Distributive target tracking in sensor networks with a Markov random field model

Authors:
Lufeng Shi;Jindong Tan
Affiliations:
Department of Electrical Computer Engineering, Michigan Technological University, Houghton, MI;Department of Electrical Computer Engineering, Michigan Technological University, Houghton, MI
Venue:
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Year:
2009

Citing 11
Cited 0

The n-hop multilateration primitive for node localization problems

Mobile Networks and Applications
Distributed Bayesian Algorithms for Fault-Tolerant Event Region Detection in Wireless Sensor Networks

IEEE Transactions on Computers
Tracking on a graph

IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Approaches to Multisensor Data Fusion in Target Tracking: A Survey

IEEE Transactions on Knowledge and Data Engineering
Tracking multiple targets using binary proximity sensors

Proceedings of the 6th international conference on Information processing in sensor networks
Information fusion for wireless sensor networks: Methods, models, and classifications

ACM Computing Surveys (CSUR)
Information fusion in wireless sensor networks

Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Using Wi-Fi Signal Strength to Localize in Wireless Sensor Networks

CMC '09 Proceedings of the 2009 WRI International Conference on Communications and Mobile Computing - Volume 01
Graph Model Based Indoor Tracking

MDM '09 Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware
Boundary estimation in sensor networks: theory and methods

IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Secure and Fault-Tolerant Event Boundary Detection in Wireless Sensor Networks

IEEE Transactions on Wireless Communications

Quantified Score

Hi-index	0.00

Visualization

Abstract

Tracking in sensor networks has shown great potentials in many real world surveillance and emergency system. Due to the distributive nature and unpredictable topology structure of the randomly distributed sensor network, a good tracking algorithm must be able to aggregate large amounts of data from various unknown sources. In this paper, a distributive tracking algorithm is developed using a Markov random field (MRF) model to solve this problem. The Markov random field (MRF) utilizes probability distribution and conditional independency to identify the most relevant data from the less important data. The algorithm converts the randomly distributed network into a regularly distributed topology structure using cliques. This makes tracking in the randomly distributed network topology simple and more predictable. Simulation demonstrate that the algorithm performs well for various sensor field setting, and for various target sizes.