Enhancement of Indoor Localization in WSN using PSO tuned EKF

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Author(s)

Ravichander Janapati 1,* ch. Balaswamy 2 K.Soundararajan 3

1. Department of ECE, SR Engineering College, Warangal, India

2. Department of ECE, QIS Engineering College Ongole, India

3. Department of ECE, TKR Engineering College, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2017.02.02

Received: 11 Apr. 2016 / Revised: 29 Jun. 2016 / Accepted: 15 Sep. 2016 / Published: 8 Feb. 2017

Index Terms

WSN, Kalman Filter, Extended Kalman Filter, Adaptive Extended Kalman Filter, Particle Swarm optimization, PSO assisted AKF, Localization

Abstract

In Wireless Sensor Networks, nodes are positioned arbitrarily and finding location of nodes is difficult. In this network, the nodes need to know their location is important for indoor applications. In this applications signals are affected by various factors such as noise, multipath, NLOS etc. This impact on inaccurate location information of node, which leads finding path to the destination node is difficult. Cooperative location based routing is alternative solution for finding better path. In this paper a solution is proposed for effective route in indoor application of WSN. The proposed solution uses Particle Swarm Optimization assisted Adaptive Extended Kalman Filter (PSO-AKF) for finding location of nodes. In this mechanism, finding accurate position of node impact on network performance such as minimization of delay, location error and also minimizes complexity.

Cite This Paper

Ravichander Janapati, ch. Balaswamy, K.Soundararajan,"Enhancement of Indoor Localization in WSN using PSO tuned EKF", International Journal of Intelligent Systems and Applications(IJISA), Vol.9, No.2, pp.10-17, 2017. DOI:10.5815/ijisa.2017.02.02

Reference

[1]Akyildiz, Ian F., et al. "Wireless sensor networks: a survey." Computer networks 38.4 (2002): 393-422.
[2]A. H. Sayed, A. Tarighat, and N. Khajehnouri,BNetwork-based wireless location:Challenges faced in developing techniques for accurate wireless location information,IEEE Signal Process. Magzine.
[3]R. Fontana, E. Richley, and J. Barney,BCommercialization of an ultra-wideband precision asset location system,[ in Proc.IEEE Int. Conf. Ultra-Wideband (ICUWB),Nov. 2003, pp. 369–373.
[4]J. Caffery and G. Stuber, BRadio location inurban CDMA microcells,[ in Proc. IEEE Int.Symp. Personal, IndoorMobile Radio Commun.,Toronto, ON, Canada, Sep. 1995, vol. 2,pp. 858–862.
[5]Sathyan, T., & Hedley, M. (2013). Fast and accurate cooperative tracking in wireless networks. Mobile Computing, IEEE Transactions on, 12(9), 1801-1813.
[6]Klonovs, J., Haque, M. A., Krueger, V., Nasrollahi, K., Andersen-Ranberg, K., Moeslund, T. B., & Spaich, E. G. (2016). Monitoring Technology. In Distributed Computing and Monitoring Technologies for Older Patients (pp. 49-84). Springer International Publishing.Chicago
[7]Seco-Granados, G., López-Salcedo, J. A., Jiménez-Baños, D., & López-Risueño, G. (2012). Challenges in indoor global navigation satellite systems: Unveiling its core features in signal processing. Signal Processing Magazine, IEEE, 29(2), 108-131.
[8]Tarrío, P., Cesana, M., & Redondi, A. (2013). Energy-accuracy trade-offs for hybrid localization using RSS and inertial measurements in wireless sensor networks. Ad Hoc Networks, 11(6), 1874-1889.
[9]Shen, J., Molisch, A. F., & Salmi, J. (2012). Accurate passive location estimation using TOA measurements. Wireless Communications, IEEE Transactions on, 11(6), 2182-2192.
[10]Wang, G., Li, Y., & Ansari, N. (2013). A semidefinite relaxation method for source localization using TDOA and FDOA measurements. Vehicular Technology, IEEE Transactions on, 62(2), 853-862.
[11]Wang, Y., & Ho, K. C. (2015). An Asymptotically Efficient Estimator in Closed-Form for 3-D AOA Localization Using a Sensor Network. Wireless Communications, IEEE Transactions on, 14(12), 6524-6535.
[12]Cota-Ruiz, J., Rosiles, J. G., Rivas-Perea, P., & Sifuentes, E. (2013). A distributed localization algorithm for wireless sensor networks based on the solutions of spatially-constrained local problems. Sensors Journal, IEEE, 13(6), 2181-2191.
[13]Cadger, F., Curran, K., Santos, J., & Moffett, S. (2013). A survey of geographical routing in wireless ad-hoc networks. Communications Surveys & Tutorials, IEEE, 15(2), 621-653.
[14]N. Bulusu, J. Heidemann, and D. Estrin, “GPS-less Low Cost Out Door Localization for Very Small Devices,”Tech. rep. 00729, Comp. Sci. Dept., USC, Apr. 2000.
[15]Cheng, Bo, et al. "An accurate GPS-based localization in wireless sensor networks: a GM-WLS method." Parallel Processing Workshops (ICPPW), 2011 40th International Conference on. IEEE, 2011.
[16]Mateos, Gonzalo, Ioannis D. Schizas, and Georgios B. Giannakis. "Performance analysis of the consensus-based distributed LMS algorithm." EURASIP Journal on Advances in Signal Processing 2009.1 (2009): 1-19.
[17]Cattivelli, Federico S., and Ali H. Sayed. "Diffusion LMS strategies for distributed estimation." Signal Processing, IEEE Transactions on 58.3 (2010): 1035-1048.
[18]Mateos, Gonzalo, Ioannis D. Schizas, and Georgios B. Giannakis. "Distributed recursive least-squares for consensus-based in-network adaptive estimation." Signal Processing, IEEE Transactions on 57.11 (2009): 4583-4588.
[19]Bertrand, Alexander, Marc Moonen, and Ali H. Sayed. "Diffusion bias-compensated RLS estimation over adaptive networks." Signal Processing, IEEE Transactions on 59.11 (2011): 5212-5224.
[20]Raghavendran, CH V., G. Naga Satish, and P. Suresh Varma. "Intelligent Routing Techniques for Mobile Ad hoc Networks using Swarm Intelligence."International Journal of Intelligent Systems and Applications 5.1 (2012): 81.
[21]G. Mallikarjuna Rao, Siva Prasad Nandyala, Ch.Satyanarayana,"Fast Visual Object Tracking Using Modified kalman and Particle Filtering Algorithms in the Presence of Occlusions", International Journal of Image, Graphics and Signal Processing (IJIGSP), Vol.6, No.10, pp.43-54, 2014.DOI: 10.5815/ijigsp.2014.10.06.
[22]Khan, M. W., Salman, N., Ali, A., Khan, A. M., & Kemp, A. H. (2015, December). A comparative study of target tracking with Kalman filter, extended Kalman filter and particle filter using received signal strength measurements. In Emerging Technologies (ICET), 2015 International Conference on (pp. 1-6). IEEE