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.

A Multi-Task Decomposition-Based Evolutionary Algorithm for Tackling High-Dimensional Bi-Objective Feature Selection

“…There are also many other kinds of excellent MOEAs [27][28][29], including the novel multi-objective particle swarm optimization algorithm proposed by Leung et al [30], which adopted a hybrid global leader selection strategy with two leaders: one for exploration and the other for exploitation. Moreover, MOEAs have also been used to solve many real-world optimization problems [31][32][33], such as system control [34,35], community detection [36,37], network construction [38][39][40], task allocation [41,42], and feature selection [43,44]. Generally speaking, feature selection is normally used to select useful feature subsets for classification [45], while the bi-objective feature selection problem usually seeks to minimize both the classification error and the number of selected features [46].…”

A Dynamic Tasking-Based Evolutionary Algorithm for Bi-Objective Feature Selection

“…There are numerous other kinds of excellent MOEAs [57][58][59][60] that have been proposed around the world, many of which are used for real-world applications, such as intrusion detection in networks [61], efficient sensing in wireless sensor networks [62], control of building systems [63], menu planning in schools [64] and control of hybrid electric vehicle charging systems [65].…”

A Combined Scheme Based on Artificial Immune System for Selective Forwarding Attack Detection in Event Driven Wireless Sensor Networks

“…Wireless Sensor Networks (WSNs) [1] are self governing networks that communicate wirelessly, gathering and transmitting environmental data [2]. Event driven Wireless Sensor Networks (EWSNs) [3] are widely used for data collection purposes. Unlike conventional networks that constantly gather information, EWSNs gather the information once predetermined events are identified by the SN within the arrangement.…”