Energy Consumption Optimization based on Economic Benefit in WSN-based IoT via Global Hierarchical Caching Strategy

Recent advances in Wireless Sensor Network (WSN) have significantly contributed to the prevalence of Internet of Things (IoT) devices and their pervasive integration into everyday life and industrial operations. A WSN-based IoT comprises numerous small, scattered, battery-operated sensors that are designed to perform collaborative tasks. These sensor nodes are prone to energy drain because of their limited battery capacity when they needed to run efficiently over extended periods of time. Enhancing the large-scale network's lifespan and controlling the economic costs become relevant since battery replacement or recharging is impractical in severe environments. In this paper, we present a novel energy optimization algorithm tailored for WSNs, which not only takes into account the reduction in replacement costs stemming from prolonged equipment lifespan but also incorporates the operational savings resulting from enhanced energy efficiency. It aims to enhance energy efficiency by leveraging a global hierarchical caching mechanism to simultaneously balance exploration and exploitation of energy resources in both the uplink and downlink of the networks. The simulation results demonstrate that our algorithm effectively minimizes energy consumption while maintaining optimal economic efficiency by decreasing the frequency of state transitions. It can consume 13% less energy than original system and extend the network lifetime by 10%.