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The Internet of Things (IoT) has vast potential to drive connectivity and automation across various sectors, yet energy inefficiency remains a critical barrier to achieving sustainable, high-performing networks. This study aims to identify and address the primary causes of energy wastage in IoT systems, proposing a framework to optimise energy consumption and improve overall system performance. A comprehensive literature review was conducted, focusing on studies from 2010 onwards across major databases, resulting in the identification of eleven key factors driving energy inefficiency: offloading, scheduling, latency, changing topology, load balancing, node deployment, resource management, congestion, clustering, routing, and limited bandwidth. The impact of each factor on energy usage was analysed, leading to a proposed framework that incorporates optimised communication protocols (such as CoAP and MQTT), adaptive fuzzy logic systems, and bio-inspired algorithms to streamline resource management and enhance network stability. This framework presents actionable strategies to improve IoT energy efficiency, extend device lifespan, and reduce operational costs. By addressing these energy inefficiency challenges, this study provides a path forward for more sustainable IoT systems, emphasising the need for continued research into experimental validations, context-aware solutions, and AI-driven energy management to ensure scalable and resilient IoT deployment.
Almudayni et al. (Thu,) studied this question.