Abstract
The vivid success of the emerging wireless sensor technology (WSN) gave rise to the notion of localization in the communications field. Indeed, the interest in localization grew further with the proliferation of the wireless sensor network applications including medicine, military as well as transport. By utilizing a subset of sensor terminals, gathered data in a WSN can be both identified and correlated which helps in managing the nodes distributed throughout the network. In most scenarios presented in the literature, the nodes to be localized are often considered static. However, as we are heading towards the 5th generation mobile communication, the aspect of mobility should be regarded. Thus, the novelty of this research relies in its ability to merge the robotics as well as WSN fields creating a state of art for the localization of moving nodes. The challenging aspect relies in the capability of merging these two platforms in a way where the limitations of each is minimized as much as possible. A hybrid technique which combines both the Particle Filter (PF) method and the Time Difference of Arrival Technique (TDOA) is presented. Simulation results indicate that the proposed approach outperforms other techniques in terms of accuracy and robustness.
References
Boukerche A, Oliveira H, Nakamura E, Loureiro A (2007) Localization systems for wireless sensor networks. Wirel Commun, IEEE 14(6):6–12
Ilyas M, Mahgoub I, Kelly L (2004) Handbook of sensor networks: compact wireless and wired sensing systems. CRC Press Inc, Boca Raton, FL, USA
Mirebrahim H, Dehghan M (2009) Monte carlo localization of mobile sensor networks using the position information of neighbor nodes, in ad-hoc, mobile and wireless networks. Springer, pp 270–283
Sathyan T, Hedley M (2009) Evaluation of algorithms for cooperative localization in wireless sensor networks. In: Personal, indoor and mobile radio communications, 2009 IEEE 20th international symposium on, Sept 2009, pp 1898–1902
Hightower J, Borriello G (2001) Location systems for ubiquitous computing. Computer 8:57–66
Wang J, Ghosh RK, Das SK (2010) A survey on sensor localization. J Control Theory Appl 8(1):2–11
Najibi M (2013) Localization algorithms in a wireless sensor network using distance and angular data, Ph.D. dissertation, Applied Sciences: School of Engineering Science
Zhang S, Cao J, Li-Jun C, Chen D (2010) Accurate and energy-efficient range-free localization for mobile sensor networks. Mobile Comput, IEEE Trans 9(6):897–910
Dellaert F, Fox D, Burgard W, Thrun S (1999) Monte carlo localization for mobile robots. In: Proceedings 1999 IEEE international conference on robotics and automation, vol. 2. IEEE, pp 1322–1328
Handschin J (1970) Monte carlo techniques for prediction and filtering of non-linear stochastic processes. Automatica 6(4):555–563
Rui Y, Chen Y (2001) Better proposal distributions: object tracking using unscented particle filter. In: Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition 2001. CVPR 2001, vol 2. IEEE, pp II–786
So HC (2011) Source localization: algorithms and analysis. Handb Position Location: Theory, Pract, Adv pp 25–66
Laaraiedh M (2010) Contributions on hybrid localization techniques for heterogeneous wire-less networks, Ph.D. dissertation, Universit´ Rennes 1
So HC, Chan YT, Chan FKW (2008) Closed-form formulae for time-difference-of-arrival estimation. IEEE Trans Signal Process 56(6):2614–2620
Boudhir AA, Mohamed B, Mohamed BA (2010) New technique of wireless sensor networks localization based on energy consumption. Int J Comput Appl 9(12):25–28
Cheung KW, So HC, Ma WK, Chan YT (2006) A constrained least squares approach to mobile positioning: algorithms and optimality. EURASIP J Appl Sig Process, vol 2006, pp 150–150
Zhou F, Qin Z, Xiao C, Li S, Jiang W, Wu Y (2011) Tracking moving object via unscented particle filter in sensor network. Int J Digit Content Technol Appl 5(12)
Athans M, Wishner R, Bertolini A (1968) Suboptimal state estimation for continuous-time nonlinear systems from discrete noisy measurements. Autom Control, IEEE Trans 13(5):504–514
Wan E, Van Der Merwe R (2000) The unscented kalman filter for nonlinear estimation. In: Adaptive systems for signal processing, communications, and control symposium 2000. AS-SPCC. The IEEE 2000, pp 153–158
Arulampalam M, Maskell S, Gordon N, Clapp T (2002) A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. Sig Process, IEEE Trans 50(2):174–188
Ko NY, Kim TG, Moon YS (2012) Particle filter approach for localization of an underwater robot using time difference of arrival. In: OCEANS, 2012—Yeosu, May 2012, pp 1–7
Bordoy J, Hornecker P, Hoflinger F, Wendeberg J, Zhang R, Schindelhauer C, Reindl L (2013) Robust tracking of a mobile receiver using unsynchronized time differences of arrival. In: (IPIN) 2013 International Conference on indoor positioning and indoor navigation, Oct 2013, pp 1–10
Djuric P, Kotecha JH, Zhang J, Huang Y, Ghirmai T, Bugallo M, Miguez J (2003) Particle filtering. Sig Process Mag IEEE 20(5):19–38
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Elayan, H., Shubair, R.M. (2018). Towards an Intelligent Deployment of Wireless Sensor Networks. In: Ismail, L., Zhang, L. (eds) Information Innovation Technology in Smart Cities. Springer, Singapore. https://doi.org/10.1007/978-981-10-1741-4_16
Download citation
DOI: https://doi.org/10.1007/978-981-10-1741-4_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-1740-7
Online ISBN: 978-981-10-1741-4
eBook Packages: EngineeringEngineering (R0)