn ratios.
Burn & value flow: A fixed share of enterprise usage fees (e.g., 30%) is programmatically used to buy & burn $ATRX, linking demand to supply reduction. Over time, fee-burn is targeted to offset releases, with a glidepath to net-neutral or deflationary dynamics as adoption scales. (Exact percentage is governed; quarterly reports disclose burn vs release.)
Example rewards split: Epoch emissions split (illustrative): 40% mobile contributors / 40% challengers-security / 20% treasury & programs, subject to governance. An example epoch at N active devices and M challengers is included in the appendix for sustainability math. These are allocated to various groups to bootstrap the ecosystem in a balanced way :
Community Rewards – 40% (400 million): Set aside to pay out to user contributors and validators over time . This is the “mining” or participation rewards pool, emitted over ~10 years.
Team & Advisors – 25% (250 million): Reserved for core developers and early contributors, with long vesting to ensure long-term commitment.
4-year vest, 1-year cliff, monthly thereafter; founder lockups disclosed.
20% base vesting; 5% performance pool tied to public KPIs: DAU milestones, enterprise ARR targets, Chain-Health ≥85 adherence, and fee-to-burn band compliance.
No acceleration except for cause; any change requires 2/3 governance approval with public reporting.
Investors – 15% (150 million): Allocated to seed and future investors providing capital to build Authra . Typically, a portion unlocks at launch (say 20-25%) and the rest vests over ~18 months, balancing investor return with preventing immediate large sell-offs.
Treasury/Foundation – 15% (150 million): Held by the Authra Foundation (or DAO) for ongoing funding of development, marketing, and ecosystem grants. This might release slowly (e.g. a small percent each quarter over several years) and is subject to community oversight.
Market Liquidity & Partnerships – 5% (50 million): Allocated to provide initial exchange liquidity and to incentivize early partners or market makers . A portion of this might be immediately available at launch (to ensure a healthy market for $ATRX), with the rest vesting over a year or so.
(Table: High-Level $ATRX Allocation at Launch)
This distribution ensures that a majority of tokens (40%) directly incentivize the network’s user base over time, aligning with our principle that network value is created by the community . The team’s share is significant enough to reward builders but is long-term locked to demonstrate commitment . Investor allocation is modest – enough to fund development but not so high as to dominate the supply. And a healthy chunk is reserved for future needs and to ensure liquidity at the outset. At launch, only roughly 20-25% of the tokens will be in circulation (from the small unlocked investor portion, initial market liquidity, etc.), with the rest releasing gradually . This slow-release model prevents an oversupply shock and helps maintain token value in the early phase when utility demand is still growing.
Emission Schedule & Inflation Control: To reward ongoing contributions beyond the genesis allocations, Authra mints new $ATRX tokens on a controlled schedule. We use a decaying emissions model, where the network mints a certain percentage of the remaining “rewards pool” each year, so that the absolute number minted decreases over time . For example, in year 1 the protocol might issue 20% of the Community Rewards pool, and each subsequent year the percentage of the remaining pool is slightly lower (creating a half-life style decay). In practice, this might result in an inflation rate of around ~8% of circulating supply in the first year, dropping to <3% annually by year 5 . This is conceptually similar to Bitcoin’s halving (but smoother) or to some POS chains that reduce emissions over time. The rationale is to front-load rewards when the network needs to attract participants and grow, but then taper off inflation as the network matures and organic usage (and fee burns) sustain the economy .
Valuation-Aligned Guardrails & KPIs
Emission Guardrails: Emissions follow a decaying schedule with governance-tunable epochs. If net fees (after burns) ≥ X% of market cap on a trailing 90-day basis, emissions ratchet down by up to Y% at the next epoch to prioritize scarcity. Conversely, if sequencer/challenger APY and contributor APY < floor bands, emissions can ratchet up within predefined caps. (Exact X/Y values to be set by governance pre-TGE.)
Utility/Burn Targets: Publish quarterly “usage-to-burn” ratios and aim for fee-burn ≥ 25–50% of new issuance by end of Year 2, with a glidepath to net-neutral or deflationary by Year 3.
Concentration Limits: Foundation-tracked caps on any single entity’s circulating ownership (e.g., 5%) enforced via vesting and program policy; unlocks linked to circulating supply and liquidity conditions.
Adoption KPIs: Public dashboards track (i) active contributor devices, (ii) covered km²/road-km, (iii) enterprise ARR, (iv) average data-point cost vs legacy tools. Governance ties grant programs to KPI attainment.
These guardrails are non-price-targeting controls that protect network health while aligning supply with real utility—providing an investor-grade path to sustainable valuation.
Emissions are typically split among:
Mobile Contributors: e.g. 30-40% of each block’s newly minted tokens go to the pool that pays users running the app.
Validators: e.g. another 30-40% of emissions reward those running nodes and staking to secure the network.
Treasury or Other Pools: e.g. 20-30% might be allocated to the treasury for long-term funding, or to specific initiatives (if guided by governance) .
This split ensures we incentivize both the “supply side” (data providers and validators) and maintain a reserve for development. As the network’s fee revenue grows, those fees effectively offset the need for high inflation – eventually the goal is to reach a net neutral or deflationary state where the number of tokens burned from usage equals or exceeds the number of new tokens minted for rewards . Our models project that if adoption grows as expected, inflation can drop from ~8% in year 1 to ~2% by year 5, and enterprise fee burns by year 5 could cancel out around 1–2% worth of tokens annually, making net inflation near zero or even negative beyond that . This keeps the token supply in check long-term, rewarding early adopters as their token holdings represent a growing share of a limited supply as the network gains real usage.
Burn Mechanics and Value Flow: On the flip side of minting, we have token burning to link usage to value:
Whenever enterprises or developers use Authra’s data, they pay in either $ATRX or fiat. If fiat, the Authra treasury will take a portion and purchase $ATRX from the open market to burn, simulating as if the client paid in tokens . For example, we might set that 30% of all revenue is used for buy-and-burn each quarter. This creates buy pressure and reduces supply as adoption increases.
If demand on the network surges (many customers pulling data, etc.), token burn accelerates and can outpace new issuance, making the token deflationary (more tokens removed than created). This dynamic ties token value to real-world demand for Authra’s services instead of pure speculation.
Additionally, if needed, governance could enact additional stability measures, such as a reserve fund that can buy back tokens in extreme cases to support the price (though this would be used sparingly, as ultimately we want the economics to be market-driven) . The protocol avoids promises of unsustainable high yields; instead, it can adjust reward rates gradually via governance to keep validator and user incentives at a healthy level without causing runaway inflation .
Economic Alignment: Authra’s economic design creates dual value streams:
Enterprise Revenue (fiat) – from selling data access and services, which provides real cash flow to fund operations and buy/burn tokens.
Crypto Token Value – from network effects and demand for $ATRX (for staking, governance, speculation on network growth).
This model means Authra is not solely dependent on token price hype; it has fundamental support from paying customers, which is a major differentiator from many earlier crypto projects . In downturns, if crypto markets slump, we can lean more on enterprise revenue to keep the system running (in the extreme, we could even temporarily reward users in a stablecoin or fiat if necessary to maintain engagement, although that’s a contingency) . Conversely, if the network is booming in usage, token demand will naturally rise (more need for staking, more burns happening, etc.), potentially driving up value and attracting more participants – a positive feedback loop .
In summary, $ATRX is the lifeblood of Authra’s decentralized economy. Its mint-and-burn flow ensures those who contribute value are compensated, and those who derive value feed some back into the system . By carefully balancing distribution, controlling inflation, and tying token mechanics to real usage, Authra’s tokenomics seek to create a virtuous cycle of growth rather than a speculative boom-bust . As network demand grows, token utility and scarcity grow in tandem – making $ATRX a true representation of the network’s success .
