Distributed Target Location in Wireless Sensors Network: An Approach Using FPGA and Artificial Neural Network

Leomário Silva  Machado; Dionne Cavalcante  Monteiro; Glaucio Haroldo Silva de  Carvalho; Mauro Rodrigo Larrat Frota e  Silva

doi:10.4236/wsn.2015.75005

Mauro Rodrigo Larrat Frota e Silva^*, Glaucio Haroldo Silva de Carvalho, Dionne Cavalcante Monteiro, Leomário Silva Machado

Abstract: This paper analyzes the implementation of an algorithm into a FPGA embedded and distributed target location method using the Received Signal Strength Indicator (RSSI). The objective is to show a method in which an embedded feedforward Artificial Neural Network (ANN) can estimate target location in a distributed fashion against anchor failure. We discuss the lack of FPGA implementation of equivalent methods and the benefits of using a robust platform. We introduce the description of the implementation and we explain the operation of the proposed method, followed by the calculated errors due to inherent Elliott function approximation and the discretization of decimal values used as free parameters in ANN. Furthermore, we show some target location estimation points in function of different numbers of anchor failures. Our contribution is to show that an FPGA embedded ANN implementation, with a few layers, can rapidly estimate target location in a distributed fashion and in presence of failures of anchor nodes considering accuracy, precision and execution time.

Keywords: Wireless Sensors Network, RSSI, FPGA, Artificial Neural Network, Distributed Localization Methods

Cite this paper: Silva, M. , Carvalho, G. , Monteiro, D. and Machado, L. (2015) Distributed Target Location in Wireless Sensors Network: An Approach Using FPGA and Artificial Neural Network. Wireless Sensor Network, 7, 35-42. doi: 10.4236/wsn.2015.75005.

References

[1] Alhmiedat, T., Salem, A.O.A. and Taleb, A.A. (2013) An Improved Decentralized Approach for Tracking Multiple Mobile Targets through Zigbee WSNs. International Journal of Wireless & Mobile Networks, 5, 61-76.
http://dx.doi.org/10.5121/ijwmn.2013.5305

[2] Liu, L.Q. (2012) A Parallel and Distributed Computing Platform for Neural Networks Using Wireless Sensor Networks. Ph.D. Thesis, The University of Toledo, Toledo.

[3] Din, A., Bona, B., Morrissette, J., Hussain, M., Violante, M. and Naseem, M.F. (2012) Embedded Low Power Controller for Autonomous Landing of UAV Using Artificial Neural Network. 10th International Conference on Frontiers of Information Technology (FIT), Islamabad, 17-19 December 2012, 196-203.

[4] Rahman, M.S., Park, Y. and Kim, K.-D. (2012) RSS-Based Indoor Localization Algorithm for Wireless Sensor Network Using Generalized Regression Neural Network. Arabian Journal for Science and Engineering, 37, 1043-1053.
http://dx.doi.org/10.1007/s13369-012-0218-1

[5] Kumar, S., Jeon, S.M. and Lee, S.R. (2014) Localization Estimation Using Artificial Intelligence Technique in Wireless Sensor Networks. The Journal of Korea Information and Communications Society, 39C, 820-827.
http://dx.doi.org/10.7840/kics.2014.39C.9.820

[6] Zheng, J. and Dehghani, A. (2012) Range-Free Localization in Wireless Sensor Networks with Neural Network Ensembles. Journal of Sensor and Actuator Networks, 1, 254-271.
http://dx.doi.org/10.3390/jsan1030254

[7] Bhardwaj, S. (2013) ANN for Node Localization in Wireless Sensor Network. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2, 1724-1731.

[8] Kumar, S. and Lee, S.-R. (2014) Localization with RSSI Values for Wireless Sensor Networks: An Artificial Neural Network Approach. International Electronic Conference on Sensors and Applications, 1 June 2014.
http://dx.doi.org/10.3390/ecsa-1-d007

[9] Sabto, N.A. and Mutib, K.A. (2013) Autonomous Mobile Robot Localization Based on RSSI. Journal of King Saud University—Computer and Information Sciences, 25, 137-143.
http://dx.doi.org/10.1016/j.jksuci.2012.10.001

[10] Saad, A.-M.H.Y. and Alhady, S.S.N. (2014) Embedded Neural Network for Distance Recognition Using Distance Sensor. 1st International Conference of Recent Trends in Information and Communication Technologies, Universiti Teknologi Malaysia, Johor, Malaysia, 182-191.

[11] Gogolák, L., Pletl, S. and Kukolj, D. (2011) Indoor Fingerprint Localization in WSN Environment Based on Neural Network. Proceedings of the IEEE 9th International Symposium on Intelligent Systems and Informatics (SISY), Subotica, 8-10 September 2011, 293-296.

[12] Chen, C.-S. (2012) Artificial Neural Network for Location Estimation in Wireless Communication Systems. Sensors, 12, 2798-2817.
http://dx.doi.org/10.3390/s120302798

[13] Neves, A., Fonseca, H.C. and Ralha, C.G. (2013) Location Agent: A Study Using Different Wireless Protocols for Indoor Localization. International Journal of Wireless Communications and Mobile Computing, 1, 1-6.

[14] Pajares, G. (2012) Sensors in Collaboration Increase Individual Potentialities. Sensors, 12, 4892-4896.
http://dx.doi.org/10.3390/s120404892

[15] Larrat, M., Machado, L. and Monteiro, D.C. (2013) Performance Evaluation of Lateration, KNN and Artificial Neural Networks Techniques Applied to Real Indoor and Outdoor Location in WSN. Journal of Communication and Computer, 12, 72-81.

[16] Csáji, B.C. (2011) Approximation with Artificial Neural Networks. M.Sc. Thesis, Faculty of Sciences, Eotvos Loránd University, Budapest.

[17] Altera (2013) DE0 Nano User Manual v1.9.

[18] Bishop, D. (2010) Fixed Point Package User’s Guide.

[19] Sibi, P., Allwyn Jones, S. and Siddarth, P. (2013) Analysis of Different Activation Functions Using Back Propagation Neural Networks. Journal of Theoretical and Applied Information Technology, 47, 1344.