A comparison study on node clustering techniques used in target tracking WSNs for efficient data aggregation. Issue 16 (5th August 2016)

• Record Type: Journal Article
• Authors: Mahdi, Omar Adil; Abdul Wahab, Ainuddin Wahid; Idna Idris, Mohd. Yamani; Abu znaid, Ammar M. A.; Khan, Suleman; Al‐Mayouf, Yusor Rafid Bahar; Guizani, Nadra
• Is Part Of: Wireless communications and mobile computing. Volume 16:Issue 16(2016)
• Page Start: 2663
• Page End: 2676
• Publication Date: 2016-08-05
• Subjects: wireless sensor network -- target tracking -- in‐network processing -- data aggregation -- clustering -- energy consumption; Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1002/wcm.2715
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract: Wireless sensor applications are susceptible to energy constraints. Most of the energy is consumed in communication between wireless nodes. Clustering and data aggregation are the two widely used strategies for reducing energy usage and increasing the lifetime of wireless sensor networks. In target tracking applications, large amount of redundant data is produced regularly. Hence, deployment of effective data aggregation schemes is vital to eliminate data redundancy. This work aims to conduct a comparative study of various research approaches that employ clustering techniques for efficiently aggregating data in target tracking applications as selection of an appropriate clustering algorithm may reflect positive results in the data aggregation process. In this paper, we have highlighted the gains of the existing schemes for node clustering‐based data aggregation along with a detailed discussion on their advantages and issues that may degrade the performance. Also, the boundary issues in each type of clustering technique have been analyzed. Simulation results reveal that the efficacy and validity of these clustering‐based data aggregation algorithms are limited to specific sensing situations only, while failing to exhibit adaptive behavior in various other environmental conditions. Copyright © 2016 John Wiley & Sons, Ltd. Abstract : In this work, a latest comparative review is presented on the clustering techniques employed for efficient data aggregation in target tracking applications along with their gains and issues. Simulation results reveal that the efficacy and validity of these clustering‐based data aggregation algorithms are limited to specific sensing situations only, while failing to exhibit adaptive behavior in other various environmental conditions.