AI Components – The TrustMesh Intelligence Layer
Authra is not only a blockchain and network data platform, it’s also an AI-powered system. We often refer to Authra’s AI layer as “TrustMesh AI” – it is like the nervous system and immune system of the network, continuously learning from data and safeguarding integrity . Unlike many projects that might bolt on some machine learning as an afterthought, Authra has AI woven in from end to end. The AI components serve two broad purposes: 1) enhancing data quality and security (through anomaly detection, spoof resistance, etc.), and 2) extracting higher-level insights (like predictions and natural language querying) that add value to the raw data . Here are the key AI-driven features:
On-Device TinyML: Each smartphone running the TruePing app carries a miniature AI agent that operates locally (on-device). We leverage TinyML models (lightweight neural networks and classifiers) that can run in real-time on a phone’s CPU or DSP with minimal battery draw . One crucial TinyML model is for spoof detection: it analyzes sensor data consistency to detect if someone is trying to fake a location or network metric . For example, if the phone’s GPS says it’s in London but the WiFi networks it sees have identifiers common to Paris, the model flags a potential anomaly. Or if a device claims a super fast network speed that is statistically out of range for its region and carrier, that could indicate manipulation. By filtering out obviously implausible data at the source, we save bandwidth and keep garbage data from ever hitting the network . Another on-device AI function is adaptive sampling using reinforcement learning: a tiny RL agent learns the optimal frequency to run tests based on context . If the user is stationary in a place with stable connectivity, the app can dial down test frequency to save battery. If the user starts moving or network quality fluctuates, the agent increases test frequency to capture those changes. Over time, this AI tunes data collection to be efficient yet responsive. These models can even personalize to each device (learning its typical sensor noise patterns, etc., to reduce false alarms) . Importantly, running ML on-device also protects privacy – raw sensor readings need not be continuously uploaded; only the relevant derived insights (like “suspicious data, likely spoofed” or “network stable, skip test now”) are sent.
Plausibility & Cross-Validation (Presence + QoE): We treat every data point as a hypothesis that must survive independent checks before it can influence rewards or analytics.
Local neighborhood corroboration: Each Presence/QoE report is compared with temporally- and spatially-adjacent reports. We compute a coherence score from agreement on radio context (cell IDs, Wi-Fi beacons), measured performance (latency/jitter/loss), and historical baselines for that area/time.
Plausibility priors: We down-weight reports that violate physical or operational constraints (e.g., implausible speed/location transitions; radio contexts inconsistent with claimed GPS; performance outliers that cannot be explained by diurnal/seasonal patterns).
Independence tests: We reduce influence from clusters that appear overly coordinated (same device models/OS builds, identical timing, identical network paths) to limit common-mode or Sybil bias.
Only reports with sufficient corroboration and plausibility progress to full reward weight and to enterprise analytics.
Privacy-Preserving Behavioral Fingerprinting: To resist at-scale spoofing, we learn each device’s temporal signature of participation (cadence, motion/state transitions, radio context variety) using on-device RL/TinyML and backend summaries.
What it is (and is not): The fingerprint is a privacy-preserving statistical profile of how a device contributes—not an identity, biometric, or any personal attribute. It is computed from quantized locations, hashed SSIDs, and coarse timing windows.
Why it matters: Large Sybil farms struggle to replicate thousands of distinct, stable temporal signatures across weeks. Sudden drifts (e.g., many devices becoming perfectly periodic, or “teleporting” patterns) are flagged for quarantine/down-weighting.
User protections: Natural behavior changes (travel, device replacement) are handled with adaptive thresholds and warm-start models; users remain pseudonymous; raw precise locations never leave the device.
Cloud Anomaly Detection & Sybil Defense: After data leaves the device and reaches Authra’s backend, a second layer of AI scrutiny kicks in. We use anomaly detection algorithms on the aggregated data streams to identify outliers or suspicious patterns that a single device might not catch . One technique employed early on is the Isolation Forest, an unsupervised model well-suited for high-dimensional anomaly detection . It looks at combinations of factors (location, time, latency, device type, etc.) and flags data that doesn’t fit the learned “norm”. For example, if suddenly a cluster of devices all report the exact same latency and coordinates (suggesting one script impersonating many phones), the anomaly detector would raise an alert. For combatting Sybil attacks – where an adversary might spin up many fake nodes or devices to flood the network – we incorporate graph analytics and graph neural networks . We model the relationships between devices, their co-locations, and their data similarity as a graph. Genuine users tend to have organic patterns (devices moving independently, data varying per environment) whereas a Sybil attack often has telltale signs (e.g., one controller coordinating many devices that might all appear together or alternate in suspicious ways). Using graph-based anomaly detection, we achieved >95% detection of Sybil nodes in simulation tests . Detected Sybils can be quarantined by the network (their data given low weight or ignored, their accounts possibly banned if severe). The anomaly detection models continuously retrain as more data flows in, meaning the system learns what normal network behavior looks like and can adapt to new attack tactics over time . Outputs from these AI systems can feed into device reputation scores (discussed next) or trigger requirements like needing additional validator confirmations for data that looks unusual. The AI here acts as an ever-vigilant sentry, more adaptive than any fixed rule set could be.
Predictive Analytics (Outage & Performance Forecasting): One of Authra’s unique value propositions to enterprise customers is not just telling them what is happening now, but what will happen soon. We apply time-series forecasting models to the vast historical dataset of network metrics to predict future performance issues . Early on, we used models like XGBoost (a powerful gradient-boosting machine) on features such as recent latency trends, time of day, weather, and more . As data volume grew, we moved to advanced deep learning approaches – for example, LSTM networks and Temporal Fusion Transformers – which can capture seasonality and complex dependencies in the data. These models can forecast metrics like latency spikes or outage probabilities 24-72 hours ahead with impressive accuracy (in tests we achieved an AUC > 0.8 for predicting cell tower outages, meaning a high true-positive rate) . A concrete example: the system might analyze all signals and output, “There is an 80% chance that mobile data latency in downtown area will exceed 200ms during 6-9pm tomorrow.” Terrascient can then surface this as an alert to operators or even an API output that a CDN can use to reroute traffic proactively . Over time, these predictions continuously validate themselves (the AI compares predicted vs actual and retrains), so the models get better. This predictive capability is a major differentiator against competitors that mostly offer reactive monitoring. Authra aims to give stakeholders a heads-up so they can fix problems before users feel pain.
Reputation & Trust Graph: All data sources (devices, validators) in Authra maintain a reputation score computed by AI. We essentially build a web-of-trust graph where edges might represent corroboration or conflict between devices’ data . Factors influencing a device’s reputation include: consistency (does it usually agree with nearby devices’ observations?), longevity (has it been contributing data reliably for a long time?), and authenticity (has it passed attestation and anomaly checks consistently?) . We use graph neural networks (GNNs) to propagate trust across this network of interactions – a concept akin to Google’s PageRank but for sensor trustworthiness, or like how one might infer trust in a social network. This allows detection of more subtle attacks: e.g., if one bad actor controls many devices that mostly validate each other but rarely mix with others, the GNN can spot this “cluster” and mark them with lower trust . Devices with high reputation might serve as “anchor nodes” whose data is weighted more heavily or used as ground truth reference. Low-reputation devices might still be allowed to contribute (to avoid false negatives of banning honest but new users), but their data might require cross-checks or yields lower rewards until they build trust. The aim is a self-healing network: if someone injects bad data, the network isolates its influence quickly and prevents it from affecting any conclusions .
Natural Language Query & Insights: To make the power of Authra’s data accessible to non-technical users, we incorporate Natural Language Understanding (NLU) in the Terrascient interface. Users (like a policymaker or an executive) can ask questions in plain English (or other languages) and get answers derived from the data . For example: “Which regions had the worst connectivity during the last storm?” or “Show me all areas in State X with average download speed below 5 Mbps last week.” The system uses NLU to interpret the question, then translates it to the appropriate database query or analytical computation, and returns a human-friendly answer or visualization . This can be powered by fine-tuned language models or template-based NLP. Additionally, we use AI to generate automated insights and summaries. A GPT-like model might periodically produce a summary report such as: “Weekly Network Health: National uptime was 99.1%, with notable outages in Region Y due to storms; QoE improved 5% MoM overall. ” These summaries save busy users from combing through dashboards and help highlight key events. While these front-end AI features don’t affect the core verification, they significantly improve user experience by turning raw data into digestible knowledge.
User Engagement AI: Another AI aspect is keeping our contributor community active. We analyze user behavior (in aggregate and privacy-safe ways) to see how to improve retention. For instance, an AI model might learn the best time to send a notification: “You earned 5 $ATRX this week, check it out!” to encourage opening the app . If a usually active user goes quiet, the system could flag it and the app could proactively check if they need help (maybe a bug has occurred) . We can personalize tips: if the model sees a user mostly on cellular data, it might suggest “connect to Wi-Fi to earn more” because Wi-Fi allows additional tests. These small AI-driven touches help maximize the contributor base – crucial for the network’s data coverage.
