Energy Conservation through Efficient Data Collection and Processing Techniques in Resource-Constrained Wireless Sensor Networks

Abstract

This paper presents a framework for energy conservation in resource-constrained wireless sensor networks (WSNs) through optimized data collection and processing techniques. We introduce a novel hybrid approach that integrates adaptive sampling, compressive sensing, and hierarchical clustering to minimize energy consumption while maintaining data integrity. Our mathematical model quantifies the energy-accuracy tradeoff using a multi-objective optimization framework that considers spatial-temporal correlations in sensed data. Rigorous analysis demonstrates that the proposed framework achieves 37.8% reduction in energy consumption compared to traditional approaches while maintaining 94.2% data reconstruction accuracy. We further extend our approach with a reinforcement learning mechanism that dynamically adjusts sampling parameters based on environmental conditions and application requirements. Experimental evaluation on both simulated environments and real-world deployments confirms the efficacy of our approach across diverse sensing scenarios including environmental monitoring, structural health assessment, and agricultural applications. The results indicate significant improvements in network lifetime without compromising application-specific quality of service requirements, thereby addressing a critical challenge in practical WSN deployments.