Distributed Algorithms to Form Cluster Based Spanning Trees in Wireless Sensor Networks
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part I
A Cluster-Based Approach for Collaborative Target Tracking in Wireless Sensor Networks
ICETET '08 Proceedings of the 2008 First International Conference on Emerging Trends in Engineering and Technology
EKF-Based Adaptive Sensor Scheduling for Target Tracking
ISISE '08 Proceedings of the 2008 International Symposium on Information Science and Engieering - Volume 02
Tracking in wireless sensor networks using particle filtering: physical layer considerations
IEEE Transactions on Signal Processing
A Distributed Wakening Based Target Tracking Protocol for Wireless Sensor Networks
ISPDC '10 Proceedings of the 2010 Ninth International Symposium on Parallel and Distributed Computing
Small target detection using cross product based on temporal profile in infrared image sequences
Computers and Electrical Engineering
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
DCTC: dynamic convoy tree-based collaboration for target tracking in sensor networks
IEEE Transactions on Wireless Communications
Prediction-based cluster management for target tracking in wireless sensor networks
Wireless Communications & Mobile Computing
Data association and geolocation for electronic support systems
Computers and Electrical Engineering
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In this study, five different algorithms are provided for tracking targets that move very fast in wireless sensor networks. The first algorithm is static and clusters are formed initially at the time of network deployment. In the second algorithm, clusters that have members at one hop distance from the cluster head are provided dynamically. In the third algorithm, clustered trees where members of a cluster may be more than one hop distance from the cluster head are provided dynamically. In the fourth, algorithm lookahead trees are formed along the predicted trajectory of the target dynamically. Linear, Kalman and particle filtering techniques are used to predict the target's next state. The algorithms are compared for linear and nonlinear motions of the target against tracking accuracy, energy consumption and missing ratio parameters. Simulation results show that, for all cases, better performance results are obtained in the dynamic lookahead tree based tracking approach.