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Wireless Personal Communications
Published in: Wireless Personal Communications 1/2017

26-04-2017

An Optimized Analysis of Localization Algorithm in Wireless Sensor Networks

Authors: Z. Mary Livinsa, S. Jayashri

Published in: Wireless Personal Communications | Issue 1/2017

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Abstract

Determining the position of the sensor node is the ultimate challenging one in wireless sensor networks (WSNs), which may perhaps lead a large localization error. To tackle this tricky, a novel Semi Definite Programming (SDP)—Multiplier method is proposed using time difference of arrival (TDOA) measurement in asynchronous networks. The SDP relaxation method is derived to prevaricate the optimal maximum likelihood (ML) convergence problem that will result in Lagrangian problem. Consequently, this problem can be optimally solved in a stress-free method which provides the value of multipliers. In this Relax and cut scheme, Lagrangian multiplier added in the objective function which gives the minimum value of the function that fulfills the relaxed constraints and reduce the execution time. Also to monitor the mobile target and to improve the energy of sensors, an optimization framework of selective approach algorithm has been used. The simulation result demonstrates that the proposed algorithm provides a better position estimation with less localization error and energy consumption.
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Literature
1.
Chenji, H., & Stoleru, R. (2013). Toward accurate mobile sensor network localization in noisy environments. IEEE Transactions on Mobile Computing, 12(6), 1094–1106.CrossRef
2.
Lim, C. B., Kang, S. H., Cho, H. H., Park, S. W., & Park, J. G. (2010, August). An Enhanced Indoor Localization algorithm Based on IEEE 802.11 WLAN using RSSI and multiple parameters. In: Fifth International Conference on Systems and Networks Communications (ICSNC), 2010 (pp. 238–242). IEEE.
3.
Paul, A. S., & Wan, E. A. (2009). RSSI-based indoor localization and tracking using sigma-point Kalman smoothers. IEEE Journal of Selected Topics in Signal Processing, 3(5), 860–873.CrossRef
4.
Patwari, N., Ash, J. N., Kyperountas, S., Hero, A. O., Moses, R. L., & Correal, N. S. (2005). Locating the nodes: Cooperative localization in wireless sensor networks. IEEE Signal Processing Magazine, 22(4), 54–69.CrossRef
5.
Xu, E., Ding, Z., & Dasgupta, S. (2011). Source localization in wireless sensor networks from signal time-of-arrival measurements. IEEE Transactions on Signal Processing, 59(6), 2887–2897.MathSciNetCrossRef
6.
Shen, J., Molisch, A. F., & Salmi, J. (2012). Accurate passive location estimation using TOA measurements. IEEE Transactions on Wireless Communications, 11(6), 2182–2192.CrossRef
7.
Meng, W., Xie, L., & Xiao, W. (2013). Decentralized TDOA sensor pairing in multihop wireless sensor networks. IEEE Signal Processing Letters, 20(2), 181–184.CrossRef
8.
Sayed, A. H., Tarighat, A., & Khajehnouri, N. (2005). Network-based wireless location: Challenges faced in developing techniques for accurate wireless location information. IEEE Signal Processing Magazine, 22(4), 24–40.CrossRef
9.
Yang, K., Wang, G., & Luo, Z. Q. (2009). Efficient convex relaxation methods for robust target localization by a sensor network using time differences of arrivals. IEEE Transactions on Signal Processing, 57(7), 2775–2784.MathSciNetCrossRef
10.
Zhu, Y., Huang, D., & Jiang, A. (2008, May). Network localization using angle of arrival. In: IEEE International Conference on Electro/Information Technology, 2008. EIT 2008. (pp. 205–210). IEEE.
11.
Tseng, P. (2007). Second-order cone programming relaxation of sensor network localization. SIAM Journal on Optimization, 18(1), 156–185.MathSciNetCrossRefMATH
12.
. Biswas, P., & Ye, Y. (2004, April). Semidefinite programming for ad hoc wireless sensor network localization. In: Proceedings of the 3rd international symposium on information processing in sensor networks (pp. 46–54). ACM.
13.
Bhatti, S., Xu, J., & Memon, M. (2011). Clustering and fault tolerance for target tracking using wireless sensor networks. IET Wireless Sensor Systems, 1(2), 66–73.CrossRef
14.
Meng, C., Ding, Z., & Dasgupta, S. (2008). A semidefinite programming approach to source localization in wireless sensor networks. IEEE Signal Processing Letters, 15, 253–256.CrossRef
15.
Xie, S., Wang, J., Hu, A., Gu, Y., & Xu, J. Positive semi-definite programming localization in wireless sensor networks.
