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A two-stage task offloading framework for IoT edge computing using BEVST initialization and Lévy-SOA optimization
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DOI：10.1007/s00542-026-06039-8.png)
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Abstract
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The growth of latency-sensitive Internet of Things (IoT) applications has intensified the need for efficient task offloading in edge computing environments. Existing metaheuristic-based solutions often suffer from poor initialization and premature convergence, resulting in inefficient resource utilization. This paper presents a two-stage task offloading framework that integrates the Best Edge VM Shortest Task (BEVST) initialization strategy with the Levy-Seagull Optimization Algorithm (Levy-SOA). BEVST provides a structure-aware warm start by assigning short tasks to suitable edge virtual machines, enabling balanced workload distribution. Levy-SOA then refines task execution using Levy flight-based exploration to escape local optima. Experimental results show that the proposed framework reduces makespan by about 13%, task rejection ratio by 25%, execution cost by 7%, and energy consumption by 15% compared to conventional methods, demonstrating its effectiveness for dynamic IoT-edge-cloud environments.
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