IJMTES – MULTI-TARGET TRACKING IN MOBILITY SENSOR NETWORKS USING ABC ALGORITHM

INTERNATIONAL JOURNAL OF MODERN TRENDS IN ENGINEERING AND SCIENCE

Author’s Name : Alexander, B.Mathew

Volume 01 Issue o1  January 2014

ISSN no:  2348-3121

Page no: 5-9

Abstract— In Mobile Sensor Networks, it is important to manage the mobility of the nodes and optimized computation in order to improve the performance and lifetime of the network. Existing methods dealt about only Single Target tracking methods for these criteria. In this paper, Multi-Target Tracking system using Artificial Bee Colony (ABC) Optimization algorithm was proposed to meet these requirements. In this proposed method, current positions of the targets are estimated and the next positions are predicted. Estimation and Prediction are done by using Interval Analysis. In order to cover multi-target in an optimal way to minimize the total travelled distances by nodes, a better decisions regarding the positions where the sensor is able to move upon is done by optimization techniques. Then assigning, each mobile node one new location within the computed set using the ABC Optimization techniques. In Artificial Intelligence, Bee Colony is the only one approach which can be applied to multi-nodal Optimization problem. This technique uses an ABC Optimization algorithm which can be better suit for unconstrained problems. Simulation results on the well-known benchmark functions, shows the efficiency and effectiveness of the proposed algorithm.

Keywords— ABC; Artificial Intelligence; Interval Analysis; MSN;  Multi-nodal Optimization; Multi-target tracking

Reference

[1] Y.Zhai, S.Cheng and N.Kehtarnavaz, “An object based on multiple-model particle filtering with state partitioning,” IEEE Trans. Instrumental and Measurement, Vol.58(5), pp. 1797-1809,(2009).
[2] N.Ahmed, M.Rutten, T.Bessell, S.S.Kanhere, N.Gordon and Sanjay Jha, “Detection and Tracking Using Particle-Filter-Based WirelessSensor Networks,” IEEE Transactions on Mobile Computing, Vol. 9, Issue: pp. 1332-1345, Sept (2010).
[3] Bo Jiang and B.Ravindran, “Completely Distributed Particle Filters for Target Tracking in Sensor Networks,” IEEE International Parallel & Distributed Processing Symposium (IPDPS), pp. 334-344,(2011).
[4] J. Teng , H. Snoussi and C. Richard, “Decentralized variational filtering for target tracking in binary sensor networks”, IEEE Transactions on Mobile Computing, Vol.9, No.10, pp. 1465 -1477,(2010).
[5] Jing Teng, H.Snoussi, C.Richard and Rong Zhou, “Distributed
Variational Filtering for Simultaneous Sensor Localization and Target Tracking in Wireless Sensor Networks”, IEEE Transactions on Vehicular Technology, Vol. 61, Issue: 5, pp. 2305-2318, June(2012).
[6] A.Yadav, N.Naik, M.R.Ananthasayanam, A.Gaur and Y.N,Singh,”A constant gain Kalman filter approach to target tracking in wireless sensor networks”, 7th IEEE International Conference on Industrial and Information Systems (ICIIS), pp. 1-7,(2012).
[7] R. Soleimanzadeh, B.J. Farahani and M. Fathy, “Particle Swarm
Optimization (PSO) based deployment algorithms in hybrid sensor
networks”, International Journal of Computer Science and Network
Security, Vol. 10, pp. 167-171,(2010).
[8] Farah Mourad, Hicham Chehade and Hichem Snoussi, “Controlled Mobility Sensor Networks for Target Tracking Using Ant Col Optimization”, IEEE Transactions on Mobile Computing, Vol. 11, No. 8, pp. 1261-1274, August(2012).
[9] F.Abdallah, A.Gning and Bonnifait, “Box Particle Filtering for nonLinear State Estimation using Interval Analysis”, Automatica,
pp. 807-815,(2008).

 Full Pdf-click here

Scroll Up