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A three-dimensional wireless sensor network with an improved localization algorithm based on orthogonal learning class topper optimization
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A three-dimensional wireless sensor network with an improved localization algorithm based on orthogonal learning class topper optimization
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Journal Article
A three-dimensional wireless sensor network with an improved localization algorithm based on orthogonal learning class topper optimization
2023
Overview
Numerous sensor network applications require accurate and rapid localization of randomly deployed sensor nodes. For wireless sensor network (WSN) localization, optimization methods can provide specific and reliable position estimates of a sensor node. The fixed density of beacons may be increase or decrease owing to various reasons, such as upkeep, lifespan, and breakdown. Because of its robustness, flexibility, and economic viability, the distance vector-hop (DV-Hop) algorithm is used to locate WSN nodes. Because of its high precision and fast computing speed, class topper optimization (CTO) is suitable to solve localization problems. This study proposes an orthogonal learning CTO-based DV-Hop localization algorithm for three-dimensional WSNs. Moreover, this study used a refined formula to calculate the minimum hop size of beacon nodes for reducing localization errors (LEs) in the approximated distance between the beacon and dumb nodes. Results revealed that our proposed method outperformed some existing algorithms in terms of reducing LEs (0.6
%
) and localization error variance (0.3
%
) and enhancing localization accuracy (0.4
%
) and coverage (0.7
%
).
Publisher
Springer International Publishing,Springer Nature B.V
