Energy Aware Task Scheduling of IoT Application Using a Hybrid Metaheuristic Algorithm in Cloud Computing

This research looks at how to schedule tasks in systems that combine IoT devices, fog nodes, and cloud servers. Handling the huge amount of data these devices produce is a big challenge. When IoT devices send many requests, we need to assign tasks wisely to cut energy use, lower costs, and boost overall performance. The paper proposes a hybrid metaheuristic algorithm. It spreads work and schedules tasks across many cloud and fog nodes. By mixing several optimization techniques, the hybrid method avoids the problems of single‑heuristic approaches, which can get stuck early or require too much computation. Because the source material only gives a high‑level view of the scheduling framework

and lacks detailed experimental data, the project focuses on simulating the strategies with common cloud‑simulation tools. Students can create different IoT workload scenarios and measure makespan, energy consumption, and resource utilization. The provided material guides you through setting up the simulation environment, defining virtual‑machine configurations, and coding the hybrid metaheuristic logic so you can study trade‑offs in resource‑limited settings.

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