Energy harvesting and resource allocation optimization in IOT devices

Lee, Si Kang (2026) Energy harvesting and resource allocation optimization in IOT devices. Final Year Project, UTAR.

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Abstract

In the Beyond-5G (B5G) era, the massive deployment of Internet of Things (IoT) devices faces a critical bottleneck regarding dynamic energy availability and limited battery lifespans. This project addresses the fundamental inefficiencies in existing Energy Harvesting (EH) IoT networks, specifically the severe performance degradation caused by stochastic energy arrivals and the reliance on Fixed Power Splitting (FPS) ratios in Simultaneous Wireless Information and Power Transfer (SWIPT) architectures. The methodology adopts a mathematical system model that integrates a normalized Lyapunov optimization framework with an Adaptive SWIPT mechanism. By executing low-complexity, real-time centralized scheduling through an Access Point, the system dynamically manages both uplink transmission power and downlink power-splitting ratios. The research process involved rigorous MATLAB simulations evaluating the framework against standard FPS baselines under normal and extreme environmental energy droughts. Results demonstrate that the proposed adaptive model successfully prevents data queue explosions under heavy traffic loads, achieves superior energy efficiency, and dynamically shifts between maximizing downlink throughput and harvesting life-saving energy. Notably, during simulated physical energy droughts, the adaptive framework maintained a 60% network survival rate, whereas aggressive fixed policies suffered total system outages. In conclusion, the integrated algorithmic model effectively extends network lifetime and guarantees queue stability, offering a sustainable resource allocation solution for future zero-maintenance IoT deployments.

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