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Mitigation of the ground reflection effect in real-time locating systems based on wireless sensor networks by using artificial neural networks

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Abstract

Wireless sensor networks (WSNs) have become much more relevant in recent years, mainly because they can be used in a wide diversity of applications. Real-time locating systems (RTLSs) are one of the most promising applications based on WSNs and represent a currently growing market. Specifically, WSNs are an ideal alternative to develop RTLSs aimed at indoor environments where existing global navigation satellite systems, such as the global positioning system, do not work correctly due to the blockage of the satellite signals. However, accuracy in indoor RTLSs is still a problem requiring novel solutions. One of the main challenges is to deal with the problems that arise from the effects of the propagation of radiofrequency waves, such as attenuation, diffraction, reflection and scattering. These effects can lead to other undesired problems, such as multipath. When the ground is responsible for wave reflections, multipath can be modeled as the ground reflection effect. This paper presents an innovative mathematical model for improving the accuracy of RTLSs, focusing on the mitigation of the ground reflection effect by using multilayer perceptron artificial neural networks.

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References

  1. Anand P, Siva Prasad BV, Venkateswarlu Ch (2009) Modeling and optimization of a pharmaceutical formulation system using radial basis function network. Int J Neural Syst 19(2): 127–136

    Article  Google Scholar 

  2. Barclay LW, Engineers IOE (2003) Propagation of radiowaves. IET, London

    Book  Google Scholar 

  3. Carnicer JM, García-Esnaola M (2002) Lagrange interpolation on conics and cubics. Comput Aided Geom Des 19: 313–326

    Article MATH  Google Scholar 

  4. Chong SK, Gaber MM, Krishnaswamy S, Loke SW (2011) Energy conservation in wireless sensor networks: a rule-based approach. Knowl Inf Syst 28(3): 579–614

    Article  Google Scholar 

  5. De Gloria A, Faraboschi P, Olivieri M (1993) Clustered Boltzmann Machines: Massively parallel architectures for constrained optimization problems. Parallel Comput 19(2): 163–175

    Article MATH  Google Scholar 

  6. De Paz JF, Bajo J, González A, Rodríguez S, Corchado JM (2010) Combining case-based reasoning systems and support vector regression to evaluate the atmosphere–ocean interaction. Knowl Inf Syst. doi:10.1007/s10115-010-0368-y

  7. Ding B, Chen L, Chen D, Yuan H (2008) Application of RTLS in warehouse management based on RFID and Wi-Fi. Wireless communications networking and mobile computing, 2008. WiCOM ’08. 4th international conference on 2008, pp 1–5

  8. Galati G, Gasbarra M, Magarò P, De Marco P, Menè L, Pici M (2006) New approaches to multilateration processing: analysis and field evaluation. Radar conference, 2006. EuRAD 2006. 3rd European, pp 116–119

  9. Goldsmith A (2005) Wireless communications. Cambridge University Press, Cambridge

    Google Scholar 

  10. Ilyas, M, Dorf, RC (eds) (2003) The handbook of ad hoc wireless networks. CRC Press Inc., Boca Raton

    Google Scholar 

  11. Kaemarungsi K, Krishnamurthy P (2004) Modeling of indoor positioning systems based on location fingerprinting. INFOCOM 2004. Twenty-third annual joint conference of the IEEE computer and communications societies. pp 1012–1022

  12. Kalogirou SA (1999) Applications of artificial neural networks in energy systems: a review. Energy Convers Manag 40(10): 1073–1087

    Article  Google Scholar 

  13. Katsuura H, Sprecher D (1994) Computational aspects of Kolmogorov’s superposition theorem. Neural Netw 7(3): 455–461

    Article MATH  Google Scholar 

  14. Kim ES, Kim JI, Kang IK, Park CG, Lee JG (2008) Simulation results of ranging performance in two-ray multipath model. Control, automation and systems, 2008. ICCAS 2008. International conference on 2008, pp 734–737

  15. Köknar-Tezel S, Latecki LJ (2011) Improving SVM classification on imbalanced time series data sets with ghost points. Knowl Inf Syst 28(1): 1–23

    Article  Google Scholar 

  16. Lecun Y, Bottou L, Orr GB and Müller KR (1998) Efficient BackProp. Lecture notes in computer science, Springer, Heidelberg, pp 5–50

  17. Lessmann S, Voß S (2009) A reference model for customer-centric data mining with support vector machines. Eur J Oper Res 199(2): 520–530

    Article MATH  Google Scholar 

  18. Li H, Liang Y, Xu Q (2009) Support vector machines and its applications in chemistry. Chemom Intell Lab Syst 95(2): 188–198