Collectively, these AI components form the TrustMesh AI layer – acting as both guardian and guide for the network . The AI is like an ever-improving filter and lens: it filters out bad data (immune system) and provides clarity and foresight from good data (brain). This combination of decentralized crypto incentives with AI analytics yields a network that gets smarter and more reliable with every data point collected . It creates a formidable moat: a competitor would not only have to replicate Authra’s network, but also its learned intelligence – which is always a moving target. Finally, by delivering near real-time insights and predictions at a fraction of the cost of traditional methods (thanks to community data and automation), Authra’s AI features help democratize high-quality infrastructure intelligence.
TrustMesh × Three-Layer Fabric: TrustMesh AI treats Layer 1 as the attestation boundary, Layer 2 as the evidence stream (PoP+QoE scoring, anomaly detection), and Layer 3 as transport policy (delay-tolerant prioritization and replay defense), keeping data quality high even in adversarial conditions.
AI Control Loop (from signal → action)
Ingest & screen: On-device checks remove impossible readings; backend anomaly detectors (statistical + ML) and graph/Sybil models score each report and cluster.
Cross-validate: Neighborhood corroboration and plausibility priors update each report’s confidence score.
Reputation update: Per-device (and per-operator) reputation is updated via a trust graph (akin to PageRank) with decay and recovery paths.
Act:
Low confidence: reduce reward weight, throttle rate limits, require extra confirmations.
Medium confidence: auto-open a challenge window; route to human/committee review if needed.
Very high confidence fraud: submit on-chain challenge and apply slashing per policy.
Learn: Periodically retrain models; compare forecasts with outcomes; publish detection KPIs.
TruePing App – User Engagement and Developer Platform
The TruePing component of Authra serves as the lifeblood at the network edge – it’s how everyday people interact with Authra and how raw data is collected, and it’s also how developers and third-party apps can interface with Authra’s dataset. TruePing has two primary personas: contributors (end users) who install the mobile app, and developers or partners who integrate via the SDK/API. We designed TruePing to be user-friendly and rewarding to attract a large user base, while also providing robust APIs to encourage ecosystem growth.
Mobile App Core Functions: Once a user installs TruePing (Android initially, iOS support to follow), the app works mostly in the background to perform its two key tasks:
Network QoE Probing: The app periodically tests the device’s internet connection – for example, pinging a nearby server to measure latency and jitter, performing a brief download to estimate bandwidth, or checking signal strength and network type . These tests are small and adaptive (the frequency can change as needed) to minimize any user impact. Essentially, the phone becomes a tiny monitoring node that reports how well the network is working from that user’s vantage point.
Proof-of-Presence Reporting: The app securely logs the device’s location context at certain intervals using a combination of signals (GPS, cell towers, WiFi, Bluetooth) . Importantly, it doesn’t constantly track or report exact locations; instead, it collects “moments” of presence and associated environment data, which serve as proofs that “this device was roughly here at time T.” Each proof uses the multi-signal fingerprint + TEE signing approach described earlier to ensure authenticity .
All data collected is signed in the device’s secure enclave before leaving the phone . This means even if the phone had malware, it couldn’t alter the measurements without breaking the signature. The data is then sent to Authra’s network (via the nearest ingestion server or directly to validators) automatically. TruePing is optimized to be lightweight: internal testing showed it adds less than 3% to daily battery usage, thanks to efficient scheduling and making use of OS features like batching tasks when the radio is awake . For example, if your phone is low on battery or you’re on a metered (cellular) connection, the app will slow down data collection and queue results until a better time (such as when you plug in or connect to WiFi) . This adaptive behavior ensures TruePing remains a “good citizen” on the device, avoiding annoyance or excessive resource use.
User Rewards and Gamification: To motivate users to participate en masse, TruePing provides meaningful rewards and an engaging experience:
$ATRX Rewards: Users earn $ATRX tokens for every valid proof their device contributes . The app includes a built-in crypto wallet (abstracted for non-technical users) where these tokens accumulate. The reward algorithm can weigh contributions – data from under-covered areas or during important events might earn more, whereas redundant data in well-covered city centers might earn less . This encourages users to provide coverage where it’s needed.
In-App Wallet & Staking: Users can hold their earned tokens in the app, and we plan to allow in-app staking or lockup for those who want to earn additional yield or simply support the network (possibly with bonuses for doing so). The UI keeps it simple: e.g., “You have 100 ATRX – stake them for a bonus” with one tap.
Gamification Elements: We introduce badges, leaderboards, and streaks to tap into intrinsic motivations . For instance, a user might earn a badge like “Explorer – provided proofs in 10 different cities” or “Reliability Champ – 30 days of continuous uptime reports.” Leaderboards can be global or local: users can see how they rank in their city or country in terms of contributions. A little competition and recognition can greatly boost engagement. We might feature top contributors in a non-personally-identifiable way (e.g., alias or device ID) on a website or in the app.
Community Challenges: Periodically, we can run campaigns, such as “Help map rural connectivity this month – participants get 2x rewards in uncharted areas” or “Campus Challenge – top 10 contributors on each college campus win extra tokens.” This drives viral growth and targeted data collection . There’s also a referral system: invite a friend and if they join and contribute, you both get a bonus.
The tone we set is that of a global community of “network citizen scientists.” We want users to feel not only that they are earning tokens, but also that they are part of a mission to improve internet transparency and reliability. This sense of purpose, combined with tangible rewards, helps with retention.
Privacy and User Control: TruePing’s adoption heavily depends on user trust, so we design it to be privacy-preserving and respectful:
We do not collect personal info at sign-up – no name, no phone number, nothing. Users can remain pseudonymous . If we introduce social features or leaderboards, users might choose a nickname, but that’s optional and not tied to their real identity.
Location data is treated carefully: as described, it’s coarse and often hashed. The app might internally know your precise GPS for a moment to generate a proof, but it typically sends only a derived proof (like “grid cell X, time T, signed”). We never broadcast a user’s live location to others, and certainly not without consent.
The app provides controls like the ability to pause data collection (say you enter a sensitive location or just want a break) . Users can also configure if they want the app to run only under certain conditions (e.g. only when charging, or only when connected to WiFi to avoid mobile data usage) .
Transparency is key: we make the app’s code open source (at least the data collection parts and the on-device ML) so that the community can verify we’re not doing anything beyond what we claim (no hidden tracking, etc.). For more tech-savvy users, this is a confidence booster.
As mentioned earlier, all contributions are anonymized and aggregated. The app itself only shows the user their own data. If an enterprise is looking at Terrascient, they see overall trends, not “John’s phone at 123 Main St had 200ms latency at 5pm.”
By giving users clear information and control, we comply with privacy laws and, more importantly, build the trust needed for them to keep the app installed long-term. We know that even with token rewards, if people fear their personal data is misused, they will opt out. Thus, privacy-by-design is not just a regulatory checkbox for us, but a foundational aspect of user experience.
User Utility: While many users will join for the earnings, we also want TruePing to be useful to them directly. The app doubles as a personal network quality dashboard:
Users can view their own connectivity stats over time: e.g., a history of their average download speeds, latency, signal strength in different places. This empowers them to see if their provider is delivering as promised or if another carrier might serve them better .
We provide coverage maps or charts accessible to the user: for example, “Your city’s average 4G speed is X Mbps, you are above/below average,” or “Your neighborhood had 2 outages last week.” This contextualizes their experience.
In-app alerts can notify users of issues: “There is a known outage in your area” or “Your WiFi is experiencing high packet loss.” This can help them troubleshoot – for instance, if they know it’s a provider issue, they won’t reset their router 10 times; or if it’s their WiFi, they can switch to cellular.
We could even implement features like an automatic network switch: if the app detects the WiFi you’re on is performing poorly and your cellular is better, it could suggest or automatically trigger a switch for apps that allow it (this might be an advanced opt-in feature, and of course only if it doesn’t conflict with user’s data plans) .
By giving users insight and potentially improving their connectivity (not just measuring it), TruePing becomes something they’d want to keep beyond just the monetary incentive. It’s akin to how fitness tracker apps give you data about yourself; here it’s about your digital connectivity health.
Developer SDK and API Strategy: The other side of TruePing is enabling developers and enterprises to tap into Authra’s capabilities. We provide a TruePing SDK that third-party app developers can embed, and a suite of APIs for querying data:
The SDK can be embedded in other popular apps (with user permission). For example, a weather app or a mobile game could include TruePing’s SDK to let their users earn tokens while playing, effectively outsourcing the data collection. In return, the app developer might get a cut of the rewards or other incentives (similar to how Tutela worked by paying app publishers) . This strategy can massively accelerate adoption, as we piggyback on apps that already have millions of users. It’s a win-win: the app adds a passive income feature for its users, and Authra gains more coverage. The SDK is lightweight and runs the same tests as the full app (or maybe a subset if configured). We ensure it runs in a sandbox respecting user privacy, and the host app must disclose the data collection in its privacy policy (similar to how analytics SDKs are disclosed).
