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Analysis of Some Feedforward Artificial Neural Network Training Algorithms for Developing Localization Framework in Wireless Sensor Networks

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Abstract

Wireless sensor networks (WSNs) have gained global attention of both, the research community and various application users. Localisation in WSN plays a crucial role in implementing myriad of applications such as healthcare management, disaster management, environment management, and agriculture management. Localization algorithms have become an essential requirement to enhance the effectiveness of WSNs demonstrating relative estimation of sensor node position of anchor nodes with their absolute coordinates. We have done a comprehensive performance evaluation of some feedforward artificial neural networks (FFANNs) training algorithms for developing efficient localization framework in WSNs. The proposed work utilizes the received signal strength observed by anchor nodes by means of some multi-path propagation effects. This paper aims for best training algorithm output while comparing results of different training algorithms. The FFANNs is designed with 3-dimensional inputs and one hidden layer with variable neurons and two outputs. For hidden layer tansigmoid transfer function while for output layer linear transfer function is used. The best training algorithm of FFANNs based model can provide better position accuracy and precision for the future applications. We have analysed and proposed the usage of training algorithms that improves the accuracy and precision of localization algorithms. The simulation results demonstrate the effectiveness and huge potential in optimizing hardware for localization module in sensor nodes.

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References

  1. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.

  2. Castillo-effen, M., Quintela, D. H., Jordan, R., Westhoff, W., & Moreno, W. (2004). Wireless sensor networks for flash-flood alerting. In Proceedings of the IEEE devices, circuits and systems (pp. 142–146).

  3. Gao, T., Greenspan, D., Welsh, M., Juang, R. R., & Alm, A. (2005). Vital signs monitoring and patient tracking over a wireless network. In Proceedings of the IEEE-EMBS (pp. 102–105).

  4. Kumar, S. (2003). Sensor networks: Evolution, opportunities, and challenges. In Proceedings of the IEEE (pp. 1247–1256).

  5. Manley, E. D., Nahas, H. A., Deogun, J. S., & States, U. (2006). Localization and tracking in sensor systems. In Proceedings of the IEEE international conference on sensor networks, ubiquitous, and trustworthy computing (pp. 237–242).

  6. Smith, A., Balakrishnan, H., Goraczko, M., & Priyantha, N. (2004). Tracking moving devices with the cricket location system. In Proceedings of the international conference on mobile systems, applications and services (pp. 190–202).

  7. Savvides, A., Park, H., & Srivastava, M. B. (2002). The bits and flops of the n-hop multi- lateration primitive for node localization problems. In Proceedings of the ACM international workshop on wireless sensor networks and applications (WSNA-02) (pp. 112–121).

  8. Niculescu, D., & Nath, B. (2001). Ad hoc positioning system (APS). In Proceedings of the IEEE global telecommunications conference (pp. 2926–2931).

  9. Shang, Y., Ruml, W., & Zhang, Y. (2003). Localization from mere connectivity. In Proceedings of the ACM international symposium on mobile ad hoc networking and computing (pp. 201–212).

  10. Shang, Y., & Ruml, W. (2004). Improved MDS-based localization. In Proceedings of the IEEE INFOCOM (pp. 2640–2651).

  11. Doherty, L., Pister, K., & Ghaoui, L. (2001). Convex position estimation in wireless sensor networks. In Proceedings of the IEEE INFOCOM (pp. 1655–1663).

  12. Enge, P., & Misra, P. (1999). Special Issue on global positioning system. In Proceedings of the IEEE (pp. 3–15).

  13. Ahmad, U., Gavrilov, A., Nasir, U., & Iqbal, M. (2006). In-building localization using neural networks. In Proceedings of the IEEE international conference on engineering of intelligent systems (pp. 1–6).

  14. Lee, K. H., Yu, C. H., Choi, J. W., & Seo, Y. B. (2008). ToA based sensor localization in underwater wireless sensor networks. In Proceedings of the SICE annual conference (pp. 1357–1361).

  15. He, T., Huang, C., Blum, B. M., Stankovic, J. A., & Abdelzaher, T. (2003). Range-free localization schemes for large scale sensor networks. In Proceedings of the international conference on mobile computing and networking (pp. 81–95).

