IoT-Based Load Management System with Edge Computing For Real-Time Decision Making

Abstract

Intelligent load control solutions that can function in real time are necessary due to the quick expansion of electrical loads & the growing need for energy efficiency. The efficacy of traditional cloud-centered Internet of Things (IoT) devices for time-sensitive energy-related uses is generally limited by high latency, higher bandwidth consumption, and delayed decision-making. This research proposes an Internet of Things-Based Load Management Platform with Edge Technology for Real-Time Decision-making as a solution to these problems. The suggested system continually monitors electrical factors including voltage, current, consumption of energy, and load status by integrating IoT-enabled devices and smart sensors. Without depending on continuous cloud communication, computing edge nodes process the collected data locally, allowing for low-latency analysis and quick control actions. Deployed at the edge, intelligent decision-making algorithms dynamically control electrical loads according to predetermined thresholds, priority levels, and current demand conditions. Compared to traditional cloud-based methods, the system architecture increases reaction time, decreases congestion in the network, and improves reliability. The suggested edge-enabled IoT framework successfully optimises energy consumption, avoids overload situations, and facilitates scalable deployment in commercial, industrial, and residential settings, according to experimental results. For future-oriented smart energy management systems, the suggested solution provides a workable and effective method.