APIs: Authra exposes RESTful and WebSocket APIs for key functionalities . For example:
GET /api/v1/qoe?location=<area>&time_range=<t1,t2> – fetch aggregated QoE metrics (latency, throughput, etc.) for a location and time window.
GET /api/v1/presence?device_id=<id>&time=<t> – verify if a given device (or user identity, if linked) was present in a location at a given time (this could serve location verification queries).
WebSocket channel like /stream/qoe_changes – subscribe to real-time feed of QoE events (e.g., get notified if latency in any subscribed region exceeds X).
GET /api/v1/oracle/chain_health — normalized 0–100 health with component breakdown and timestamps (see Governance).
WS /stream/chain_events — live feed: sequencer rotations, DAC quorum dips, delayed-inbox force-includes, fraud-proof openings/resolutions, and anchor commits.
These APIs enable myriad use cases: a telecom NOC could integrate live Authra data into their dashboards, a smart contract oracle could call the presence API to unlock an action based on real-world location, a content provider could subscribe to QoE events to adapt streams dynamically, etc. .
Language SDKs: We also provide SDKs in common programming languages (Python, JavaScript, etc.) to make it easy for developers to use the API without dealing with low-level details . For example, a Python developer can pip install authra-sdk and do authra.get_qoe(area) to get data – under the hood it handles auth, queries, etc.
Integration & Plugins: To drive enterprise adoption, we consider building integrations for popular analytics platforms – e.g., a plugin for Splunk, or a connector for Tableau/PowerBI . This way, an enterprise can pull Authra data into their existing tools seamlessly. We want to meet enterprises where they are, not force them to reinvent their workflow.
Data Access Models: For heavy users, we might offer bulk data access or even a dedicated database read replica where they can run their own SQL queries on Authra’s data . Some regulated clients might want an on-prem version – Terrascient can be deployed in such a mode where it houses a local copy of the necessary data.
The strategy is to make Authra’s data as easy to consume as possible – turning it effectively into an infrastructure API for the physical world’s network performance. The more developers build on Authra, the stickier and more valuable the ecosystem becomes (and the more burn of $ATRX via usage).
In summary, TruePing is where human users meet the network and where external systems plug in. By carefully balancing user engagement (through rewards and a good app experience) with developer friendliness (robust SDK/APIs), Authra creates a vibrant edge ecosystem. Millions of users can passively contribute and benefit, while countless applications and services can leverage the truth layer Authra provides – whether to enhance their own offerings or to enable entirely new functionalities (like smart contracts reacting to real-world SLAs, etc.). TruePing’s success is critical as it feeds the network; thus, we designed it to be as accessible and appealing as possible to drive adoption on a global scale.
Terrascient Platform – Enterprise Intelligence and Analytics
Terrascient is Authra’s enterprise-facing platform, the analytical brain that turns the torrent of raw data and on-chain proofs into actionable intelligence. If TruePing is the engine collecting data, Terrascient is the cockpit where the data is interpreted and utilized by decision-makers. It’s designed for telecom operators, regulators, cloud service providers, smart city planners, defense and government agencies, and large enterprises that need deep visibility into network performance and presence data. Every insight carries a confidence score and provenance trail (what corroborated it, which models agreed), enabling auditors to trace why an alert or KPI is trustworthy.
Platform Overview: Terrascient is a web-based application (with options for on-premises deployment) that offers:
Interactive Dashboards: Maps and charts showing current network QoE across regions, historical trends, and device presence hotspots. Users can drill down on a country, city, or specific cell tower area to see metrics like average throughput, latency distribution, uptime/downtime, etc., all derived from Authra’s verified data.
Alerts & Monitoring: The platform can be configured to monitor certain SLAs or conditions – for example, “Alert me if any region’s latency >100ms for >5 minutes” or “Notify if any base station goes offline.” When triggered, Terrascient highlights these events (and can also push notifications via email/SMS or integrate with systems like PagerDuty for NOC alerts).
Analytics & Reports: Users can generate reports (e.g. monthly SLA compliance report for Carrier X) that are cryptographically signed by Authra as tamper-evident proof . These reports can serve as independent audit documents. For instance, a regulator can download a report on broadband coverage in rural areas, with each data point attested by Authra, which could be used as evidence in policy evaluations or even legal proceedings.
ContractWatch: A dashboard tile that subscribes to /stream/chain_events and Chain-Health to highlight sequencer rotations, DAC quorum dips, force-include events, and fraud-proof state—alongside QoE anomalies—to give ops teams one common picture.
Operational Tiles: Terrascient includes a “ContractWatch” tile for live chain events (fraud proofs, DAC signer churn, force-include triggers) and a Chain-Health summary. Operators can subscribe to SLO breaches and auto-open incident tickets via webhooks.
Query Tools: For power users, Terrascient provides query interfaces, including the aforementioned natural language query feature and a more advanced query builder for complex filters. They can ask questions or run analyses without needing to export data to another tool.
Integration Hooks: Terrascient isn’t a silo, it can push data to other systems. For example, it can forward alerts to a telecom’s existing Network Operations Center dashboard, or provide an API endpoint for an operator’s OSS/BSS systems to fetch Authra metrics.
Public APIs (selected)
/presence — query presence proofs by time/region/provider
/qoe — query QoE aggregates and time-series
/heatmap — geospatial tiles/aggregates
/alerts — streaming/outage and degradation alerts
/risk_score — endpoint reputation and device-integrity hints
/oracle/chain_health — current 0–100 Chain-Health score + components
/stream/chain_events — sequencer/DAC/force-include live events
Commercial Rails: Enterprise billing is fiat-first; on-chain attestations and burns run under the hood. This reduces procurement friction while preserving cryptographic auditability and token sinks.
Use Case Example – Telecom SLA Monitoring: (We will detail this as a pivotal example in the next section.)
Government and Public Sector: Terrascient offers enormous value to public sector users:
Broadband Coverage Audits: National telecom regulators can use Terrascient to independently verify if telecom operators meet their licensed coverage and quality obligations . For example, if a carrier claims 98% 4G coverage in a state, the regulator can see Authra’s data to confirm or refute that claim – highlighting underserved pockets that the carrier’s self-reported data might have missed . With governments worldwide investing in broadband (e.g., the U.S. BEAD program in the billions of dollars), such audits ensure accountability for public funds . Terrascient can do this continuously, not just via one-time drive tests.
Defense and Emergency Response: Defense agencies can deploy Terrascient in an air-gapped environment to monitor communications readiness in real time . During a crisis (natural disaster or military operation), having a live map of network availability is crucial. If an adversary is jamming signals or a disaster knocks out towers, Authra-enabled devices (including possibly devices carried by first responders or soldiers) would show exactly where communications are failing . Commanders get instant situational awareness of the digital battlefield. Terrascient can also integrate with security policy – for instance, verifying that a user is physically on a secure base before they access a system (an example of presence-based access control) .
Critical Infrastructure Oversight: Utilities and infrastructure regulators can use Terrascient to monitor networks that critical systems rely on. E.g., an energy grid operator can see if their IoT sensors’ connectivity issues correlate with network outages or are due to sensor faults, aiding in root-cause analysis .
Terrascient is built with high security options for these clients: it can run on government clouds, and with an architecture suitable for deployment on FedRAMP-authorized clouds and aligned to NIST 800-53 class controls (Moderate baseline), as required by agency policy. This means even conservative agencies can host Terrascient in environments where they are comfortable, without fear of data mixing or leaks.
Enterprise & Commercial Uses:
Telecom Operators: Interestingly, the telcos themselves can be customers. While Authra might seem like a watchdog, enlightened operators can use it to augment their internal tools with an outside-in perspective. Terrascient gives a customer’s-eye view of their network performance in real time , which can help them identify issues that their internal monitoring might miss (like localized problems). Some may integrate Authra data directly into their NOC via the TruePing API, while others might use the full Terrascient UI for deeper analysis . For telcos facing regulatory scrutiny, showing they use an independent platform to verify their service can even be a selling point (“we have nothing to hide, we self-audit with Authra”).
CDNs and Cloud Services: Content Delivery Networks and cloud providers can use Terrascient’s predictive analytics to optimize traffic routing and resource allocation . For example, if Terrascient forecasts a latency spike in a region (perhaps due to an upcoming event or a degrading ISP), a CDN can proactively reroute traffic through a different path or spin up extra edge servers . Cloud gaming companies, where milliseconds matter, could similarly plan server assignments based on QoE predictions . The platform basically becomes an early warning system for internet performance issues, which is incredibly valuable to any online service provider.
