AI-driven sensor fusion for real-time environmental monitoring networks

Thesis Abstract

This study addresses the challenge of achieving timely, accurate environmental monitoring in heterogeneous sensor networks by integrating artificial intelligence-driven sensor fusion to enhance data quality, reduce latency, and improve robustness against sensor failures and environmental perturbations. The aim is to develop and validate a scalable AI-based sensor fusion framework that leverages multimodal data streams (air quality, meteorological, acoustic, and visual sensors) to deliver real-time situational awareness for environmental management. Specific objectives are to (i) design a multi-sensor fusion architecture incorporating deep learning and probabilistic inference, (ii) evaluate data quality improvement and anomaly detection under varying environmental conditions, (iii) quantify improvements in spatial-temporal resolution and forecast accuracy, (iv) assess system resilience to node failures and communication disruptions, and (v) provide a deployable workflow for urban and peri-urban monitoring networks. The methodology adopts a mixed-methods research design combining quantitative experimentation with qualitative system evaluation. The population comprises sensor nodes deployed across an urban region covering 50 square kilometers, including 120 fixed stations and 30 mobile units mounted on public transport. A stratified random sample of 200 sensor time-series segments (each 24 hours long) is selected to capture diurnal and meteorological variability, supplemented by 40 field validation campaigns using portable reference instruments. Data collection instruments include low-cost multi-parameter air quality sensors, high-precision meteorological stations, acoustic noise meters, and short-wave infrared cameras, alongside maintenance logs and network health metrics. Data preprocessing employs standardized calibration procedures, synchronization across time stamps, and imputation for intermittent gaps. The fusion model integrates a recurrent neural network encoder-decoder with attention mechanisms and a Bayesian fusion layer to produce calibrated, fused estimates with quantified uncertainty. Model development iterates through three stages (1) feature extraction and sensor-specific modeling, (2) cross-modal fusion with temporal alignment and uncertainty propagation, and (3) anomaly detection and fault-tolerant inference. Validity and reliability are ensured through cross-validation, hold-out testing on unseen events, and repeatability checks using synthetic perturbations. Analytical techniques include regression analysis for calibration accuracy, time-series analysis with ARIMA and LSTM components for short-term forecasting, likelihood ratio tests for model comparison, and ROC/AUC metrics for anomaly detection performance. The study also employs Monte Carlo simulations to assess uncertainty under sensor dropout scenarios, and conduct sensitivity analyses to determine the influence of individual modalities. Expected findings indicate that the AI-driven fusion framework yields statistically significant reductions in root-mean-square error by 25–40% for pollutant concentration estimates and enhances forecast skill scores by 15–25% for 6- to 12-hour ahead predictions, compared with baseline kalman-filter-based fusion. The approach is anticipated to improve spatial resolution by up to 60% and reduce data loss impact through robust imputation and predictive confidence intervals. The study contributes to knowledge by demonstrating a scalable, uncertainty-aware sensor fusion paradigm that integrates deep learning with probabilistic inference for real-time environmental monitoring, and by providing a transferable methodology for urban environmental management that combines data provenance, model interpretability, and system resilience. The main conclusion posits that AI-driven sensor fusion substantially improves the reliability and timeliness of environmental monitoring networks in complex urban settings, enabling more informed decision-making for air quality management, noise mitigation, and climate resilience. Recommendations include implementing the framework in city-scale pilot projects, integrating citizen science data to enhance coverage, developing standardized benchmarks for multimodal fusion, and advancing edge-computing strategies to sustain real-time operation under communication constraints.