Hybrid Whale Optimization‐Based Energy‐Efficient Lightweight Internet of Things Framework

Abstract

The wireless intelligent computing paradigm has significantly provided services to various sectors in today's technology‐driven landscape. Despite its popularity, wireless intelligent computing faces challenges in addressing time‐sensitive tasks due to the physical distance between servers from users. Edge computing has been introduced for the internet of things (IoT) as an effective complement to enhance the wireless intelligent computing capacity for handling latency‐critical tasks. However, the limited resources of IoT and edge nodes can lead to suboptimal task management. In response to these challenges, we propose a lightweight approach that leverages a hybrid technique combining the whale optimization algorithm (WOA) with adaptive inertia weight and a genetic algorithm component. This method aims to enhance the efficiency of task offloading in a cloud‐edge computing environment. Experimental results demonstrate that the proposed strategy not only addresses the limitations of traditional methods but also achieves significant improvements, a 34% increase in makespan minimization, an 11% reduction in task rejection ratio, a 17% decrease in execution cost, and a 15% improvement in energy utilization compared to WOAs. The simulation results highlight the effectiveness of the proposed hybrid algorithm in enhancing quality of service (QoS) metrics for latency‐sensitive IoT applications.