Localization in Wireless Sensor Networks

A fundamental problem in wireless sensor networks is localization - the determination of the geographical locations of sensors. Most existing localization algorithms were designed to work well either in networks of static sensors or networks in which all sensors are mobile. In this paper, we propose two localization algorithms, MSL and MSL*, that work well when any number of sensors are static or mobile. MSL and MSL* are range-free algorithms - they do not require that sensors are equipped with hardware to measure signal strengths, angles of arrival of signals or distances to other sensors. We present simulation results to demonstrate that MSL and MSL* outperform existing algorithms in terms of localization error in very different mobility conditions. MSL* outperforms MSL in most scenarios, but incurs a higher communication cost. MSL outperforms MSL* when there is significant irregularity in the radio range. We also point out some problems with a well known lower bound for the error in any range-free localization algorithm in static sensor networks.

[1]  Tarek F. Abdelzaher,et al.  Range-free localization schemes for large scale sensor networks , 2003, MobiCom '03.

[2]  Dave Thomas,et al.  Practice , 2004, IEEE Softw..

[3]  Andy Hopper,et al.  A new location technique for the active office , 1997, IEEE Wirel. Commun..

[4]  R. Scheaffer,et al.  Mathematical Statistics with Applications. , 1992 .

[5]  Miklós Maróti,et al.  Radio interferometric geolocation , 2005, SenSys '05.

[6]  Tracy Camp,et al.  A survey of mobility models for ad hoc network research , 2002, Wirel. Commun. Mob. Comput..

[7]  Ian F. Akyildiz,et al.  Wireless sensor networks , 2007 .

[8]  Radhika Nagpal,et al.  Organizing a Global Coordinate System from Local Information on an Ad Hoc Sensor Network , 2003, IPSN.

[9]  A. Ledeczi,et al.  Node-density independent localization , 2006, 2006 5th International Conference on Information Processing in Sensor Networks.

[10]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[11]  John Eccleston,et al.  Statistics and Computing , 2006 .

[12]  Hari Balakrishnan,et al.  6th ACM/IEEE International Conference on on Mobile Computing and Networking (ACM MOBICOM ’00) The Cricket Location-Support System , 2022 .

[13]  K. Ramachandran,et al.  Mathematical Statistics with Applications. , 1992 .

[14]  Emin Gün Sirer,et al.  Sextant: a unified node and event localization framework using non-convex constraints , 2005, MobiHoc '05.

[15]  Christopher Taylor,et al.  Localization in Sensor Networks , 2005, Handbook of Sensor Networks.

[16]  Holger Füßler,et al.  Effects of a realistic channel model on packet forwarding in vehicular ad hoc networks , 2006, IEEE Wireless Communications and Networking Conference, 2006. WCNC 2006..

[17]  Brad Karp,et al.  GPSR: greedy perimeter stateless routing for wireless networks , 2000, MobiCom '00.

[18]  Paul J. M. Havinga,et al.  Range-Based Localization in Mobile Sensor Networks , 2006, EWSN.

[19]  B. R. Badrinath,et al.  Ad hoc positioning system (APS) , 2001, GLOBECOM'01. IEEE Global Telecommunications Conference (Cat. No.01CH37270).

[20]  Bernhard Hofmann-Wellenhof,et al.  Global Positioning System , 1992 .

[21]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[22]  Gianluca Mazzini,et al.  Localization in sensor networks with fading and mobility , 2002, The 13th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications.

[23]  Mingyan Liu,et al.  Sound mobility models , 2003, MobiCom '03.

[24]  Urs Bischoff,et al.  Constraint-Based Distance Estimation in Ad-Hoc Wireless Sensor Networks , 2006, EWSN.

[25]  Suprakash Datta,et al.  Distributed localization in static and mobile sensor networks , 2006, 2006 IEEE International Conference on Wireless and Mobile Computing, Networking and Communications.

[26]  B. R. Badrinath,et al.  Ad hoc positioning system (APS) using AOA , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[27]  Mani B. Srivastava,et al.  Dynamic fine-grained localization in Ad-Hoc networks of sensors , 2001, MobiCom '01.

[28]  Wolfram Burgard,et al.  Monte Carlo Localization: Efficient Position Estimation for Mobile Robots , 1999, AAAI/IAAI.

[29]  B. Hofmann-Wellenhof,et al.  Global Positioning System , 1992 .

[30]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .

[31]  David Evans,et al.  Localization for mobile sensor networks , 2004, MobiCom '04.

[32]  Deborah Estrin,et al.  Localization in sensor networks , 2004 .

[33]  Ivan Stojmenovic,et al.  Handbook of Sensor Networks: Algorithms and Architectures , 2005, Handbook of Sensor Networks.

[34]  Vinay Kolar,et al.  Dynamic localization control for mobile sensor networks , 2005, PCCC 2005. 24th IEEE International Performance, Computing, and Communications Conference, 2005..

[35]  Yong Wang,et al.  Energy-efficient computing for wildlife tracking: design tradeoffs and early experiences with ZebraNet , 2002, ASPLOS X.

[36]  Thomas E. Nichols.  Tools for statistical inference in functional & structural brain imaging , 2009 .

[37]  G. Meek.  Mathematical statistics with applications , 1973 .