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A swarm intelligence based distributed localization technique for wireless sensor network

Published: 03 August 2012 Publication History

Abstract

Wireless sensor network (WSN) refers to a group of spatially dispersed and dedicated sensors for monitoring and recording the physical conditions of the environment and organizing the collected data at a central location. Sensor Localization is a fundamental challenge in WSN. In this paper localization is modeled as a multi dimensional optimization problem. A comparison study of energy of processing and transmission in a wireless node is done, main inference made is that transmission process consumes more than processing. An energy efficient distributed localization technique is proposed. Distributive localization is addressed using swarm techniques Particle Swarm Optimization (PSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO) because of their quick convergence to quality solutions. The performances of both algorithms are studied. The accuracy of both algorithms is analyzed using parameters such as number of nodes localized, computational time and localization error. A simulation was conducted for 100 target nodes and 20 beacon nodes, the results show that the PSO based localization is faster and CLPSO is more accurate.

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cover image ACM Other conferences
ICACCI '12: Proceedings of the International Conference on Advances in Computing, Communications and Informatics
August 2012
1307 pages
ISBN:9781450311960
DOI:10.1145/2345396
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 03 August 2012

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Author Tags

  1. comprehensive learning particle swarm optimization (CLPSO)
  2. localization
  3. particle swarm optimization (PSO)
  4. wireless sensor networks (WSN)

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  • RPS

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Cited By

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  • (2021)Impact of K-NN imputation Technique on Performance of Deep Learning based DFL Algorithm2021 Sixth International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)10.1109/WiSPNET51692.2021.9419414(153-157)Online publication date: 25-Mar-2021
  • (2021)An Efficient Localization Approach in Wireless Sensor Networks Using Chicken Swarm Optimization2021 International Conference on Information Systems and Advanced Technologies (ICISAT)10.1109/ICISAT54145.2021.9678446(1-6)Online publication date: 27-Dec-2021
  • (2021)Computational intelligence techniques for localization and clustering in wireless sensor networksRecent Trends in Computational Intelligence Enabled Research10.1016/B978-0-12-822844-9.00011-6(23-40)Online publication date: 2021
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  • (2021)An Energy-Efficient Communication Scheme for Multi-robot Coordination Deployed for Search and Rescue OperationsCommunication and Intelligent Systems10.1007/978-981-16-1089-9_16(187-199)Online publication date: 29-Jun-2021
  • (2021)Performance Analysis of Machine Learning Techniques in Device Free Localization in Indoor EnvironmentAdvances in Computing and Data Sciences10.1007/978-3-030-81462-5_49(550-560)Online publication date: 23-Oct-2021
  • (2018)An enhanced wireless sensor network localization scheme for radio irregularity models using hybrid fuzzy deep extreme learning machinesWireless Networks10.1007/s11276-016-1372-224:3(799-819)Online publication date: 1-Apr-2018
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  • (2016)Indoor Robot Positioning Using an Enhanced Trilateration AlgorithmInternational Journal of Advanced Robotic Systems10.5772/6324613:3Online publication date: 1-Jan-2016
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