Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Next century challenges: scalable coordination in sensor networks
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Power-Aware Localized Routing in Wireless Networks
IEEE Transactions on Parallel and Distributed Systems
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Entropy-based sensor selection heuristic for target localization
Proceedings of the 3rd international symposium on Information processing in sensor networks
Dynamic Clustering for Acoustic Target Tracking in Wireless Sensor Networks
IEEE Transactions on Mobile Computing
Structures for In-Network Moving Object Tracking in Wireless Sensor Networks
BROADNETS '04 Proceedings of the First International Conference on Broadband Networks
Collaborative in-network processing for target tracking
EURASIP Journal on Applied Signal Processing
A Branch and Bound Clustering Algorithm
IEEE Transactions on Computers
Distributed and energy-efficient target localization and tracking in wireless sensor networks
Computer Communications
Maximum mutual information principle for dynamic sensor query problems
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Acoustic target tracking using tiny wireless sensor devices
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Posterior Cramer-Rao bounds for discrete-time nonlinear filtering
IEEE Transactions on Signal Processing
Decentralized sigma-point information filters for target tracking in collaborative sensor networks
IEEE Transactions on Signal Processing - Part II
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
An application-specific protocol architecture for wireless microsensor networks
IEEE Transactions on Wireless Communications
DCTC: dynamic convoy tree-based collaboration for target tracking in sensor networks
IEEE Transactions on Wireless Communications
Dynamic sensor collaboration via sequential Monte Carlo
IEEE Journal on Selected Areas in Communications
Nonparametric belief propagation for self-localization of sensor networks
IEEE Journal on Selected Areas in Communications
| Hi-index | 0.00 |
The problem of collaborative tracking of mobile nodes in wireless sensor networks is addressed. By using a novel metric derived from the energy model in LEACH (W.B. Heinzelman, A.P. Chandrakasan and H. Balakrishnan, Energy-Efficient Communication Protocol for Wireless Microsensor Networks, in: Proceedings of the 33rd Hawaii International Conference on System Sciences (HICSS '00), 2000) and aiming at an efficient resource solution, the approach adopts a strategy of combining target tracking with node selection procedures in order to select informative sensors to minimize the energy consumption of the tracking task. We layout a cluster-based architecture to address the limitations in computational power, battery capacity and communication capacities of the sensor devices. The computation of the posterior Cramer-Rao bound (PCRB) based on received signal strength measurements has been considered. To track mobile nodes two particle filters are used: the bootstrap particle filter and the unscented particle filter, both in the centralized and in the distributed manner. Their performances are compared with the theoretical lower bound PCRB. To save energy, a node selection procedure based on greedy algorithms is proposed. The node selection problem is formulated as a cross-layer optimization problem and it is solved using greedy algorithms.