Improving the resource allocation with enhanced learning in wireless sensor networks

Abstract

Efficient resource allocation is crucial for optimizing performance and extending the lifespan of wireless sensor networks (WSNs), which are often constrained by limited energy and bandwidth. This paper proposes an enhanced learning approach (ELA) for dynamic resource allocation in WSNs, leveraging augmented reinforcement learning to adaptively manage energy consumption, optimize routing, and schedule node activity. ELA integrates predictive feedback and real-time data from network states to refine policy decisions, enabling the network to maintain optimal performance under varying traffic loads and environmental conditions. Comparative analyses with existing methods, including deep neural networks (DNN), artificial neural networks (ANN), and support vector machines (SVM), demonstrate that ELA achieves superior results across multiple key metrics: energy consumption, network lifetime, packet delivery ratio, end-to-end delay, and throughput. Our findings indicate that ELA can sustain higher data reliability and throughput while minimizing latency and energy depletion, addressing fundamental challenges in WSNs. The proposed approach presents a scalable and adaptive solution that is well-suited for real-time and large-scale IoT applications, making it a valuable contribution to the advancement of intelligent resource management in WSNs.