Metaheuristic-Driven Secure Task Optimization for Consumer Edge Devices

The swift expansion of consumer electronics has led to the rapid development of multiaccess edge computing (MEC). This technology is indispensable for the real-time processing of data in Internet of Things (IoT) environments. The proximity of MEC to data sources, including autonomous vehicles and IoT sensors, leads to rapid processing and reduced latency. However, this proximity also exposes MEC systems to security vulnerabilities and complex task assignment challenges. This article presents a robust, metaheuristic-based technique for securing and optimizing task assignments in MEC systems. Our technique solves two issues: effective job distribution and intrusion detection. The flexible parameter grid search (FPGS) technique optimizes the performance of an artificial neural network for intrusion detection. Deep particle swarm optimization dynamically assigns tasks and balances computational loads based on real-time network conditions and device capabilities. The efficiency of the proposed framework is demonstrated by comprehensive experiments using three standard datasets, which show considerable gains in system performance, reduced latency, and efficient resource utilization. These findings demonstrate that metaheuristic algorithms and FPGS may improve MEC security and operational efficiency, enabling robust and effective IoT applications.