16.
Yin, F., Fritsche, C., Gustafsson, F., & Zoubir, A. M. (2013). TOA-based robust wireless geolocation and Cramér-Rao lower bound analysis in harsh LOS/NLOS environments. IEEE Transactions on Signal Processing, 61(9), 2243–2255.CrossRef
17.
Vaghefi, R. M., & Buehrer, R. M. (2013, May). Asynchronous time-of-arrival-based source localization. In: ICASSP (pp. 4086–4090).
18.
Shrivastava, N., Mudumbai, R., Madhow, U., & Suri, S. (2009). Target tracking with binary proximity sensors. ACM Transactions on Sensor Networks (TOSN), 5(4), 30.CrossRef
19.
Li, S., Qin, Z., Shan, L., Zhang, R., & Yang, X. (2014). A survey on target tracking in well-deployed wireless sensor networks. Journal of Software, 9(5), 1255–1262.
20.
Tsai, H. W., Chu, C. P., & Chen, T. S. (2007). Mobile object tracking in wireless sensor networks. Computer Communications, 30(8), 1811–1825.CrossRef
21.
Ramya, K., Praveen Kumar, K., & Srinivas Rao, V. (2012). A survey on target tracking techniques in wireless sensor networks. International Journal of Computer Science and Engineering Survey, 3(4), 93–108.CrossRef
22.
Ashourian, M., Woo, S., & Jeong, H. (2007). Performance evaluation of blind source separation schemes for separating sensor signals in a distributed network. In: International Conference on Convergence Information Technology, IEEE.
23.
Shrivastava, N., Madhow, R. M. U., & Suri, S. (2006). Target tracking with binary proximity sensors: fundamental limits, minimal descriptions, and algorithms. In: Proceedings of the 4th International Conference on Embedded Networked Sensor Systems (pp. 251–264). ACM.
24.
Zhang, H., & Hou, J. C. (2005). Maintaining sensing coverage and connectivity in large sensor networks. Ad Hoc & Sensor Wireless Networks, 1(1–2), 89–124.
25.
Cardei, M., & Wu, J. (2006). Energy-efficient coverage problems in wireless ad-hoc sensor networks. Computer Communications, 29(4), 413–420.CrossRef
26.
Zou, Y., & Chakrabarty, K. (2004). Sensor deployment and target localization in distributed sensor networks. ACM Transactions on Embedded Computing Systems (TECS), 3(1), 61–91.CrossRef
27.
Alaybeyoglu, A., Dagdeviren, O., Erciyes, K., & Kantarci, A. (2009, September). Performance evaluation of cluster-based target tracking protocols for wireless sensor networks. In: 24th International Symposium on Computer and Information Sciences, 2009. ISCIS 2009. (pp. 357–362). IEEE.
28.
Demigha, O., Hidouci, W. K., & Ahmed, T. (2013). On energy efficiency in collaborative target tracking in wireless sensor network–a review. IEEE Communications Surveys & Tutorials, 15(3), 1210–1222.CrossRef
29.
. Zhu, Y., Vikram, A., & Fu, H. (2012). On tracking multiple indistinguishable targets. In: 9th International Conference on Mobile Adhoc and Sensor Systems (MASS), 2012 IEEE (pp. 1–9). IEEE.
30.
. Goldenberg, D. K., Bihler, P., Cao, M., Fang, J., Anderson, B., Morse, A. S., & Yang, Y. R. (2006). Localization in sparse networks using sweeps. In: Proceedings of the 12th Annual International Conference on Mobile Computing and Networking (pp. 110–121). ACM.
31.
Kay, S. M. (2013). Fundamentals of statistical signal processing: Practical algorithm development (Vol. 3). New Jersey: Pearson Education.
32.
Foy, W. H. (1976). Position-location solutions by Taylor-series estimation. IEEE Transactions on Aerospace and Electronic Systems, 12(2), 187–194.CrossRef
33.
. Biswas, P., & Ye, Y. (2004). Semidefinite programming for ad hoc wireless sensor network localization. In: Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks (pp. 46–54). ACM.
34.
Alfakih, A. Y., Khandani, A., & Wolkowicz, H. (1999). Solving Euclidean distance matrix completion problems via semi definite programming. Computational Optimization and Applications, 12(1–3), 13–30.MathSciNetMATH
35.
36.
Sturm, J. F. (1999). Using SeDuMi 1.02, a MATLAB toolbox for optimization over symmetric cones. Optimization Methods and Software, 11(1–4), 625–653.MathSciNetCrossRefMATH
37.
Zhu, Y., Vikram, A., & Fu, H. (2014). On topology of sensor networks deployed for multitarget tracking. IEEE Transactions on Intelligent Transportation Systems, 15(4), 1489–1498.CrossRef
Metadata
Title
An Optimized Analysis of Localization Algorithm in Wireless Sensor Networks
Authors
Z. Mary Livinsa
S. Jayashri
Publication date
26-04-2017
Publisher
Springer US
Published in
Wireless Personal Communications / Issue 1/2017
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-017-4247-7

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