AN ADAPTIVE LOCALIZATION SYSTEM USING PARTICLE SWARM OPTIMIZATION IN A CIRCULAR DISTRIBUTION FORM
DOI:
https://doi.org/10.11113/jt.v78.9743Keywords:
Indoor localization system, particle swarm optimization, Euclidean distanceAbstract
Tracking the user location in indoor environment becomes substantial issue in recent research High accuracy and fast convergence are very important issues for a good localization system. One of the techniques that are used in localization systems is particle swarm optimization (PSO). This technique is a stochastic optimization based on the movement and velocity of particles. In this paper, we introduce an algorithm using PSO for indoor localization system. The proposed algorithm uses PSO to generate several particles that have circular distribution around one access point (AP). The PSO generates particles where the distance from each particle to the AP is the same distance from the AP to the target. The particle which achieves correct distances (distances from each AP to target) is selected as the target. Four PSO variants, namely standard PSO (SPSO), linearly decreasing inertia weight PSO (LDIW PSO), self-organizing hierarchical PSO with time acceleration coefficients (HPSO-TVAC), and constriction factor PSO (CFPSO) are used to find the minimum distance error. The simulation results show the proposed method using HPSO-TVAC variant achieves very low distance error of 0.19 meter.
References
Al-Ahmadi, A. S. M., Omer, A. I. A. and Kamarudin, M. R., and Rahman, T. A. 2010. Multi-floor Indoor Positioning System Using Bayesian Graphical Models. Progress In Electromagnetics Research B. 25(2010): 241-259.
Alhammadi, A., Alias, M. Y., Tan, S. W. and Sapumohotti, C. 2013. Enhanced Indoor Localization System for Multi-Floor Environment using Clustering Techniques. 1st International Conference of Recent Trends in Information and Communication Technologies, Malaysia, 12-15 September 204. 37-47.
Li. M, Kwok. S. H and Gordon, H. 2010. Accurate Angle-of-Arrival Measurement Using Particle Swarm Optimization. Wireless Sensor Network. 2(5): 358-364.
Rady, S., Wagner,A. and Badreddin, E. 2010. Hierarchical Localization Using Entropy-Based Feature Map And Triangulation Techniques. 2010 IEEE International Conference on Systems Man and Cybernetics (SMC). 10-13 October 2010. 519-525.
Ding, G., Zhang, J., Zhang, L. and Tan, Z., 2013. Overview Of Received Signal Strength Based Fingerprinting Localization In Indoor Wireless LAN Environments. 2013 IEEE 5th International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications (MAPE). 29 October 2013. 160-164.
Alhammadi, A., Fazirulhiysam, M. Fadlee and S. Alraih. 2016. Effects of Different Types Of RSS Data On The System Accuracy Of Indoor Localization System. 2016 IEEE Region 10 Symposium (TENSYMP). 9-11 May 2016. 311-314.
Machaj, J. and Brida, P. 2012. Optimization Of Rank Based Fingerprinting Localization Algorithm. International Conference on Indoor Positioning and Indoor Navigation (IPIN). 13-15 November 2012. 1-7.
Kennedy, J. and Eberhart, R. 1995. Particle Swarm Optimization. Proc. IEEE International Conference on Neural Networks. 27 Nov.-1 Dec. 1995. 1942-1948.
Yan, W. and Nan, H. 2014. A Novel Particle Swarm Optimization Based Non-line of Sight Mobile Node Localization Algorithm. Journal of Computational Information Systems. 8759-87661.
Zhu, H. Ngah, S., Xu Y., Tanabe, Y. and Baba, T. 2008. A Random Time-varying Particle Swarm Optimization for Local Positioning Systems. International Journal of Computer Science and Network Security. 8)6(: 49-60.
Tsai, P., Lin, Chun., Chen C., and Wang J. 2013. A Scalable Localization Scheme using Particle Swarm Approach for Sensor Networks. 7th International Conference on Sensor Technologies and Applications, 25 -31 August 2013. 21-26.
Leng, M., Tay, W. P., Quek, T. Q. S. 2012. Cooperative And Distributed Localization For Wireless Sensor Networks In Multipath Environments. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 25-30 March 2012. 3125-3128.
Lin, T. N., Fang, S. H., Tseng, W. H., Lee, C. W. and Hsieh, J. W. 2014. A Group-Discrimination-Based Access Point Selection for WLAN Fingerprinting Localization. IEEE Transactions on Vehicular Technology. 63(8): 3967- 3976.
Chen, Y., Yang, Q., Yin J., and Chai X., 2006. Power-Efficient Access-Point Selection For Indoor Location Estimation. IEEE Transactions on Knowledge Data Engingeering. 18(7): 877-888.
Kushki, A., Plataniotis, K. N., and Venetsanopoulos, A. N., 2007. Kernelbased Positioning In Wireless Local Area Networks. IEEE Transactions Mobile Computing. 6(6): 689-705.
Vecchio, M., López-Valcarce, R. and Marcelloni, F. 2012. A Two-Objective Evolutionary Approach Based On Topological Constraints For Node Localization In Wireless Sensor Networks. Applied Soft Computing Journal. 12(7): 1891-1901.
Shokrian, M. and High, K. A. 2014. Application Of A Multi Objective Multi-Leader Particle Swarm Optimization Algorithm On NLP And MINLP Problems. Computers and Chemical Engineering. 60(2014): 57-75.
Zhang, E., Wu, Y., and Chen, Q. 2014. A Practical Approach For Solving Multi-Objective Reliability Redundancy Allocation Problems Using Extended Bare-Bones Particle Swarm Optimization. Journal of Reliability Engineering and System Safety. 127(2014): 65-76.
Duan, C., Wang, X., Shu, S., Jing, C., and Chang, H. 2014. Thermodynamic Design Of Stirling Engine Using Multi-Objective Particle Swarm Optimization Algorithm. Energy Conversion and Management. 84(204): 88-96.
Sun, Z., Tao, L., Wang, X., and Zhou, Z. 2014. Localization Algorithm in Wireless Sensor Networks Based on Multiobjective Particle Swarm Optimization. International Journal of Distributed Sensor Networks. 11(8): 1-9.
Yuhui, S. and Eberhart, R. C. 1999. Empirical Study Of Particle Swarm Optimization. Proceedings of the 1999 Congress on Evolutionary Computation. 6-9 July 1999. 1950.
Ratnaweera, A., Halgamuge, S., and Watson, H. C. 2004. Self-organizing Hierarchical Particle Swarm Optimizer With Time-Varying Acceleration Coefficients. IEEE Transactions on Evolutionary Computation. 8(3): 240-255.
Clerc, M. and Kennedy, J. 2002. The Particle Swarm - Explosion, Stability, And Convergence In A Multidimensional Complex Space. IEEE Transactions on Evolutionary Computation. 6(1): 58-73.
Downloads
Published
Issue
Section
License
Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.
