Multi-objective deep Q-self-attention transformer for PM2.5 prediction on edge devices

Abstract

Air quality monitoring is important for environmental management, especially to forecast PM2.5-level pollution concentration. Classical DL models involve heavy computational power, inefficient task offloading, and poor adaptability. Hence, it is ill-fitted for real-time edge application scenarios. To this end, Multi-objective Artificial Afterimage deep Q Self-Attention and Inter-sample Attention Transformer (MAAQ-SAINT) is proposed for PM2.5 prediction on edge devices such as Raspberry Pi 4B and 3B +. The design implements Regression Relief Feature Selection (RRFS) for optimal feature extraction, and Optimal Stopping Theory (OST) for task offloading. The Multi-objective Artificial Afterimage Algorithm (MAAA) is used to enhance prediction accuracy while reducing computational complexity. A self-attention mechanism and inter-sample attention mechanism learn spatial and temporal dependencies by maintaining robust performance. Quantization represents applied optimization for processing in resource-constrained environments. A performance evaluation using MAE, RMSE, and execution time shows that MAAQ-SAINT outperforms traditional techniques in terms of classification (prediction) accuracy (99%) and lower inference latency (120 ms), making it a good candidate for efficient PM2.5 forecasting in air quality monitoring.