Securing AI-Driven Systems for Environmental Sustainability in IoT Applications: A Lightweight Message-Authentication Scheme Against Telemetry Poisoning.

Machine-learning models for environmental monitoring are only as trustworthy as the field measurements that train them, yet raw or custom LoRa telemetry links-unlike a correctly configured LoRaWAN network-carry no application-layer authentication, letting any in-range transmitter inject fabricated readings and poison the training pipeline. We propose a lightweight, unidirectional, encryption-free scheme in which each message carries an HMAC-SHA-256 tag over the device identifier, a timestamp, a monotonic counter, and the sensor payload; the server accepts a message only if its tag verifies and its counter exceeds a durable, transactionally updated high-water mark, giving origin authentication, tamper evidence, and duplicate or stale rejection without encryption. For an ESP32-S3/Heltec reference implementation, device-server-reference HMAC agreement was confirmed on a published test vector, with zero false accepts or rejects across a 25,000-message four-class adversarial test; both checks ran in a host-side reference implementation of the device's algorithm, not on physical ESP32-S3 hardware. A ten-seed illustrative poisoning study on two public environmental datasets shows a value-based defence can increase PM2.5 forecast RMSE by up to about 104% by discarding genuine extremes, whereas our scheme holds the clean baseline exactly, since forged rows are rejected before training. The tag grows an example message from 34 to 99 bytes (about 2.9×) and roughly doubles airtime (about 2.2×); per-message CPU time and energy are not quantified here. Under independent per-device keys and rate limits, partial key compromise bounds admitted forged traffic to the compromised devices' share of total traffic.