A cross-layer architecture of wireless sensor networks for target tracking
IEEE/ACM Transactions on Networking (TON)
Information fusion for wireless sensor networks: Methods, models, and classifications
ACM Computing Surveys (CSUR)
Journal of Intelligent and Robotic Systems
Tracking in wireless sensor networks using particle filtering: physical layer considerations
IEEE Transactions on Signal Processing
Sidewinder: a predictive data forwarding protocol for mobile wireless sensor networks
SECON'09 Proceedings of the 6th Annual IEEE communications society conference on Sensor, Mesh and Ad Hoc Communications and Networks
Mobile multi-target tracking in two-tier hierarchical wireless sensor networks
MILCOM'09 Proceedings of the 28th IEEE conference on Military communications
MILCOM'09 Proceedings of the 28th IEEE conference on Military communications
EURASIP Journal on Wireless Communications and Networking - Special issue on theoretical and algorithmic foundations of wireless ad hoc and sensor networks
Energy-efficient collaborative tracking in wireless sensor networks
International Journal of Sensor Networks
Tracking a moving object via a sensor network with a partial information broadcasting scheme
Information Sciences: an International Journal
PCRLB-based sensor selection for maneuvering target tracking in range-based sensor networks
Future Generation Computer Systems
Automatic node selection and target tracking in wireless camera sensor networks
Computers and Electrical Engineering
| Hi-index | 0.08 |
We consider the application of sequential Monte Carlo (SMC) methods for Bayesian inference to the problem of information-driven dynamic sensor collaboration in clutter environments for sensor networks. The dynamics of the system under consideration are described by nonlinear sensing models within randomly deployed sensor nodes. The exact solution to this problem is prohibitively complex due to the nonlinear nature of the system. The SMC methods are, therefore, employed to track the probabilistic dynamics of the system and to make the corresponding Bayesian estimates and predictions. To meet the specific requirements inherent in sensor network, such as low-power consumption and collaborative information processing, we propose a novel SMC solution that makes use of the auxiliary particle filter technique for data fusion at densely deployed sensor nodes, and the collapsed kernel representation of the a posteriori distribution for information exchange between sensor nodes. Furthermore, an efficient numerical method is proposed for approximating the entropy-based information utility in sensor selection. It is seen that under the SMC framework, the optimal sensor selection and collaboration can be implemented naturally, and significant improvement is achieved over existing methods in terms of localizing and tracking accuracies.