Incentive-Based Energy-Efficient Workload Scheduling of Mobile Edge Computing Using Blockchain

In incentive-based mobile edge computing (MEC) systems, task execution depends on volunteer devices which are often limited in battery capacity. Continuous computation on such devices directly increases energy consumption and may reduce their operational lifetime. These issues become more critical in delay-sensitive and deadline-driven applications, where even small delays can affect system reliability and usefulness of the results. Therefore, efficient and energy-aware task scheduling becomes an important requirement. In this paper, we propose an enhanced whale optimization algorithm (WOA)-based scheduling framework for heterogeneous MEC environments. The proposed method considers multiple objectives including energy consumption, deadline satisfaction, and economic reward. It integrates energy awareness, a deadline-sensitive fitness formulation, constraint-repair mechanisms, and local search refinement to ensure feasible and efficient task allocation. Simulation experiments are conducted using real-world Bitbrains workload traces with heterogeneous device configurations. The results show that the proposed method reduces energy consumption by about 15–20% compared to genetic algorithm (GA), Particle Swarm Optimization (PSO), Harris Hawks Optimization (HHO), and DTOME variants. It also reduces deadline violations by approximately 20–25% and improves net income by around 10–18%. In addition, the proposed framework is able to maintain a higher number of active devices in later stages of scheduling. To ensure reliability, repeated experiments are performed, and results are reported with 95% confidence intervals. The ablation analysis further shows that energy awareness and the repair mechanism play a major role in achieving improved performance.