Improving Monte Carlo localization algorithm using time series forecasting method and dynamic sampling in mobile WSNs

Localization of sensor nodes is one of the important operations in wireless sensor networks. Because the data produced by sensor nodes should also provide geographical location of these nodes. So having a reliable localization algorithm is always necessary. Most of presented algorithms for localization of sensor networks considered situations that the sensor nodes are static. In some of sensor networks, the nodes are mobile. So, using static localization algorithms in these networks is not suitable. Thus to support the mobility of nodes in these networks a localization algorithm will be needed that must be consistent with the mobility of nodes. Two important localization algorithms that presented in this area are Monte Carlo localization algorithm (MCL) and its improvement Monte Carlo localization boxed (MCB). Despite having a good localization accurately, sampling in these algorithms is static and they have high energy consumption. Also these algorithms are not able to localize sensor nodes in some circumstances. The main reason is that in some time slots the node can not hear any seed node. In this paper a new method has been suggested that uses forecasting and dynamic sampling for localization. This method has the ability of nodes localization in these conditions and that is an energy efficient method. Simulation results showed that the proposed method has a better performance in sparse networks in comparison with previous similar methods.

[1]  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).

[2]  Radha Poovendran,et al.  SeRLoc: secure range-independent localization for wireless sensor networks , 2004, WiSe '04.

[3]  Winston Khoon Guan Seah,et al.  Selective Iterative Multilateration for Hop Count-Based Localization in Wireless Sensor Networks , 2006, 7th International Conference on Mobile Data Management (MDM'06).

[4]  Ying Zhang,et al.  Localization from mere connectivity , 2003, MobiHoc '03.

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

[6]  Deborah Estrin,et al.  GPS-less low-cost outdoor localization for very small devices , 2000, IEEE Wirel. Commun..

[7]  David C. Moore,et al.  Robust distributed network localization with noisy range measurements , 2004, SenSys '04.

[8]  L. El Ghaoui,et al.  Convex position estimation in wireless sensor networks , 2001, Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No.01CH37213).

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

[10]  Wolfram Burgard,et al.  Monte Carlo localization for mobile robots , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[11]  Julie A. McCann,et al.  Towards the design of an energy-efficient, location-aware routing protocol for mobile, ad-hoc sensor networks , 2004 .

[12]  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).

[13]  Vincent W. S. Wong,et al.  Dual and Mixture Monte Carlo Localization Algorithms for Mobile Wireless Sensor Networks , 2007, 2007 IEEE Wireless Communications and Networking Conference.

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

[15]  Cesare Alippi,et al.  A RSSI-based and calibrated centralized localization technique for wireless sensor networks , 2006, Fourth Annual IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOMW'06).

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

[17]  Santosh Pandey,et al.  A survey on localization techniques for wireless networks , 2006 .

[18]  Xue Wang,et al.  Time Series Forecasting for Energy-efficient Organization of Wireless Sensor Networks , 2007, Sensors.

[19]  Julie A. McCann,et al.  Towards the design of an energy-efficient, location-aware routing protocol for mobile, ad-hoc sensor networks , 2004, Proceedings. 15th International Workshop on Database and Expert Systems Applications, 2004..

[20]  Hewijin Christine Jiau,et al.  Localization with mobile anchor points in wireless sensor networks , 2005, IEEE Transactions on Vehicular Technology.

[21]  Hojung Cha,et al.  Multi-hop-based Monte Carlo Localization for Mobile Sensor Networks , 2007, 2007 4th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks.

[22]  Sarfraz Nawaz.  Anchor free localization for ad-hoc wireless sensor networks , 2008 .

[23]  B. R. Badrinath,et al.  DV Based Positioning in Ad Hoc Networks , 2003, Telecommun. Syst..

[24]  Koen Langendoen,et al.  Monte Carlo localization for mobile wireless sensor networks , 2008, Ad Hoc Networks.

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

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

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