Hybrid Evolutionary Techniques for Ultra Wide Band Sensor Network Localization
M. Jamuna Rani1, V. Geethalakshmi2, K. Sindhumitha3

1M. Jamuna Rani, Assistant Professor Sr.G, Department of ECE, Sona College of Technology, Salem (Tamil Nadu), India.
2V. Geethalakshmi, Assistant Professor, Department of ECE, Sona College of Technology, Salem (Tamil Nadu), India.
3K. Sindhumitha, PG Student, M.E Communication Systems, Sona College of Technology, Salem (Tamil Nadu), India.
Manuscript received on 24 April 2019 | Revised Manuscript received on 02 May 2019 | Manuscript Published on 08 May 2019 | PP: 464-467 | Volume-7 Issue-5S3 February 2019 | Retrieval Number: E11820275S19/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Localization of sensors is an important and an integral issue for wireless sensor networks control and operation. Definite self-localization competence is immensely desirable in a wireless sensor network. A prominent complication in distance oriented localization of wireless sensor network is whether a given sensor network is cognizable or not. This paper introduces an innovative and computationally proficient localization method for WSN that uses Tabu Search (TS) based global optimization on the results of Hill Climbing based local optimization for the location computation and optimization of sensor nodes. From the performance analysis of this integrated method it is prominent that in spite of memory demands, TS-based method has superior convergence characteristics compared to other earlier proposed WSN localization techniques. Also, trends in the recent years have shifted to the hybrid optimization methods i.e. optimization by hybridization of metaheuristics and other techniques. This greatly reduces localization error and computation time for large networks. In the proposed system hill climbing local search method is implemented along with tabu search algorithm for efficient localization.
Keywords: Localization, Tabu Search, Hill Climbing, Optimization, Meta-Heuristics.
Scope of the Article: Sensor Networks, Actuators for Internet of Things