An effective Bat algorithm for node localization in distributed wireless sensor network. Issue 1 (4th December 2017)

• Record Type: Journal Article
• Authors: Mihoubi, Miloud; Rahmoun, Abdellatif; Lorenz, Pascal; Lasla, Noureddine
• Is Part Of: Security and privacy. Volume 1:Issue 1(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-12-04
• Subjects: Bat algorithm -- meta‐heuristic -- node localization -- optimization -- wireless sensor network; Computer security -- Periodicals; Data protection -- Periodicals; Cyberterrorism -- Periodicals; 005.8
• DOI: 10.1002/spy2.7
• Languages: English
• ISSNs: 2475-6725

Abstract

Abstract : Wireless sensor networks (WSNs) have recently been extensively investigated due to their numerous applications in processes that have to be spread over a large area. One of the important issues of WSN is node localization; node localization capability is highly desirable for the performance evaluation in monitoring applications. Localization is defined as estimating the locations of sensors with initially unknown location information as most sensors do not know their locations due to the cost and size of sensors. The main objective of localization is to find the nodes' positions in a short time with a low energy cost; for this reason, recent approaches relying on swarm intelligence techniques are utilized, and node localization is considered an optimization problem in a multidimensional space. Recently, the meta‐heuristic Bat algorithm was proposed as a solution for the node localization problem. This paper proposes an effective Bat algorithm for the node localization problem, the effectiveness of which is based on the adaptation of velocity of the Bats by hybridization, with Doppler effect for improving the performance, aptly termed Dopeffbat. Hence, Dopeffbat computes (through evolution) the nodes' positions iteratively through the Euclidian distance as fitness. Deploying this algorithm on a large WSN with hundreds of sensors demonstrates decent performance in terms of node localization. Moreover, the Dopeffbat parameters are simulated and interpreted in different scenarios of simulation; in addition, a comparative study was performed to further demonstrate the performance of the proposed algorithm, and the simulation results prove that Dopeffbat has a high convergence rate and greater precision compared with the original Bat algorithm and particle swarm optimization (PSO) algorithms.