  16. Jakes, W. C. (1974). Microwave mobile communications. New York: Wiley.

  17. Chen, T. Y., Chiu, C. C., & Tu, T. C. (2003). Mixing and combining with AOA and TOA for enhanced accuracy of mobile location. In Proceedings of the personal mobile communications conference (pp. 276–280).

  18. Kaune, R., Horst, J., & Koch, W. (2011). Accuracy analysis for TDOA localization in sensor networks, In Proceedings of the IEEE FUSION (pp. 1–8).

  19. Gustafsson, F., & Gunnarsson, F. (2003). Positioning using time- Difference of arrival measurements, In Proceedings of the acoustics, speech, and signal processing (pp. VI-553–556).

  20. Cong, L., & Zhuang, W. (2002). Hybrid TDOA/AOA mobile user location for wideband CDMA cellular systems. IEEE Transactions on Wireless Communications, 1(3), 439–447.

    Article  Google Scholar 

  21. Deligiannis, N., Louvros, S., & Kotsopoulos, S. (2007). Optimizing location positioning using hybrid TOA-AOA techniques in mobile cellular networks. In Proceedings of the mobile multimedia communications.

  22. Peng, R., & Sichitiu, M. L. (2006). Angle of arrival localization for wireless sensor networks. In Proceedings of the IEEE SECON (pp. 374–382).

  23. Niculescu, D. (2003). Ad hoc positioning system (APS) using AOA. In Proceedings of the IEEE INFOCOM (pp. 1734–1743).

  24. Patwari, N., & Hero, A. O. (2002). Location estimation accuracy in wireless sensor networks. In Proceedings of the signals, systems and computers (pp. 1523–1527).

  25. Benkič, K., Malajner, M., Planinšič, P., & Čučej, Ž. (2008). Using RSSI value for distance estimation in wireless sensor networks based on ZigBee. In Proceedings of the systems, signals and image processing IWSSIP (pp. 303–306).

  26. Bulusu, N., Heidemann, J., & Estrin, D. (2000). GPS-less low cost outdoor localization for very small devices. IEEE Personal Communication Magazine, 7(5), 28–34.

    Article  Google Scholar 

  27. He, T., Huang, C., Blum, B. M., Stankovic, J. A., & Abdelzaher, T. (2005). Range-free localization and its impact on large scale sensor networks. ACM Transactions on Embedded Computing Systems, 4(4), 877–906.

    Article  Google Scholar 

  28. Nagpal, R., Shrobe, H., & Bachrach, J. (2003). Organizing a global coordinate system from local information on an Ad Hoc sensor network. In Proceedings of the international conference on information processing in sensor networks (pp. 333–348).

  29. Shareef, A., Zhu, Y., Musavi, M., & Shen, B. (2007). Comparison of MLP neural network and Kalman filter for localization in wireless sensor networks. In Proceedings of the IASTED (pp. 323–330).

  30. Yun, S., Lee, J., Chung, W., Kim, E., & Kim, S. (2009). A soft computing approach to localization in wireless sensor networks. Expert Systems with Applications, 36(4), 7552–7561.

    Article  Google Scholar 

  31. Tian, J., & Shi, H. (2007). Study of localization scheme base on neural network for wireless sensor networks. In Proceedings of the IET conference on wireless, mobile and sensor networks (pp. 64–67).

  32. Want, R., Hopper, A., Falco, V., & Gibbons, J. (1992). The active badge location system. ACM Transactions on Information Systems, 10(1), 91–102.

    Article  Google Scholar 

  33. Bahl, P., & Padmanabhan, V. N. (2000). RADAR: an in building RF-based user location and tracking system. In Proceedings of the IEEE INFOCOM (pp. 775–784).

  34. Priyantha, N. B., Chakraborty, A. & Balakrishnan, H. (2000). The cricket location-support system. In Proceedings of the mobiCom (pp. 32–43).

  35. Harter, A., & Hopper, A. (1997). A new location technique for the active office. IEEE Personal Communications, 4(5), 42–47.

    Article  Google Scholar 

  36. Hightower, J., & Borriello, G. (2004). Particle filters for location estimation in ubiquitious computing: A case study. In Proceedings of the international conference on ubiquitious computing (UBICOMP) (pp. 88–106).