    Article  Google Scholar 

  19. Liang X-Z, Wang R-H, Cui L-H, Zhang J-L, Zhang M (2006) Some researches on trivariate Lagrange interpolation. J Comput Appl Math 195(1–2): 192–205

    Article MathSciNet MATH  Google Scholar 

  20. Liu H, Darabi H, Banerjee P, Liu J (2007) Survey of wireless indoor positioning techniques and systems. IEEE Trans Syst Man Cybern Part C Appl Rev 37(6): 1067–1080

    Article  Google Scholar 

  21. Lo S (2008) Web service quality control based on text mining using support vector machine. Expert Syst Appl 34(1): 603–610

    Article  Google Scholar 

  22. Mata A, Corchado JM (2009) Forecasting the probability of finding oil slicks using a CBR system. Expert Syst Appl 36(4): 8239–8246

    Article  Google Scholar 

  23. Narendra KS, Thathachar MAL (1974) Learning automata—a survey. IEEE Trans Syst Man Cybern SMC-4 4: 323–334

    Article MathSciNet MATH  Google Scholar 

  24. n-Core (2011) n-Core: a faster and easier way to create wireless sensor networks. Retrieved 1 Sept fromhttp://www.n-core.info

  25. Nerguizian C, Despins C, Affès S (2004) Indoor geolocation with received signal strength fingerprinting technique and neural networks. Telecommunications and networking—ICT 2004. Springer Berlin/ Heidelberg. pp 866–875

  26. Nguyen H, Chan C (2004) Multiple neural networks for a long term time series forecast. Neural Comput Appl 13(1): 90–98

    Article  Google Scholar 

  27. Ray JK, Cannon ME, Fenton PC (1999) Mitigation of static carrier-phase multipath effects using multiple closely spaced antennas. Navig Wash 46(3): 93–202

    Google Scholar 

  28. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088): 533–536

    Article  Google Scholar 

  29. Salcic Z, Chan E (2000) Mobile station positioning using GSM cellular phone and artificial neural networks. Wirel Pers Commun 14(3): 235–254

    Article  Google Scholar 

  30. Schmitz A, Wenig M (2006) The effect of the radio wave propagation model in mobile ad hoc networks. In: Proceedings of the 9th ACM international symposium on modeling analysis and simulation of wireless and mobile systems, Terromolinos, Spain, 2006, pp 61–67

  31. Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3): 199–222

    Article MathSciNet  Google Scholar 

  32. Stelios MA, Nick AD, Effie MT, Dimitris KM, Thomopoulos SCA (2008) An indoor localization platform for ambient assisted living using UWB. In: Proceedings of the 6th international conference on advances in mobile computing and multimedia, Linz, Austria, 2008, pp 178–182

  33. Tapia DI, De Paz JF, Rodríguez S, Bajo J, Corchado JM (2008) Multi-agent system for security control on industrial Environments. Int Trans Syst Sci Appl J 4(3): 222–226

    Google Scholar 

  34. Vapnik VN (1998) Statistical learning theory. Wiley-Interscience, New York

    MATH  Google Scholar 

  35. Xie JJ, Palmer R, Wild D (2005) Multipath mitigation technique in RF ranging. Electrical and Computer Engineering. Canadian Conference on 2005, pp 2139–2142

  36. Yang Q, Li X, Shi X (2008) Cellular automata for simulating land use changes based on support vector machines. Comput Geosci 34(6): 592–602

    Article  Google Scholar 

  37. Yazdi HS, Rowhanimanesh A, Modares H (2011) A general insight into the effect of neuron structure on classification. Knowl Inf Syst. doi:10.1007/s10115-011-0392-6

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Authors and Affiliations

  1. Department of Computer Science and Automation, Faculty of Computer Sciences, University of Salamanca, Plaza de la Merced, s/n, 37008, Salamanca, Spain

    Juan F. De Paz, Dante I. Tapia, Ricardo S. Alonso, Cristian I. Pinzón & Juan M. Corchado

  2. Faculty of Computer Sciences, Pontifical University of Salamanca, Compañía, 5, 37002, Salamanca, Spain

    Javier Bajo

Authors
  1. Juan F. De Paz

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  2. Dante I. Tapia

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  3. Ricardo S. Alonso

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  4. Cristian I. Pinzón

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  5. Javier Bajo

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  6. Juan M. Corchado

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Correspondence toJuan F. De Paz.

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De Paz, J.F., Tapia, D.I., Alonso, R.S.et al. Mitigation of the ground reflection effect in real-time locating systems based on wireless sensor networks by using artificial neural networks.Knowl Inf Syst34, 193–217 (2013). https://doi.org/10.1007/s10115-012-0479-8

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