Work

An On-Device Edge AI Agent for Reference-Free Self-Diagnosis of Low-Cost Multi-Pollutant Sensors

Year: 2026

Type: article

Abstract: Low-cost multi-pollutant sensors make personal exposure monitoring affordable, but assuring their data quality in the field is the bottleneck, while current devices leave it to remote servers: the field unit is a passive terminal that cannot self-check its sensors, takes days to accept a new one, and loses quality control whenever connectivity drops. We develop Zhiwei, an on-device edge AI agent for personal exposure monitoring that brings the reasoning loop onto the device, so it can diagnose its own sensors without a reference, onboard new ones through a declarative skill package with a capability-association graph, and keep working offline through a three-tier cloud-to-rule-engine fallback. We validate these capabilities, rather than field exposure tracking, in a 30-day fixed indoor deployment in Beijing of 1,896,789 records at 99.9% completeness. The agent decided on its own, without a reference, which channels to trust, identifying that the nominal ozone channel measures total oxidizing gas rather than ozone alone, a conclusion the manufacturer’s datasheet independently confirms, while the PM2.5 and NO2 channels were separately corroborated as relatively usable against a nearby station (r = 0.90 and 0.86). Under a simulated cloud outage, it kept data collection uninterrupted by handing inference to the on-device local model. This is a single fixed indoor site and a design-and-functional validation; evaluation under mobile, rapidly changing microenvironments is future field work. Zhiwei shows that an environmental sensing device can manage its own data quality autonomously on-device, a prerequisite for trustworthy personal exposure monitoring.

Source: Sensors

DOI: 10.3390/s26144526

Authors: Yinan Wang, Tianqi Wang, Yubing Pan

Institutions: Chinese Academy of Sciences, Institute of Atmospheric Physics, Chengdu University of Information Technology, Institute of Urban Meteorology

Language: English

Topic: Air Quality Monitoring and Forecasting

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funders: Chinese Academy of Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences, National Science and Technology Major Project