An On-Device Edge-AI Agent for Reference-Free Sensor Self-Diagnosis in Multi-Pollutant Personal Exposure Monitoring

Personal environmental exposure monitoring increasingly relies on low-cost multi-pollutant sensors, yet existing devices keep all perceptual intelligence on remote servers. As a result the device cannot self-check sensor degradation in real time, onboarding a new sensor takes days of firmware work, and quality control fails whenever connectivity is lost. We present Zhiwei, a personal-exposure monitoring system that internalizes the reasoning loop on the device. Built on a Raspberry Pi 5, it combines reference-free on-device multi-sensor self-diagnosis, a five-layer declarative skill-package mechanism with a capability-association graph for plug-and-play sensor onboarding, and a three-tier resilient reasoning architecture that sustains quality control offline. Over a 30-day indoor deployment in Beijing comprising 1,896,789 records at 99.9% completeness, the device autonomously graded a nominal oxidizing-gas channel as untrustworthy for ozone from three complementary physical-consistency checks: a temperature dependence of −7.9 ppb per °C, a cross-interference of the wrong sign with NO2, and an apparent drift that became non-significant once confounders were removed. It also confirmed the relative consistency of the PM2.5 and NO2 channels against a nearby reference station, with correlations of 0.90 and 0.86. Zhiwei establishes the feasibility of fully on‑device autonomous sensor quality assurance for trustworthy personal‑exposure monitoring.