On the Impact of Localization and Density Control Algorithms in Target Tracking Applications for Wireless Sensor Networks

Target tracking is an important application of wireless sensor networks. The networks' ability to locate and track an object is directed linked to the nodes' ability to locate themselves. Consequently, localization systems are essential for target tracking applications. In addition, sensor networks are often deployed in remote or hostile environments. Therefore, density control algorithms are used to increase network lifetime while maintaining its sensing capabilities. In this work, we analyze the impact of localization algorithms (RPE and DPE) and density control algorithms (GAF, A3 and OGDC) on target tracking applications. We adapt the density control algorithms to address the k-coverage problem. In addition, we analyze the impact of network density, residual integration with density control, and k-coverage on both target tracking accuracy and network lifetime. Our results show that DPE is a better choice for target tracking applications than RPE. Moreover, among the evaluated density control algorithms, OGDC is the best option among the three. Although the choice of the density control algorithm has little impact on the tracking precision, OGDC outperforms GAF and A3 in terms of tracking time.

[1]  Éfren Lopes Souza,et al.  On the performance of target tracking algorithms using actual localization systems for wireless sensor networks , 2009, MSWiM '09.

[2]  Michael Beetz,et al.  Cooperative probabilistic state estimation for vision-based autonomous mobile robots , 2002, IEEE Trans. Robotics Autom..

[3]  Azzedine Boukerche,et al.  Localization systems for wireless sensor networks , 2007, IEEE Wireless Communications.

[4]  Jeffrey K. Uhlmann,et al.  Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.

[5]  Eduardo Freire Nakamura,et al.  A Hybrid Adaptive Routing Algorithm for Event-Driven Wireless Sensor Networks , 2009, Sensors.

[6]  Lixia Zhang,et al.  Recursive position estimation in sensor networks , 2001, Proceedings Ninth International Conference on Network Protocols. ICNP 2001.

[7]  Eduardo Freire Nakamura,et al.  Diffuse: A topology building engine for wireless sensor networks , 2007, Signal Process..

[8]  J. L. Roux.  An Introduction to the Kalman Filter , 2003 .

[9]  W. H. Neill,et al.  Modelling animal movement as a persistent random walk in two dimensions: expected magnitude of net displacement , 2000 .

[10]  Mark Smith,et al.  Mobile computing technology at Vindigo , 2002, IEEE Wirel. Commun..

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

[12]  Deborah Estrin,et al.  Geography-informed energy conservation for Ad Hoc routing , 2001, MobiCom '01.

[13]  Zack J. Butler,et al.  Tracking a moving object with a binary sensor network , 2003, SenSys '03.

[14]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[15]  Eduardo Freire Nakamura,et al.  A reactive role assignment for data routing in event-based wireless sensor networks , 2009, Comput. Networks.

[16]  Azzedine Boukerche,et al.  Error analysis of localization systems for sensor networks , 2005, GIS '05.

[17]  Miguel A. Labrador,et al.  A3: A Topology Construction Algorithm for Wireless Sensor Networks , 2008, IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference.

[18]  Ahmad Hashemi-Sakhtsari,et al.  Target tracking by time difference of arrival using recursive smoothing , 2005, Signal Process..

[19]  Saurabh Bagchi,et al.  Location tracking with directional antennas in wireless sensor networks , 2005, IMS 2005.

[20]  Azzedine Boukerche,et al.  An Efficient Directed Localization Recursion Protocol for Wireless Sensor Networks , 2009, IEEE Transactions on Computers.

[21]  Prasant Mohapatra,et al.  Power conservation and quality of surveillance in target tracking sensor networks , 2004, MobiCom '04.

[22]  Jennifer C. Hou,et al.  Maintaining Sensing Coverage and Connectivity in Large Sensor Networks , 2005, Ad Hoc Sens. Wirel. Networks.

[23]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[24]  H. Sorenson.  Least-squares estimation: from Gauss to Kalman , 1970, IEEE Spectrum.

[25]  Carlos Mauricio S. Figueiredo,et al.  An integrated approach for density control and routing in wireless sensor networks , 2006, Proceedings 20th IEEE International Parallel & Distributed Processing Symposium.

[26]  Deborah Estrin,et al.  ASCENT: adaptive self-configuring sensor networks topologies , 2004, IEEE Transactions on Mobile Computing.

[27]  Eduardo F. Nakamura,et al.  Using Information Fusion to Assist Data Dissemination in Wireless Sensor Networks , 2005, Telecommun. Syst..

[28]  Fredrik Gustafsson,et al.  Particle filters for positioning in wireless networks , 2002, 2002 11th European Signal Processing Conference.

[29]  Eduardo F. Nakamura,et al.  Information fusion for wireless sensor networks: Methods, models, and classifications , 2007, CSUR.

[30]  Johann van der Merwe,et al.  A survey on peer-to-peer key management for mobile ad hoc networks , 2007, CSUR.

[31]  Songwu Lu,et al.  PEAS: a robust energy conserving protocol for long-lived sensor networks , 2002, 10th IEEE International Conference on Network Protocols, 2002. Proceedings..

[32]  Azzedine Boukerche,et al.  DV-Loc: a scalable localization protocol using Voronoi diagrams for wireless sensor networks , 2009, IEEE Wireless Communications.

[33]  Di Tian,et al.  A coverage-preserving node scheduling scheme for large wireless sensor networks , 2002, WSNA '02.