A Multi-Objective Crowding Optimization Solution for Efficient Sensing as a Service in Virtualized Wireless Sensor Networks

Abstract

The Internet of Things (IoT) encompasses a wide range of applications and service domains, from smart cities, autonomous vehicles, surveillance, medical devices, to crop control. Virtualization in wireless sensor networks (WSNs) is widely regarded as the most revolutionary technological technique used in these areas. Due to node failure or communication latency and the regular identification of nodes in WSNs, virtualization in WSNs presents additional hurdles. Previous research on virtual WSNs has focused on issues such as resource maximization, node failure, and link-failure-based survivability, but has neglected to account for the impact of communication latency. Communication connection latency in WSNs has an effect on various virtual networks providing IoT services. There is a lack of research in this field at the present time. In this study, we utilize the Evolutionary Multi-Objective Crowding Algorithm (EMOCA) to maximize fault tolerance and minimize communication delay for virtual network embedding in WSN environments for service-oriented applications focusing on heterogeneous virtual networks in the IoT. Unlike the current wireless virtualization approach, which uses the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), EMOCA uses both domination and diversity criteria in the evolving population for optimization problems. The analysis of the results demonstrates that the proposed framework successfully optimizes fault tolerance and communication delay for virtualization in WSNs.