International Journal of Sensors, Wireless Communications and Control

2210-32792210-3287

Bentham Science

645463

10.2174/0122103279270847231205100550

Computer and Information Science

Research Article

A Novel Polytope Algorithm based On Nelder-mead Method for Localization in Wireless Sensor Network

Gumaida

Bassam

info@benthamscience.netIbrahim

Adamu

info@benthamscience.net

Department of Computer Science, International Islamic University Malaysia

01012024

141213511012025

2024

Bentham Science Publishers

https://journals.eco-vector.com/2210-3279/article/view/645463

Background and Objective:Magnificent localization precision and low operating expenses are the main keys and essential issues to managing and operating outdoor wireless sensor networks. This work proposes a novel and rigorous efficiency localization algorithm utilizing a simplex optimization approach for node localization. This novel optimization method is a direct search approach, and is usually directed to solve nonlinear optimization problems that may not have wellknown derivatives, and it is called the Nelder-mead Method (NMM).

Methods:It is suggested that the objective function that will be optimized using NMM is the mean squared error of the range of all neighboring anchor nodes installed in the studied WSNs. This paper emphasizes employing a ranging technique called Received Signal Strength Indicator (shortly RSSI) to calculate the length of distances among all the nodes of WSNs.

Results:Simulation results perfectly showed that the suggested localization algorithm based on NMM can carry out a better performance than that of other localization algorithms utilizing other optimization approaches, including a particle swarm optimization, ant colony (ACO) and bat algorithm (BA). This obviously appeared in several metrics of performance evaluation, such as accuracy of localization, node localization rate, and implementation time.

Conclusions:The proposed algorithm that utilized NMM is more functional to enhance the precision of localization because of particular characteristics that are the flexible implementation of NMM and the free cost of using the RSSI technique.

Wireless sensor networksranging modelRSSIoptimization techniquesnelder mead methodlocalization.

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