Enhancing IOT Energy Efficiency Using Distributed Edge Computing Mechanisms
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The rapid expansion of the Internet of Things (IOT) has significantly increased the number of interconnected smart devices across healthcare, agriculture, smart cities, industrial automation, and transportation systems. However, the continuous operation and data transmission of IoT devices result in high energy consumption, limited battery lifespan, increased network congestion, and reduced system efficiency. Traditional cloud-centric architectures often introduce higher latency and excessive energy utilization due to long-distance communication and centralized data processing. To address these challenges, this paper presents a distributed edge computing mechanism for enhancing energy efficiency in IoT environments. The proposed framework utilizes decentralized edge nodes to process, filter, and manage data closer to IoT devices, thereby reducing communication overhead, minimizing latency, and optimizing computational resource allocation. The framework integrates intelligent task scheduling, adaptive load balancing, and energy-aware data processing techniques to improve overall system performance while conserving device energy. Experimental analysis demonstrates that the proposed approach significantly reduces energy consumption, improves response time, enhances network reliability, and extends the operational lifetime of IoT devices compared with conventional cloud-based models. The findings indicate that distributed edge computing provides an effective and scalable solution for achieving sustainable and energy-efficient IoT ecosystems in next-generation smart applications.
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