Title: A clustering-based improved Grey-Markov target tracking algorithm in wireless sensor networks

Authors: Shaoming Guo; Jin Zheng; Naixue Xiong; Guojun Wang

Addresses: School of Information Science and Engineering, Central South University, Changsha 410083, China ' School of Information Science and Engineering, Central South University, Changsha 410083, China ' School of Computer Science, Colorado Technical University, Colorado Springs 80907-3812, USA ' School of Information Science and Engineering, Central South University, Changsha 410083, China

Abstract: Target position prediction in wireless sensor networks (WSN) has long been an important but difficult problem to be addressed. Owing to the dynamic motion of the target, the accuracy of prediction results is still far from what we expect in real applications. In order to solve this problem, this paper presents a clustering-based improved Grey-Markov target tracking (CIGMTT for short) algorithm to combine Markov process with a segmentation Grey model to adapt to the changes of target motion. The algorithm can greatly improve prediction accuracy by introducing new initial value and background value into the segmentation Grey model. After getting the predicted position, a clustering routing mechanism is used to transmit the tracking information. Our clustering routing mechanism is based on node distance to the predicted target position and node residual energy to dynamically construct the tracking cluster. In this way, sensor nodes can wake up in advance so that we can complete the target tracking with as few nodes as possible and maximise the network lifetime. The performance analysis and simulation results show that the proposed algorithm is energy-efficient and achieves a superior performance in tracking accuracy and tracking probability.

Keywords: wireless sensor networks; WSNs; clustering routing; Grey-Markov; target tracking; segmentation Grey prediction; target position prediction; target motion; network lifetime; simulation; energy efficiency; tracking accuracy; tracking probability.

DOI: 10.1504/IJCSE.2016.076936

International Journal of Computational Science and Engineering, 2016 Vol.12 No.4, pp.287 - 297

Received: 18 Feb 2013
Accepted: 06 Apr 2013
Published online: 08 Jun 2016 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article