Intelligent Sensor Networks for Real-Time Environmental Monitoring

Abstract

Real-time environmental monitoring at urban and regional scales requires dense, heterogeneous sensor networks capable of continuous
multi-pollutant measurement, anomaly detection, and adaptive sampling under energy and communication constraints. Existing deployments suffer
from sensor drift, missing data due to network failures, and the absence of intelligent data fusion that integrates low-cost sensor arrays with
reference-grade instruments. This study presents EnviroSense, an intelligent sensor network framework integrating edge-deployed anomaly
detection, cross-sensor calibration via transfer learning, and spatiotemporal kriging-augmented deep learning for real-time pollutant field
reconstruction. EnviroSense was deployed across three real-world environmental monitoring networks: Hamburg urban air quality (142 low-cost
PM2.5/NO2 nodes, 18-month operation), Barcelona coastal water quality (64 multi-parameter buoys, 12-month operation), and Grenoble alpine
meteorological network (88 mountain weather stations, 24-month operation). EnviroSense achieved sensor calibration accuracy of R2 = 0.947 vs.
reference instruments, missing data imputation RMSE reduction of 43.7% over kriging-only baselines, and anomaly detection F1-score of 0.924.
Pollutant field reconstruction latency averaged 2.3 seconds per 1 km2 grid cell update. These results establish EnviroSense as a scalable, accurate
framework for intelligent real-time environmental monitoring in complex urban and natural environments.