Joint Coverage Scheduling and Identity Management for Multiple-Target Tracking in Wireless Sensor Networks

Wireless surveillance sensor networks are often required to track multiple targets accurately, especially when the targets come close to each other at so-called mixing regions. This research presents eTrack protocol to address the following two problems for a number of targets and their mixing region: (i) which sensor nodes should be scheduled to sense a particular event such that targets are tracked accurately, and (ii) which pair of tracks passing through the mixing region belong to a particular target. To tackle the scheduling problem, eTrack introduces a first-of-its-kind closed-loop coverage control technique that allocates resources of sensor nodes for each target in proportion with its required tracking accuracy. To handle the identity management problem, eTrack exploits the fact that targets prefer certain paths through a mixing region and takes advantage of the spatio-temporal correlation of events detected by different sensor nodes within the mixing region for the same target. Simulation results that eTrack significantly reduces the computation and communication overhead with mixing regions while achieving better tracking accuracy than existing multiple target tracking techniques.

[1]  S.S. Blackman,et al.  Multiple hypothesis tracking for multiple target tracking , 2004, IEEE Aerospace and Electronic Systems Magazine.

[2]  Wei Wang,et al.  Coverage for target localization in wireless sensor networks , 2006, 2006 5th International Conference on Information Processing in Sensor Networks.

[3]  Guoliang Xing,et al.  Integrated coverage and connectivity configuration in wireless sensor networks , 2003, SenSys '03.

[4]  David Simplot-Ryl,et al.  Energy-efficient area monitoring for sensor networks , 2004, Computer.

[5]  Wolfgang Maass,et al.  Fast Approximation Algorithms for a Nonconvex Covering Problem , 1987, J. Algorithms.

[6]  Himanshu Gupta,et al.  Connected sensor cover: self-organization of sensor networks for efficient query execution , 2003, IEEE/ACM Transactions on Networking.

[7]  H. Balakrishnan,et al.  Polynomial approximation algorithms for belief matrix maintenance in identity management , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[8]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[9]  Arthur P. Dempster,et al.  Upper and Lower Probabilities Induced by a Multivalued Mapping , 1967, Classic Works of the Dempster-Shafer Theory of Belief Functions.

[10]  Jennifer C. Hou,et al.  Tracking targets with quality in wireless sensor networks , 2005, 13TH IEEE International Conference on Network Protocols (ICNP'05).

[11]  Y. Bar-Shalom Tracking and data association , 1988 .

[12]  Feng Zhao,et al.  Distributed Group Management for Track Initiation and Maintenance in Target Localization Applications , 2003, IPSN.

[13]  S. Shankar Sastry,et al.  A Hierarchical Multiple-Target Tracking Algorithm for Sensor Networks , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[14]  Martin Nilsson,et al.  Investigating the energy consumption of a wireless network interface in an ad hoc networking environment , 2001, Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No.01CH37213).

[15]  Yu Hen Hu,et al.  Detection, classification, and tracking of targets , 2002, IEEE Signal Process. Mag..

[16]  Di Tian,et al.  A node scheduling scheme for energy conservation in large wireless sensor networks , 2003, Wirel. Commun. Mob. Comput..

[17]  Leonidas J. Guibas,et al.  A Distributed Algorithm for Managing Multi-target Identities in Wireless Ad-hoc Sensor Networks , 2003, IPSN.