Smart Cities & Infrastructure Companies: City IT departments can monitor public Wi-Fi, IoT networks, and cellular coverage in their jurisdiction via Terrascient . For instance, if a city offers free Wi-Fi in parks, they can see if it’s actually working well and identify if/when it goes down . During large events or emergencies, they can see communication blackouts in real time and coordinate responses (like deploying mobile cell towers to an outage zone) . Over the long term, cities can use Authra data to make the case to carriers for network upgrades in under-served neighborhoods, armed with concrete data of poor service .
Logistics and Transportation: Companies with supply chains or fleets can use Terrascient for verified tracking and connectivity assurance . For instance, a delivery company can require a cryptographic proof-of-presence at the delivery location before releasing payment (preventing drivers from faking deliveries) . They can also review the connectivity along routes – if their smart trackers went offline, was it because the device failed or because that route has dead cellular zones? Authra data can pinpoint exactly where connections drop, so they can choose alternate routes or satellite backup for those stretches . Public transit authorities might do similar checks to ensure passengers or onboard systems have consistent connectivity along transit lines (since some now view good mobile signal as part of service quality).
Advanced Analytics and AI in Terrascient: Terrascient doesn’t just display data; it leverages Authra’s AI to provide higher-level intelligence:
Reputation Scores & Risk Indicators: It can show a “trust score” for data points or devices on a dashboard – e.g., flagging if some data is considered low-confidence by the AI (perhaps it came from a new device that looks like a bot). This helps users gauge data reliability at a glance .
Scenario-specific Modules: We envision modular add-ons in Terrascient. For example, a “Telecom SLA Compliance” module could automatically compute each operator’s compliance vs their targets and generate report cards. A “Disaster Response” mode might focus the UI on outage maps and provide suggestions (like nearest functional cell site to deploy portable towers). A “Fraud Detection” module might integrate with a bank’s system to verify location claims for high-value transactions using Authra presence proofs . Over time, as we learn common use cases, we can package tailored experiences for each.
Collaboration and Data Export: Terrascient allows teams to share dashboards or annotate events (e.g., a regulator can mark “investigation opened for outage incident here”). It also provides data export in standard formats for offline analysis or record-keeping.
In essence, Terrascient is designed to be the consumption layer of Authra for professionals . It’s what turns raw “truth data” into decisions and actions. By providing this polished interface with the necessary security/compliance wrappers, we lower the barrier for enterprises and agencies to benefit from Authra. They don’t need to be blockchain or AI experts – they get a web portal or software that speaks their language (SLAs, uptime, coverage, KPIs) but under the hood, every number is backed by Authra’s decentralized verification. This combination of rigorous data integrity with ease of use enables a world where infrastructure decisions and policies can be based on verified empirical evidence rather than estimates or self-interested reports . That is the core promise of Authra realized through Terrascient.
Illustrative Use Case: Telecom SLA Verification
To ground the discussion, let’s walk through one of Authra’s pivotal use cases in detail: Telecom Service Level Agreement (SLA) Verification. This scenario exemplifies how Authra’s components come together to solve a real-world problem, and it showcases the mechanics, benefits, and unique value proposition of the platform.
Scenario Background: Suppose a country’s telecommunications regulator has set minimum service requirements for mobile operators. For example, each carrier must provide at least 95% 4G coverage in rural regions and maintain at least 99% network uptime (availability) in each quarter, per their license agreements. Historically, the regulator has had to rely on carriers’ self-reported data or occasional drive tests to check compliance – methods that are infrequent, costly, and can be biased.
Commercial Model & ROI (Illustrative)
Contract Shape: Regulator/Ministry or Auditor-of-Record subscription, scoped per metro/region, with optional national rollout.
Pricing Unit: Monthly subscription per covered region (includes device-seeding budget), plus optional per-API or per-report charges.
Typical Range (illustrative): USD $5k–$50k per metro/month depending on population, coverage goals, and data retention SLAs.
ROI: Replaces periodic drive-tests (often $250k+ per national campaign) with continuous measurement; improves enforcement and grant audits; reduces disputes with carriers via cryptographic evidence.
Payment Rails: Fiat or ATRX. Fiat inflows trigger protocol buy-and-burn; direct ATRX payments route a programmatic burn share.
Challenge: The regulator suspects that in some remote areas, coverage is overstated by the carriers. Consumers have complained about dead zones, and there’s anecdotal evidence of dropped calls and slow data. The regulator needs an independent, continuous verification of actual user experience to ensure carriers are truthful and to identify any gaps in service. Additionally, if a carrier fails to meet SLA, the regulator needs hard evidence to enforce penalties or mandate improvements.
Authra Deployment:
The regulator partners with Authra to deploy the TruePing app to volunteers across the country. It also encourages citizens to install the app (perhaps even subsidizing tokens for initial participants in remote areas). Over a few months, tens of thousands of users in cities and rural villages are running TruePing.
At the same time, the regulator sets up a private instance of the Terrascient platform in their secure cloud environment (or uses the cloud-hosted version with appropriate data sharing agreements). This instance is configured with the specific regions and SLA metrics of interest.
Carriers are informed that an independent audit mechanism is now in place (though since Authra is permissionless, it would run regardless of carrier buy-in). Some carriers, recognizing the value, even join the network themselves, perhaps running validators or promoting the app, since it can help them identify issues proactively.
Reliability Backstop: If sequencer unavailability is detected, data-point transactions are relayed to the delayed inbox for force-inclusion at expiry, ensuring evidence continuity for audits even across transient outages.
Data Collection Mechanics:
Each TruePing user’s phone periodically performs connectivity tests: pinging a test server or downloading a small file to measure throughput. The app also logs presence proofs tying the performance data to a location (e.g., “Device X observed 120ms latency in region Y at time T, and was connected to Carrier A’s network”). All of this is signed by the device’s TEE to ensure authenticity.
Suppose in a certain rural district, Carrier A has claimed 95% coverage. TruePing data shows that out of 100 distinct geogrid cells in that district, users could get a 4G signal in only 85 of them consistently, and in 15 cells they either had only 2G/3G or no service at all. These findings are gathered from many user devices over the quarter.
Meanwhile, a significant outage occurred one weekend when a fiber cut caused Carrier B’s towers in another region to go down for half a day. TruePing devices in that area all recorded “no connectivity” or extremely high latencies during that period, creating a cluster of PoP records showing presence but no network quality.
Verification and Consensus:
As the data streams in, Authra’s validators verify each proof (checking that, for example, a reported “no connectivity” proof is legitimate by seeing that neighboring devices also reported issues, etc., and that all are correctly signed).
The blockchain accumulates these proofs into an immutable log. So for any given location and time, there is a tamper-proof record of what users experienced, endorsed by the consensus of validators .
The regulator doesn’t need to parse blockchain data directly; Terrascient translates it into human-readable insights.
Using Terrascient for SLA Monitoring:
On Terrascient’s dashboard, the regulator has set up an SLA Compliance view. This view shows, for each carrier and region:
Coverage Percentage – e.g., “Carrier A: 85% of rural Region X had 4G coverage at least 90% of the time” (versus the claimed 95%).
Uptime – e.g., “Carrier B: 99.5% uptime overall this quarter, but on June 5th experienced a 12-hour outage affecting 20 cell sites.” Terrascient flags this date in red on the calendar view for Carrier B .
Performance Metrics – average and 90th percentile latency, throughput ranges, etc., compared against any SLA targets.
By clicking on a specific region on the map, the regulator can see granular data: for example, a heatmap overlay might show exactly which locales in Region X had no service (those 15 grid cells) . Because Authra collects multi-signal presence proofs, the regulator can trust these are real dead zones, not artifacts (each proof is backed by actual device readings and consensus).
For the outage in Carrier B’s network, Terrascient provides a timeline of the event. It shows that starting at 2:00 AM, users in Town Y connected to Carrier B dropped to 0. By 2:15 AM, X number of devices reported no connectivity. The outage persisted until around 2:00 PM when devices started coming back online. Terrascient can generate a report quantifying the impact: e.g., “Outage of 12h 14m, roughly 5,000 device-hours of no service observed.” This report is cryptographically signed and time-stamped by Authra, meaning Carrier B cannot dispute it – it’s as if thousands of notarized witnesses attested to the outage .
