**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**30127

##### Comparative Analysis of Sigmoidal Feedforward Artificial Neural Networks and Radial Basis Function Networks Approach for Localization in Wireless Sensor Networks

**Authors:**
Ashish Payal,
C. S. Rai,
B. V. R. Reddy

**Abstract:**

With the increasing use and application of Wireless Sensor Networks (WSN), need has arisen to explore them in more effective and efficient manner. An important area which can bring efficiency to WSNs is the localization process, which refers to the estimation of the position of wireless sensor nodes in an ad hoc network setting, in reference to a coordinate system that may be internal or external to the network. In this paper, we have done comparison and analysed Sigmoidal Feedforward Artificial Neural Networks (SFFANNs) and Radial Basis Function (RBF) networks for developing localization framework in WSNs. The presented work utilizes the Received Signal Strength Indicator (RSSI), measured by static node on 100 x 100 m^{2} grid from three anchor nodes. The comprehensive evaluation of these approaches is done using MATLAB software. The simulation results effectively demonstrate that FFANNs based sensor motes will show better localization accuracy as compared to RBF.

**Keywords:**
Localization,
wireless sensor networks,
artificial neural network,
radial basis function,
multi-layer perceptron,
backpropagation,
RSSI.

**Digital Object Identifier (DOI):**
doi.org/10.5281/zenodo.1125123

**References:**

[1] I.F Akyildiz, W. Su, and Y. Sankarasubramaniam, “A Survey on Sensor Networks”, IEEE Communications Magazine, 2002, pp.102-114.

[2] M. Castillo-effen, D.H. Quintela, R. Jordan, W. Westhoff, and W. Moreno, "Wireless Sensor Networks for Flash-Flood Alerting," Computer Engineering, 2004, pp. 142-146.

[3] T. Gao, D. Greenspan, M. Welsh, R.R. Juang, and A. Alm, "Vital Signs Monitoring and Patient Tracking Over a Wireless Network," Engineering In Medicine And Biology, 2005, pp. 102-105.

[4] S. Kumar, "Sensor networks: Evolution, opportunities, and challenges," Proceedings of the IEEE, vol. 91, 2003, pp. 1247-1256.

[5] B. Placzek, “Uncertainty-dependent data collection in vehicular sensor networks,” in Computer Networks, Communications in Computer and Information Science, vol. 291, pp. 430–439, Springer, Berlin, Germany, 2012.

[6] E. D. Manley, H. A. Nahas, J. S. Deogun, and U. States, “Localization and Tracking in Sensor Systems,” Proceedings of the IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC’06), 2006,pp. 237-242.

[7] A. Smith, H. Balakrishnan, M. Goraczko, and N. Priyantha, “Tracking Moving Devices with the Cricket Location System,” Proceedings of the Second International Conference on Mobile Systems, Applications and Services, 2004, pp. 190–202.

[8] B. Płaczek and M. Berna´s, “Uncertainty-based information extraction in wireless sensor networks for control applications,”Ad Hoc Networks, vol. 14, pp. 106–117, 2014.

[9] A. Payal, C. S. Rai, and B. V. R. Reddy, “Soft computing approach towards localisation in wireless sensor networks: a survey,” Int. J. Inf. Technol. Commun. Converg., vol. 2, no. 4, pp. 353–367, 2013.

[10] Savvides, A., H. Park and M. B. Srivastava, “The bits and flops of the n-hop multi- lateration primitive for node localization problems”, Proceedings of the First ACM International Workshop on Wireless Sensor Networks and Applications (WSNA-02), pp. 112–121

[11] Y. Shang,W. Ruml, and Y. Zhang, M. P. J. Fromherz, “Localization from mere connectivity”, Proceedings of ACM MobiHoc, 2003,pp. 201-212.

[12] L. Doherty, K. Pister, and L. Ghaoui,” Convex position estimation in wireless sensor networks”. Proceedings of IEEE Infocom, 2001, pp. 1655-1663

[13] D. Niculescu and B. Nath. ,”Ad hoc positioning system (APS)”, Proceedings of IEEE Global Telecommunications Conference, 2001, pp. 2926-2931.

[14] T. He, C. Huang, B.M. Blum, J.a. Stankovic, and T. Abdelzaher, "Range-free localization schemes for large scale sensor networks," Proceedings of the 9th annual international conference on Mobile computing and networking - MobiCom '03, 2003, pp. 81-95.

[15] P.-J. Chuang and Y.-J. Jiang, “Effective neural network-based node localisation scheme for wireless sensor networks,” IET Wireless Sensor Systems, vol. 4, no. 2, pp. 97–103, 2014

[16] U Ahmad, A. Gavrilov, U. Nasir, and M. Iqbal, “In-building Localization using Neural Networks” Proceedings IEEE International Conference on Engineering of Intelligent Systems, 2006, pp. 1-6.

[17] A. Payal, C. S. Rai, and B. V. R. Reddy, “Artificial Neural Networks for developing localization framework in Wireless Sensor Networks,” Data Min. Intell. Comput. (ICDMIC), 2014 Int. Conf., pp. 1–6, 2014.

[18] S. Yun, J. Lee, W. Chung, E. Kim, and S. Kim, "A soft computing approach to localization in wireless sensor networks," Expert Systems with Applications, vol. 36, 2009, pp. 7552-7561.

[19] A. Payal, C. S. Rai, and B. V. R. Reddy, “Analysis of some feedforward artificial neural network training algorithms for developing localization framework in wireless sensor networks,”Wireless Personal Communications, vol. 82, no. 4, pp. 2519–2536, 2015.

[20] G. Cybenko, Approximation by superposition of a sigmoidal function, Math. Control, Signal Systems 2(4) (1989) 303–314.

[21] K. Funahashi, On the approximate realization of continuous mappings by neural networks, Neural Networks 2 (3) (1989) 183–192

[22] K. Hornik, M. Stinchcombe, H. White, Multilayer feedforward networks are universal approximators, Neural Networks 2 (3) (1989) 359–366

[23] T.Y. Chen, C.C. Chiu, T.C. Tu, “Mixing and Combining with AOA and TOA for Enhanced Accuracy of Mobile Location”, Proceedings Personal Mobile Communications Conference, 2003, pp. 276-280

[24] W.C. jakes, Microwave Mobile Communications, IEEE Press,1994

[25] P. Bahl and V.N. Padmanabhan, “RADAR: An In-building RF-Based User Location and Tracking System”, In Proceedings of the IEEE INFOCOM ’00,2000,pp. 775-784

[26] S. Haykin, Neural Networks, 3rd ed., Prentice Hall, 2011

[27] T. M. Cover, “Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition”, IEEE Transactions on Electronic Computers, Vol. EC-14, pp. 326-334.

[28] Y. Zhu and A. Shareef, “Comparisons of Three Kalman Filter Tracking Algorithms in Sensor Network.”, International Workshop on Networking, Architecture, and Storages, 2006, pp.61-62.

[29] A. Shareef, Y. Zhu, and M. Musavi, “Localization Using Neural Networks in Wireless Sensor Networks,” Proceedings of MOBILWARE, 2008.

[30] A. Shareef, Y. Zhu, M. Musavi, and B. Shen, “Comparison Of MLP Neural Network And Kalman Filter For Localization In Wireless Sensor Networks,” Proceedings of the 19th IASTED International conference on parallel and distributed computing and systems, 2007, pp. 323-330.

[31] MATLAB 7.1. The Math Works, 1995.

[32] T. S. Rappaport, Wireless Communications, Principles and Practice, 2nd ed., Prentice-Hall, Upper Saddle River, NJ, 2002, p. 145.