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A new hybrid localization approach in wireless sensor networks based on particle swarm optimization and tabu search

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Abstract

In recent years, many works have proposed solutions for indoor localization in Wireless Sensor Networks (WSN). The challenge in these different works is above all to improve localization accuracy. New trends in the field are the use of optimization techniques to improve the accuracy in determining the location of a sensor. Thus, this study aims to propose a new contribution to the indoor localization problem in WSN based on optimization techniques. The designed approach improves the performance of particle swarm optimization (PSO). In this improved version of PSO, on the one hand, a form of tabu search is used by each particle to determine its best local neighbor in order to accelerate its possibilities of convergence towards a better solution. On the other hand, limit and performance checks are introduced into the PSO algorithm to evolve only with better particles belonging to the search space constructed by constraint analysis, around an initial solution obtained by trilateration. This proposed approach called FPSOTS uses the received signal strength indicator (RSSI) method to evaluate inter-sensor distances. Localization accuracy and convergence performances of the FPSOTS approach were evaluated in simulation and compared with other recent localization approaches based on optimization techniques. Results show that FPSOTS succeeds in locating unknown nodes of a WSN with fast convergence and better accuracy than recent state-of-the-art approaches such as HPSOVNS, NS-IPSO, ECS-NL and GTOA. Indeed, in comparison with these four approaches, the accuracy of FPSOTS approach was better by 40%, 35%, 44% and 22% respectively.

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Authors and Affiliations

  1. Department of Computer Science, University of Dschang, Dschang, Cameroon

    Elie Tagne Fute & Doris-Khöler Nyabeye Pangop

  2. Department of Computer Engineering, University of Buea, Buea, Cameroon

    Elie Tagne Fute

  3. Department of Electrical and Electronics Engineering, University of Yaounde I, Yaounde, Cameroon

    Emmanuel Tonye

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  1. Elie Tagne Fute
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  2. Doris-Khöler Nyabeye Pangop
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  3. Emmanuel Tonye
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Correspondence to Doris-Khöler Nyabeye Pangop.

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Tagne Fute, E., Nyabeye Pangop, DK. & Tonye, E. A new hybrid localization approach in wireless sensor networks based on particle swarm optimization and tabu search. Appl Intell 53, 7546–7561 (2023). https://doi.org/10.1007/s10489-022-03872-y

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