Quantum-Inspired Differential Evolution Based Efficient IoT Service Placement in Edge Networks

The rise in Internet of Things (IoT) devices has led to the creation of sophisticated applications that demand various resources in real time to support a wide range of IoT services. Leveraging edge computing (EC) infrastructure, these services can be effectively placed on edge nodes (ENs). However, due to the limited computational resources of ENs, it becomes challenging to manage a large number of services while maintaining the system’s quality of service (QoS) and quality of experience (QoE). This paper introduces a quantum-inspired differential evolution method (QIDE-IoTSP) designed to optimize the placement of IoT services within EC networks. The primary objectives of QIDE-IoTSP are to maximize throughput, ensure optimal load balance, and minimize computation time. A quantum vector (QV) is utilized to develop a robust solution for the optimal deployment of IoT services in EC networks to achieve this. The effectiveness of each solution is evaluated using a formulated fitness function. Simulation results demonstrate that QIDE-IoTSP surpasses other metaheuristic techniques in terms of throughput, computation latency, and load balancing.