A Game-Theoretic Framework for Energy-Efficient Scheduling and Resource Allocation in Sensor Cloud Systems
Abstract
Sensor cloud systems play a vital role in Internet of Things (IoT) applications by enabling scalable sensing, data processing, and service provisioning; however, they face significant challenges in terms of energy-efficient scheduling and resource allocation. To address these challenges, this paper proposes a game-theoretic framework for energy-efficient scheduling and resource allocation in sensor cloud systems. The interactions between sensor nodes and cloud resources are modeled as a strategic game, where each entity autonomously optimizes its utility by balancing energy consumption, execution latency, and resource usage. An iterative, distributed scheduling algorithm is developed to achieve a stable equilibrium without centralized control.
Extensive simulation-based evaluation demonstrates that the proposed framework significantly reduces overall energy consumption and task completion time while improving resource utilization, throughput, and fairness compared to baseline heuristic approaches. Convergence analysis confirms fast stabilization of the algorithm, and statistical significance tests validate the robustness of the performance gains. The results indicate that the proposed game-theoretic approach provides an effective and scalable solution for energy-efficient scheduling and resource management in sensor cloud environments.
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