TRACC: On-chain Emission via DePIN

Description

TRACC Performance Evaluation Dataset This dataset contains performance evaluation data for TRACC (Transparent Real-time Accountability for Carbon Compliance), a Web 3.0 IoT-blockchain platform that links decentralized methane sensor reporting to on-chain liability assignment through device-bound wallets and non-tradable Carbon Burden Tokens (CBTs). It accompanies the paper "Automated Accountability Platforms for Carbon Governance: A Web 3.0 IoT-Blockchain Design and Evaluation." Data was collected over 30 days in November 2024 on the Polygon Amoy testnet, combining a physical IoT testbed (four Raspberry Pi 4 nodes with MQ-4 methane sensors and ADS1115 ADCs) and large-scale simulation (up to 1,500 concurrent sensors across 100 companies). The evaluation captures five dimensions: end-to-end accountability latency, throughput scalability, per-event cost, decentralized identity (DID) authentication reliability, and adversarial robustness. Contents (single .xlsx workbook, 9 sheets): 1. Summary - headline metrics: 1,500 sensors, 125,540 events, 25,372 violations (20.2%), 32,675 CBTs minted, 6.25 s mean latency, 94.25% mean DID authentication success, $0.00357 mean per-event cost, 146 adversarial scenarios all blocked. 2. Emission Events - 2,891-row representative sample with full per-event telemetry: timestamp, sensor and company IDs, methane reading (ppm), exceedance flag, severity tier (0-3), CBTs minted, six per-stage latency components (sensor detection, signing, contract submission, blockchain confirmation, webhook, dashboard), gas usage, gas price, POL price, USD cost, authentication outcome. 3. Daily Metrics - 30 rows: daily event totals, violation counts and rates, CBT mints, authentication success/failures, mean latency, mean cost per violation, and component uptime. 4. Scalability Tests - 45 rows: 15 sensor-count configurations (100 to 1,500), three trials each, with mean/min/max/std latency, success rate, failures, and CBT throughput. 5. Latency Breakdown - per-component latency statistics (mean, std, min, max, 50th/95th/99th percentiles) for the six pipeline stages. 6. Security Incidents - all 146 scripted adversarial scenarios across ten attack classes (Invalid DID, Replay, DDoS, Unauthorized Access, Sensor Tampering, Man-in-Middle, Signature Forgery, Sybil, Smart Contract Exploit, Network Intrusion), with timestamp, severity, detection time, blocked-flag, and response action. 7. Resource Utilization - CPU, memory, network bandwidth, and storage usage at 13 sensor-count configurations. 8. Comparative Analysis - TRACC against manual auditing, CEMS, generic blockchain carbon systems, and a published MRV+O system, normalised to per-event metrics. 9. Cost Analysis - 24 hourly observations of Polygon gas price, confirmation time, and per-CBT cost. Related resources: - Source code: https://github.com/CryptoGuy1/TRACC-On-Chain-Emission-Tracking-via-DePIN - Live dashboard: https://trac-alpha.vercel.app/

Steps to reproduce

Hardware: - Physical testbed: 4 x Raspberry Pi 4 Model B (4 GB RAM, Raspberry Pi OS Bookworm 64-bit), each with one MQ-4 methane sensor and one ADS1115 16-bit I2C ADC. WiFi with Ethernet fallback. Sensors sample atmospheric methane at 10-second intervals. - Simulation host: standard Linux workstation (16 GB RAM or higher recommended for runs above 1,000 sensors). Software: - Solidity 0.8.20, Hardhat, OpenZeppelin Contracts, ERC-6551 Reference Implementation - Node.js 18 LTS, Ethers.js v6, React 18, SpruceID didkit v0.3 - Python 3.10+ with pandas, numpy, web3.py, eth-account Deployment: 1. Clone the repository (see Description). 2. Provision a Polygon Amoy testnet account with at least 1 POL; add the private key to .env. 3. Deploy the five smart contracts in order: SensorRegistryNFT, ERC6551Registry, CarbonBurdenToken, TraccController, USDT mock. Run: npx hardhat run scripts/deploy.js --network amoy. Sensor registration: 4. On each Pi, generate a key pair, derive a W3C DID from the public key, build a DID Document with controlling-company DID and metadata, upload to IPFS, and record the content hash on-chain via SensorRegistryNFT.register(). Registration also mints the sensor NFT and deterministically derives the ERC-6551 device-bound wallet via the CREATE2 formula in Eq. (1) of the paper. 30-day evaluation: 5. Physical trial: start the on-Pi reporter (python sensor_node/reporter.py --config <node.yaml>). Each node samples methane at 10 s, signs each reading, and POSTs to the backend, which submits the verification-and-minting transaction. 6. Simulation: python sim/run_evaluation.py --sensors 1500 --companies 100 --days 30 --threshold 500 --pareto-alpha 1.2 --pareto-scale 200 --jitter-ms 1000 --seed 42. Methane is drawn from a Pareto distribution (alpha=1.2, scale=200 ppm), reproducing the 20.2% exceedance rate against the 500 ppm threshold. 7. Scalability sweep: python sim/scalability_sweep.py --configs 100,200,300,400,500,600,750,800,900,1000,1100,1200,1250,1350,1500 --trials 3 --seed 42. 8. Cost sweep: python sim/cost_observation.py --hours 24 --interval 1h over the same window. 9. Adversarial harness: python sec/adversarial_suite.py --scenarios all. Issues malformed or attacker-controlled inputs across ten classes (47 Invalid DID, 23 Replay, 18 DDoS, 15 Unauthorized Access, 12 Sensor Tampering, 9 Man-in-Middle, 8 Signature Forgery, 7 Sybil, 4 Smart Contract Exploit, 3 Network Intrusion). A scenario is blocked only when rejected at the relevant verification layer with no unauthorized state change. Aggregation: 10. python analysis/build_workbook.py --in runs/ --out tracc_performance_data.xlsx produces the nine-sheet workbook. Determinism: --seed 42 reproduces the simulation headline numbers. Smart-contract execution is deterministic given the same input, but on-chain timing depends on Polygon Amoy network conditions during the run, so latency and gas-cost numbers from re-runs will differ slightly.

Institutions

• University of Wyoming

  Wyoming, Laramie

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