Abstract
Wireless Sensor Networks (WSNs) is a widely used technology for remote area monitoring in collaboration with the Internet of Things (IoT). The fundamental research challenge of mobile sensor nodes for the WSN community is localization. The sensor node localization of the WSN is related to the NP-hard problem, and because of this, determining the actual coordinate of the sensor node is quite complex. The computational intelligence approach is assisted in obtaining an optimal solution to the given NP-hard problem. Most researchers today are more concerned about three beacon-based localization approaches, but the fewest researchers are concerned about two or single beacon-based localization approaches. This paper provides a single beacon-based localization approach using the hybrid approach of the Eurasian Wolves Optimizer (EWO) and the Cuckoo Search Optimizer (CSO) algorithm called the EW-CSO computational intelligence algorithm for randomly deployed mobile sensor nodes. The simulation results of the computational intelligence algorithms show that the proposed work using EW-CSO performs better in terms of mean localization error, computational cost, and number of localized nodes from the EWO and EW- Particle Swarm Optimization (EW-PSO) algorithms. It also reduced the line of sight problem for mobile sensor nodes with efficient use of network resources.
Highlights
Today is the era of technological automation [1], where systems are designed with the help of global networks (Internet) in such a way that human intervention is minimized
The presented literature survey paper concern about the three-beacon based location, and they are trying to improve the measured position of sensor nodes using computational intelligence algorithms such as Particle Swarm Optimization (PSO), Biography-Based Optimization (BBO), Firefly Algorithm (FA), Artificial Bee Colony (ABC), Bat Algorithm (BA), Eurasian Wolves Optimizer (EWO), etc
The localization of the mobile sensor node poses a significant challenge for Wireless Sensor Networks (WSNs)
Summary
Today is the era of technological automation [1], where systems are designed with the help of global networks (Internet) in such a way that human intervention is minimized. Researchers worked with the IoT system to meet all the requirements of technical automation [2] [3] [4]. These types of systems consume a lot of data to solve real-time challenges. This sub-section of the introduction section describes how well-known computational intelligence algorithms work Computational intelligence algorithms such as EWO, CSO, and PSO are as follows: Eurasian Wolves Optimizer (EWO): Mirjalili et al [24] proposed an EWO algorithm for eurasian wolves' inspired leadership quality. It is far superior to other swarm optimization algorithms The social pecking order is simulated by classifying the population of search agents based on their fitness:
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