If Carrier B claims “it wasn’t our fault, it was a third-party fiber issue,” Terrascient’s data can even potentially help correlate cause (if Authra had data on other carriers or networks in the same area not failing, it strengthens that the issue was specific to B).
Enforcement and Benefits:
The regulator now has hard evidence to enforce SLAs. In the case of Carrier A’s coverage shortfall, they can present a report to Carrier A: “Our independent audit shows you only achieved 85% coverage in Region X, not 95%. Here are the anonymized data points backing this, all verifiable on-chain . Please submit a remediation plan or face penalties per your license agreement.” Carrier A, confronted with this level of detail (and knowing it’s pointless to argue against cryptographically verified data), must respond constructively – perhaps accelerating tower deployments or optimizing their network in that region.
For Carrier B’s outage, the regulator might waive penalties if it was a genuine accident but will use the evidence to ensure better redundancy measures. More importantly, the public and enterprise customers gain trust that the regulator has real visibility. Carrier B might even leverage Authra data themselves to communicate transparently: e.g., “Yes, we had an outage on June 5, here’s the third-party evidence of it and we’re ensuring it won’t happen again.”
Over time, the availability of Authra’s neutral data can reduce disputes. Often carriers contest traditional reports (like from Opensignal or Tutela) by saying the methodology was flawed. With Authra, any stakeholder can independently verify each piece of data (drill down to the proof if needed, see it was signed by a device’s TEE on a given date, etc.) . This transparency discourages futile arguments and encourages carriers to just fix issues.
The regulator also benefits from continuous monitoring vs. snapshots. Instead of an annual drive test campaign that provides a limited view, Authra gives them a 24/7, 365 feed. Trends can be spotted – e.g., maybe every day at peak hours, a certain region’s latency spikes (indicating capacity issues). They can prompt the carrier to address that proactively rather than waiting for complaints.
Another benefit is public accountability. The regulator could choose to release some of Authra’s findings to the public via a web portal (since it’s all open data in essence). Citizens can see unbiased information about network performance in their area, fostering a more competitive environment where carriers are pushed to genuinely improve service, not just marketing claims.
Unique Value Proposition: In this SLA verification scenario, Authra’s USP shines:
It provides proof, not just data. Traditional crowdsourced data (like Opensignal) gives statistics but requires trust in the aggregator. Authra provides proof-of-presence and proof-of-QoE for each data point, which can serve as audit-grade evidence . This is crucial for regulatory or legal proceedings.
It’s continuous and real user-based, covering places and times that scheduled tests might miss. The use of everyday smartphones means coverage of the “long tail” of locations – from highways to small villages – as long as someone with a phone is there occasionally.
It aligns incentives such that data collection is cost-effective and scalable (users are paid in tokens, far cheaper and more scalable than hiring drive-test teams or installing probes).
It’s neutral and transparent – since the data is on a public blockchain (or accessible ledger), even the carriers being monitored could verify the data for themselves. This neutral “source of truth” reduces friction.
The regulator can achieve its oversight mandate with far greater precision and less cost, and carriers can improve by focusing on real problem areas, ultimately leading to better service for users – fulfilling Authra’s mission of improving internet reliability.
Layer activation in this use case: L1 Device Integrity (attested phones), L2 PoP+QoE (audit-grade measurements), L3 Resilient Transport (bundled inclusion across outages). This mapping generalizes to regulators, enterprises, and public safety.
In summary, telecom SLA verification via Authra demonstrates how an age-old problem (trusting service metrics) is solved by combining decentralized tech and community data. It’s a repeatable scenario: regulators in many countries have similar needs (some already spend millions on measurements). Authra can thus become a standard tool in the regulator’s toolkit globally, and similarly for enterprise customers who need to verify their providers. This use case also hints at future possibilities: such verified QoE data could be fed into smart contracts for automated SLA enforcement (e.g., an enterprise’s contract with a carrier could automatically trigger a penalty or credit if Authra data shows SLAs weren’t met). Authra essentially acts as the oracle of network truth enabling such arrangements. The result is higher accountability and ultimately a better experience for end users who enjoy improved network service quality thanks to the feedback loop Authra creates.
Broader Applications and Use Cases
Layer Activation Matrix (examples)
While the telecom SLA example is a core early application, Authra’s infrastructure has broad applicability across industries. Here we summarize other notable use cases that illustrate the platform’s versatility:
Defense-Grade Presence Verification: In military or high-security contexts, Authra’s PoP proofs can enforce that certain actions only occur when authorized personnel are physically present at designated secure locations. For instance, consider launch of a critical system command: a policy might require that the officer issuing it is on-site at Command Center A. Using Authra, the officer’s device can automatically provide a presence proof (leveraging multi-signal environment and TEE attestation) that is verified by the network . Only if the proof is valid (meaning the device and thus presumably the officer is indeed at the location) will the system accept the command. This significantly hardens security versus relying on passwords or GPS alone, which can be spoofed. Similarly, in defense operations, tracking friendly assets in a privacy-preserving way can be done via Authra – you get real-time presence of units without each soldier needing to manually report, and all data is securely attested.
Another angle: zero-trust access control on bases or secure facilities. Before granting network access or decryption keys to a device, require an Authra proof that the device is in an approved location and has not been tampered with (since the TEE signature also confirms device integrity). This would mitigate risks of stolen devices or remote hackers.
Content Delivery & Streaming Optimization: Modern content providers (Netflix, YouTube, gaming platforms) strive to minimize buffering and lag. Authra can feed them real user experience data that goes beyond what their server logs show. For example, a video streaming service could use Authra’s QoE data to dynamically adjust bitrates or content distribution networks. If Authra predicts a user’s network is about to degrade (say, moving from WiFi to cellular on a commute), the service might pre-buffer more content or switch to a lower resolution preemptively, thus avoiding a stall. This kind of real-time, user-centric network info is a game changer for Quality of Experience management on the application side.
DePIN Synergies (IoT and Location): Authra’s data could complement other decentralized physical networks. For instance, Hivemapper (decentralized maps) could ingest Authra’s connectivity info to annotate map data with “good/bad signal zones.” A network like Helium could use Authra’s phone data as a cheap way to verify coverage of its LoRaWAN hotspots (e.g., Authra phones can detect Helium hotspot beacons and report that presence, providing proof that a hotspot is actually delivering coverage). These are partnership potentials rather than competitions, as Authra’s focus (QoE + presence) is unique and can augment other datasets .
Financial Services (Proof-of-Location for Transactions): Banks and payment networks lose billions to fraud, some of which involve location spoofing (e.g., a credit card used simultaneously in two cities, or a hacker from overseas trying to withdraw from a local account). Authra could provide an API for transaction verification: when a suspicious transaction triggers, the bank’s app could request an Authra presence proof from the user’s phone. If the proof shows the phone is indeed at the ATM location, it’s likely legitimate; if not, it could auto-decline the transaction. This could be like a more robust version of the geolocation checks banks do, but with cryptographic assurance (and privacy, since the bank gets a yes/no proof, not raw location). Additionally, loyalty or insurance programs that require proving you were at a certain place/time (like “prove you attended the gym for insurance rebate” or “prove your phone was at home during a break-in to verify an alibi”) can all leverage Authra’s presence verification.
Supply Chain Integrity: Beyond just tracking connectivity for logistics, Authra could underpin smart supply chain contracts. Imagine a shipment that must remain within certain geofenced routes or needs to hit checkpoints by certain times. Authra devices on trucks can provide undeniable proof of where the truck was and when. If a contract says “if delivery not at location by 5 PM, auto-trigger penalty,” an Authra oracle can supply that truth to a blockchain-based contract. It could also verify storage conditions indirectly – e.g., if a refrigerated container goes out of network range unexpectedly (maybe diverted), that might signal a problem.
Crowdsourced Sensor Network Expansion: Authra’s model (phones as sensors) can be extended. For instance, phones could measure environmental data like noise levels or air quality if equipped with sensors, and Authra could verify the where/when of those readings similarly. This moves beyond connectivity into any sensor data that benefits from proof-of-location. A city could, for example, incentivize citizens to collect air pollution data with their phones (some have PM2.5 sensors or could attach one) and rely on Authra to prove those readings are from where they claim. This synergy of PoP + sensors could create a broader “proof of physical world data” platform.
Gaming & CDN: Matchmaking/region gating by proven latency/region and scheduled rollouts using QoE forecasts to avoid peak congestion.
Insurance & Retail/OOH: Parametric QoE credits/micro-payouts for downtime beyond thresholds, and POAP-style anti-spoof flows for venue attendance and geo-verified redemptions.
