Adaptive Hybrid Energy Harvesting With Integrated Sensing and Communication for Consumer IoT Networks

The growth of IoT in smart cities, industry, and consumer electronics requires energy-efficient solutions. This study presents the Adaptive Hybrid Energy Harvesting Algorithm (AHEHA) equipped with Integrated Sensing and Communication (ISAC). AHEHA-ISAC merges solar, thermal, and vibration energy harvesting with dynamic resource management, adaptive scheduling, and joint sensing–communication. Unlike traditional approaches, the proposed technique adopted entropy-maximizing strategies to diversify energy sources that minimize variability and balance sensing quality. The simulation shows the AHEHA-ISAC technique not only increases the network lifetime but also increases residual battery by 27.6%, and packet delivery by 14.9%. It also gains an average energy efficiency of 2.31± joules of work per joule (sensing-communication workload) harvested, surpassing the best reinforcement learning at 1.64 joules. Additionally, the joint optimization reduces latency by 37.7% at high traffic λ = 0.8, with delays below 60 ms, and boosts task completion by over 20%.