  37. Ladd, A. M., Bekris, K. E., Marceau, G., Rudys, A., Wallach, D. S., & Kavraki L. E. (2002). Using wireless ethernet for localization. In Proceedings of the IEEE/RSJ international conference on intelligent robots and system (pp. 402–408).

  38. Thrun, S. (2000). Probabilistic algorithms in robotics. Ai Magazine, 21(4), 93–109.

    Google Scholar 

  39. Moore, D., Leonard, J., Rus, D., & Teller S. (2004). Robust distributed network localization with noisy range measurements. In Proceedings of the international conference on embedded networked sensor systems sensys (pp. 50–61).

  40. Shen, S., Member, S., Xia, C., & Goddard, S. (2007). Comparison of three Kalman filters for an indoor passive tracking system. In Proceedings of the IEEE EIT (pp. 284–289).

  41. Zhu, Y. & Shareef, A. (2006). Comparisons of three Kalman filter tracking algorithms in sensor network. In Proceedings of international workshop on networking, architecture, and storages (pp. 2–3).

  42. Wu, B., Jen, C., & Chang, K. (2007). Neural fuzzy based indoor localization by Kalman filtering with propagation channel modeling. In Proceedings of the IEEE international conference on systems, man and cybernetics (pp. 812–817).

  43. Battiti, R., Nhat, T. L., & Villani, A. (2002). Location-aware computing: A neural network model for determining location in wireless LANs, Technical Report DIT-02-0083, University of Trento, Department of Information and Communication Technology.

  44. Oldewurtel, F., & Mahonen, P. (2006). Neural wireless sensor networks. In proceedings of international conference on systems and networks communication (pp. 28–28).

  45. Cybenko, G. (1989). Approximation by superposition of a sigmoidal function. Mathematics of Control, Signals and Systems, 2(4), 303–314.

    Article  MATH  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  48. MATLAB 7.1. (1995). The math works.

  49. Haykin S. (2011). Neural networks and learning machines, 3rd ed., PHI.

  50. Hagan, M. T., Demuth, H. B., & Beale, M. (1996). Neural network design. Boston: PWS publishing company.

    Google Scholar 

  51. Rumelhart, D. E., McClelland, J. L., & the PDP Research Group, (Eds.) (1986). Parallel distributed processing, vols. 1 and 2, Cambridge, MA: MIT Press.

  52. Battiti, R. (1992). First and second-order methods for learning: Between steepest descent and Newton’s method. Neural Computation, 4(2), 141–166.

    Article  Google Scholar 

  53. Riedmiller, M., & Braun, H. (1993). A direct adaptive method for faster backpropagation learning: The RPROP algorithm. In Proceedings of the IEEE international conference on neural networks (pp. 586–591).

  54. Hagan, M. T., & Menhaj, M. (1994). Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5(6), 989–993.

    Article  Google Scholar 

  55. Battiti, R., & Masulli, E. (1990). BFGS Optimization for faster and automated supervised learning. In Proceedings of the international neural network conference (pp. 757–760).

  56. Foresee, F. D., & Hagan, M. T. (1997). Gauss-Newton approximation to bayesian learning. In Proceedings of the international joint conference on neural networks (pp. 1930–1935).

  57. Mackay, D. J. C. (1992). A practical Bayesian framework for backpropagation networks. Neural Computation, 4(3), 448–472.

    Article  Google Scholar 

  58. Powell, M. J. D. (1997). Restart procedures for the conjugate gradient method. Mathematical Programming, 12(1), 241–254.

    Article  Google Scholar 

  59. Fletcher, R., & Reeves, C. M. (1964). Function minimization by conjugate gradients. Computer Journal, 7(2), 149–154.

    Article  MATH  MathSciNet  Google Scholar 

  60. Moller, M. F. (1993). A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks, 6(4), 525–533.

    Article  Google Scholar 

  61. Rappaport, T. S. (2002). Wireless Communications: Principles and Practice. Upper Saddle River, NJ: Prentice Hall.

    Google Scholar 

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Payal, A., Rai, C.S. & Reddy, B.V.R. Analysis of Some Feedforward Artificial Neural Network Training Algorithms for Developing Localization Framework in Wireless Sensor Networks. Wireless Pers Commun 82, 2519–2536 (2015). https://doi.org/10.1007/s11277-015-2362-x

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