These use cases underline Authra’s flexibility: it’s essentially a general proof layer for real-world data, with the initial focus being network metrics and device presence. As we grow, the community and enterprise partners will no doubt discover new creative ways to leverage this trust fabric.
Gated Milestones (Gate-Driven, not Calendar-Driven)
Gate 1: Testnet → Pilot — DAU ≥25k; p95 API <1.2s; Chain-Health ≥80 for 90 days; 2 paying design partners.
Gate 2: 3 Cities → 10 Cities — DAU ≥250k; ≥3 accepted proofs/device/day median; high-confidence proofs ≥85%; SOC 2 Type I.
Gate 3: Token Launch (optional) — ≥5 enterprise logos billing; weekly buy/burn rehearsed; oracles live; ≥12 months runway.
Gate 4: Global Scale — ARR ≥$10M; Chain-Health ≥85 (95% of time); forced-inclusion ≤24h; red-team pass (<1% false-positive).
Core KPIs
Technical: Chain-Health time-above-threshold; proof quality %; inclusion latency; uptime/failover success.
Business: Billed usage; proofs/device/day median; Enterprise NPS >50; MoM usage growth.
Risk: Anomaly rate <5%; false positives <1%; 0 compliance violations; <2 security incidents/quarter.
Competitive Landscape and Differentiation
Authra sits at the intersection of blockchain-based decentralized networks and traditional network analytics. To understand our unique positioning, it’s helpful to compare against key competitors in two buckets: Decentralized Physical Infrastructure (DePIN) projects and Traditional centralized solutions.
1. Decentralized Networks (DePIN and Web3 Projects):
Helium (and Helium Mobile): Helium pioneered the DePIN model with a crowdsourced wireless network. It incentivized users to deploy LoRaWAN hotspots (and now 5G small cells) in exchange for HNT/MOBILE tokens . Helium’s strength is its large community and the physical coverage achieved (especially for IoT LoRa devices). However, Helium’s focus is providing coverage, not measuring user QoE. It doesn’t collect metrics like user-level latency or do per-phone presence proofs. Its “proof-of-coverage” mechanism verifies that hotspots are active via neighboring hotspots, which is a form of location witness but limited to the infrastructure, not end-user devices . Helium also requires new hardware deployment (people had to buy hotspots or run nodes), which is a higher barrier and cost. Authra’s differentiation: zero new hardware (just use phones), dual data (we cover both connectivity quality and presence), and enterprise integration from the get-go . Also, Helium’s tokenomics struggled because usage of the network was low relative to speculative mining – Authra addresses this by tying token value directly to data consumption (via burns when enterprises use data) . We view Helium more as a potential partner (e.g., Helium Mobile could use Authra data to optimize their networks ) than a direct competitor in the QoE intelligence space.
WiFi Map, Nodle, Hivemapper: These are three distinct projects but all pay users for data collection. WiFi Map rewards discovery of Wi-Fi hotspots, Nodle pays for connecting to IoT sensors via Bluetooth, and Hivemapper builds a maps (Street View) by paying dashcam users . Each targets a different dataset (hotspots, IoT signals, map imagery respectively). None of them measure network performance or provide a general presence proof – they each have narrow scopes. Also, they rely heavily on either altruism or simplistic token incentives without robust verification layers. For instance, Nodle just takes whatever data a phone gives about encountering BLE devices; there isn’t a concept of validating if those encounters were real or spoofed. Authra’s differentiation: We target a more valuable and broad dataset (the actual quality of internet service and reliable location proof) . We secure it with cryptography and consensus, whereas those projects often accept data on trust or lightweight checks. Moreover, our network intelligence is immediately usable by enterprises; by contrast, the value of, say, a global WiFi hotspot map is limited and doesn’t directly integrate into enterprise operations except maybe for specific use cases.
FOAM: FOAM attempted a decentralized Proof-of-Location via special radio beacons (Zone Anchors) and a token . It was an ambitious idea: essentially deploying ground hardware to triangulate devices and give them location proofs anchored on Ethereum. The issue was it required heavy infrastructure (four+ radios per zone) and few zones were deployed, so it never scaled. FOAM’s dynamic PoL could be very precise in a small area, but had zero coverage outside those zones . Also, FOAM focused purely on location, not network quality, and the use cases remained niche (like checking in at locations for blockchain dApps). Authra’s differentiation: We use existing infrastructure (cell towers, WiFi, etc.) and ubiquitous phones, so it’s immediately global and scalable . Our presence proof may be slightly less pinpoint (tens of meters accuracy vs FOAM’s potential sub-meter in a zone), but it’s practically useful and available anywhere a phone has signals . And we go beyond PoP to include QoE data, which FOAM never addressed. Essentially, Authra achieves FOAM’s goals in a more pragmatic way and pairs it with an additional high-demand dataset.
XYO Network: XYO also tackled location by creating a network of devices that vouch for each other’s proximity (the concept of “bound witnesses”) . Users could carry Bluetooth beacons and a mobile app to have devices mutually sign encounters, thereby creating a web of location truth. Like FOAM, it needed many participants in the same place to work well and struggled to find strong commercial use cases beyond maybe proof-of-presence for gamified applications. Authra’s differentiation: One phone with its environment signals can produce a presence proof; we don’t require multiple crypto devices meeting together (which was XYO’s model) . This lowers the coordination problem significantly. Also, XYO lacked focus on network quality or an enterprise angle, whereas Authra positions itself squarely as an enterprise-grade data network. In competitive terms, FOAM and XYO are adjacent projects solving a subset of what Authra does (location), but neither achieved enterprise traction. Authra fills that whitespace by blending location + QoE and doing so with a clear business model (selling data insights) .
Others (POAP, etc.): POAP (Proof of Attendance Protocol) is a popular crypto application where event organizers give out NFT badges to attendees. It’s somewhat related to presence but in a very simplified, centralized way (scan a QR code and claim an NFT) . It’s not secure (people share codes) and not aiming to be. We mention it only to show market interest in proof-of-presence concepts. Authra could supplant such use cases when a more fraud-proof solution is needed (e.g., high-value events or where the presence proof has financial weight) . But POAP itself isn’t a competitor, more an inspiration that presence verification has demand in Web3 communities.
2. Traditional Centralized Solutions:
Cisco ThousandEyes: TE is a gold standard in internet monitoring for enterprises. It places agents in data centers, clouds, and on enterprise endpoints to run synthetic tests (pings, traceroutes, etc.), and has rich analytics . It’s used by many large companies, but it’s expensive and requires deployment of those agents. TE excels at deep network diagnostics (layer 3 routing details, BGP analysis, etc.) that Authra in its initial form doesn’t do on phones . However, TE’s weakness is it misses the last-mile/mobile perspective – it typically doesn’t have agents on every ISP or on random mobile users, so it can’t tell you the consumer experience on a far-flung LTE cell unless you set up an agent there (which is impractical at scale) . Authra’s differentiation: sheer scale of vantage points (potentially millions of devices globally vs. thousands of TE agents) and much lower cost (no specialized hardware or high licensing fees; it’s powered by the crowd and open data). While TE is like a microscope for network engineers, Authra is like a wide-area radar showing broad conditions among real users. In fact, Authra can feed data into TE – i.e., a NOC using TE might take Authra’s data as another stream. Long-term, Authra could capture a segment of the Internet Performance Monitoring market by being more dynamic and cost-effective for many use cases . TE still has an edge in deep troubleshooting, so we see Authra as complementary for now, but potentially disruptive for monitoring “in the wild.”
Catchpoint: Another major player, similar to TE, with strong synthetic monitoring and some real-user monitoring (by embedding scripts in customer apps) . Catchpoint has good analytics and an “Internet Insights” offering. It’s also expensive and requires either deploying monitors or convincing app developers to include their SDK (which some do). Authra’s differentiation: open, token-incentivized user base vs. closed, paid deployments . Catchpoint can gather RUM (real user metrics) but those are limited to its clients’ user base and it’s all proprietary. Authra’s open network can potentially cover everywhere and share data across clients. Also, with tokenization, Authra might achieve far greater coverage than any one company’s RUM deployment. That said, Catchpoint and TE do offer fine control for private testing that a public network might not match (like testing an internal app behind a firewall). So enterprises might still use them for certain internal scenarios while using Authra for external, broad monitoring.
Ookla (Speedtest) and Opensignal/Tutela: These are incumbent crowdsourcing companies. Ookla Speedtest is widely used by consumers to test speeds; it aggregates billions of test results and sells reports and data to operators and governments. Opensignal (and Tutela, which it acquired) distribute an SDK in apps to quietly collect mobile network performance data and likewise produce reports for carriers and regulators . In fact, Opensignal and Tutela have been used by regulators to inform policy, which validates the need for crowdsourced data. However, these companies operate in a Web2 model:
They rely on users or app partnerships without direct user incentives (users run Speedtest mainly for their own curiosity or to troubleshoot, not to earn something).
They do not have cryptographic verification of the data. They mitigate cheating by statistical methods (if someone tries to fake Speedtests, it usually doesn’t affect the aggregate much, and they can identify outliers) , but they cannot provide a proof for each data point like Authra can. It’s a trust model – e.g., a regulator trusting Opensignal’s aggregation methods.
They don’t provide presence proofs at all; they focus purely on network metrics.
Opensignal’s scale is large (millions of devices via apps), but Authra could surpass it by unlocking a new incentive (tokens) to attract participants. If even a small percentage of the billions of global smartphone users join Authra, we dwarf current crowdsourcing numbers .
Authra’s differentiation: Combining the incentivization of Web3 (to achieve massive scale of data collection) with verifiability of blockchain (so data isn’t just statistically believable, but provably true) . We also offer a dynamic ecosystem where data is used in real-time (APIs, predictive alerts), whereas Opensignal/Tutela often provide historical reports and some live stats but not on a trustless public ledger.
We should note, these incumbents have established relationships and trust with carriers and regulators; Authra will need to demonstrate its data quality is as good or better . But if we achieve global scale, the sheer volume and openness of Authra’s data could make it the Wikipedia of network data compared to their Encyclopedia Britannica – more comprehensive and ultimately more utilized . Also, Authra’s presence proof adds a new dimension that Opensignal doesn’t have (e.g., verifying location of users during tests, which could be useful for things like proving rural coverage in subsidy programs more rigorously).
Tutela (Nokia): Tutela, now under Nokia/Opensignal, collected data via app partnerships. It paid app developers for access rather than users. Authra can actually learn from Tutela’s strategy: we could similarly pay or incentivize app devs (with tokens or revenue share) to include our SDK to bootstrap user base quickly . But our edge is we can also attract users directly through tokens, creating a community, not just behind-the-scenes data harvesting. Tutela’s existence proved carriers and regulators want this data; Authra’s addition of trustless verification addresses the lingering skepticism some carriers had (“How do we know the data’s not biased?”) . With Authra, any carrier could independently audit any data point if they wanted, which is a huge step beyond trusting a vendor’s report.
Summarizing Differentiation: Authra uniquely fuses the strengths of both worlds:
From the decentralized side: community-driven growth, token incentive flywheel, coverage breadth, and innovative proofs (presence).
From the traditional side: focus on enterprise-grade data quality, analytics, compliance, and practical use cases that deliver ROI.
No competitor provides the full package Authra does – global last-mile QoE monitoring + verified presence + AI analytics + compliance-ready design + an economic model linking token value to real utility. Helium covers hardware wireless networks, Opensignal covers crowdsourced QoE but without proofs, TE covers deep monitoring but not crowdsourced or decentralized. Authra finds a sweet spot combining elements of all three into one platform . This integrated approach, plus being first to execute it at scale, can make Authra the go-to “source of truth” for real-world network intelligence in the coming Web3 and enterprise landscape.
We also maintain an attitude of collaboration: where possible, we make our data interoperable or even partner with others (like feeding our data into existing tools as mentioned, or integrating with other DePIN projects to augment each other). This openness is itself a differentiator in an industry where many solutions are siloed or proprietary.
Conclusion
Authra represents a bold step towards a more transparent, reliable, and intelligent Internet. By turning the world’s smartphones into a decentralized network of witnesses, Authra builds an unprecedented trust layer for real-world data – specifically focusing on where devices are (presence) and how well networks perform (QoE). This whitepaper has outlined the comprehensive strategy behind Authra: from its robust blockchain architecture and innovative single-chain compliance approach, to its carefully balanced tokenomics, to the embedded AI that enhances security and insight, to the user and enterprise platforms that drive adoption and value.
The design is holistic and future-proof. We chose a single global chain model to maximize network effects and simplicity, but we modularized compliance and data handling to meet diverse regulatory needs on that unified network. We use proven technologies built on Arbitrum Orbit (Nitro): a high-throughput L3 ordered by a chain-opt-in sequencer, with permissionless fraud proofs (BoLD) anchoring security to Ethereum and data availability provided via AnyTrust (with rollup mode as a yet push the envelope with features like on-chain attested data and zero-knowledge selective disclosure. Scalability is addressed through off-chain batching and hierarchical design, allowing us to grow to millions of users and high data volumes without straining the blockchain layer . At every step, we considered not just “can we build it?” but “will people use it and trust it?” – leading to strong privacy safeguards, user-centric app design, enterprise-grade compliance, and open auditability.
Authra’s token $ATRX is engineered to create a virtuous cycle rather than speculation. Contributors and validators are rewarded fairly for building the dataset, and as enterprises consume that dataset, they in turn drive value back to the token through burn mechanics . This dual model of value (crypto-economic + real revenue) provides resilience: the network can thrive whether crypto markets boom or bust, because it’s anchored in real-world utility .
Our AI “TrustMesh” layer sets Authra apart from any simple data collection network. It fortifies the system’s integrity (catching spoofers, Sybils, anomalies in real time) and elevates the product offering (providing predictions, natural language insights, personalized engagement) . This combination of human participation and machine intelligence means Authra not only gathers truth but learns and adds value to the truth continuously.
Through TruePing and Terrascient, we’ve ensured that both ends of the ecosystem are catered to: the public is motivated and empowered to join the mission (earning tokens and knowledge about their connectivity), and enterprises/regulators can seamlessly tap into the information they need (with a polished interface and APIs, not having to worry about the blockchain complexity under the hood). By proactively addressing regulatory concerns (GDPR, SOC2, FedRAMP, etc.) and building in auditability, we aim to preempt objections that have hampered adoption of other crypto projects. An enterprise or government official evaluating Authra should come away with confidence that it’s not “crypto cowboy” tech, but a well-governed, secure, and compliant platform that just happens to use decentralized mechanisms under the hood for greater effect. We implement k-anonymized grids, pseudonymous IDs, ZK-ready commitments, and regional data residency—so most deployments avoid personal data creation while retaining verifiability.
In the competitive context, Authra’s approach is validated by adjacent successes but improves on them: we’ve learned from Helium’s community growth and Opensignal’s proven demand and combined those lessons with a more secure, scalable design. We recognize that to truly become the trust layer for digital infrastructure, we must deliver quality on par with or better than incumbents while leveraging the advantages of decentralization. That means engaging with existing standards and tools, not rejecting them – for example, providing connectors to feed Authra data into legacy systems, or allowing private deployments for those who need them, ensures we can slot into current workflows rather than requiring a revolution from day one .
Inspiration and Vision: At its heart, Authra is about trust through proof. We envisage a world where:
A student in a rural village can run an app that not only earns them pocket money but also contributes to shining a light on a lack of network service, which in turn compels a carrier to improve connectivity there.
An enterprise can confidently rely on network performance data that no single provider controls, for making million-dollar routing decisions or verifying SLA claims, reducing friction and disputes.
A government can base policy and investment on real, current data from the ground, ensuring public funds are effectively spent and progress is transparent to citizens.
Developers can innovate new services (from automated SLA smart contracts to location-based NFT drops that are cheat-proof) using Authra as a plug-in source of real-world truth.
Achieving this vision will not be without challenges. We must scale the technology and the community, navigate regulatory landscapes, and outcompete entrenched players. However, the groundwork laid out in this paper – a sound architecture, a robust economic model, clear use cases, and a commitment to security and compliance – demonstrates our readiness to tackle those challenges. We will continue to iterate in the open, governed by a growing community of stakeholders, and remain adaptable to new requirements or learnings as deployment expands.
In conclusion, Authra aims to become the decentralized trust anchor for the physical internet. By delivering reliable, verifiable data from the edge, Authra turns the intangible concept of “network trust” into a tangible, auditable reality. We believe this will catalyze significant improvements in how networks are built, operated, and experienced by everyone. The internet has become as critical as electricity in modern life – Authra is our answer to ensuring this critical infrastructure is transparent, accountable, and continuously improving for the benefit of all. And we do so through a Three-Layer Global Trust Fabric that is auditable, sovereign-ready, and commercially monetizable from day one. We invite technologists, enterprises, policymakers, and everyday users to join us in building an Internet that runs on proof, not just promises.