Data Fusion and Sensor Management: A Decentralized Information-Theoretic Approach
Data Fusion and Sensor Management: A Decentralized Information-Theoretic Approach
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
Distributed weighted-multidimensional scaling for node localization in sensor networks
ACM Transactions on Sensor Networks (TOSN)
Collaborative in-network processing for target tracking
EURASIP Journal on Applied Signal Processing
Binary Variational Filtering for Target Tracking in Sensor Networks
SSP '07 Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing
Wireless Sensor Networks
Distributed Energy Optimization for Target Tracking in Wireless Sensor Networks
IEEE Transactions on Mobile Computing
Maximum mutual information principle for dynamic sensor query problems
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Adaptive sensor activation for target tracking in wireless sensor networks
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Distributed target tracking using signal strength measurements by a wireless sensor network
IEEE Journal on Selected Areas in Communications - Special issue on simple wireless sensor networking solutions
Capacity aware optimal activation of sensor nodes under reproduction distortion measures
MILCOM'06 Proceedings of the 2006 IEEE conference on Military communications
Adaptive quantized target tracking in wireless sensor networks
Wireless Networks
IEEE Transactions on Mobile Computing
IEEE Transactions on Signal Processing
Rate-Constrained Distributed Estimation in Wireless Sensor Networks
IEEE Transactions on Signal Processing
SOI-KF: Distributed Kalman Filtering With Low-Cost Communications Using the Sign of Innovations
IEEE Transactions on Signal Processing
Particle filters for state-space models with the presence ofunknown static parameters
IEEE Transactions on Signal Processing
Design challenges for energy-constrained ad hoc wireless networks
IEEE Wireless Communications
Energy-constrained modulation optimization
IEEE Transactions on Wireless Communications
RSS-Based Location Estimation with Unknown Pathloss Model
IEEE Transactions on Wireless Communications
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In this paper, we address the problem of genetic algorithm optimization for jointly selecting the best group of candidate sensors and optimizing the quantization for target tracking in wireless sensor networks. We focus on a more challenging problem of how to effectively utilize quantized sensor measurement for target tracking in sensor networks by considering best group of candidate sensors selection problem. The main objective of this paper is twofold. Firstly, the quantization level and the group of candidate sensors selection are to be optimized in order to provide the required data of the target and to balance the energy dissipation in the wireless sensor network. Secondly, the target position is to be estimated using quantized variational filtering (QVF) algorithm. The optimization of quantization and sensor selection are based on the Fast and Elitist Multi-objective Genetic Algorithm (NSGA-II). The proposed multi-objective (MO) function defines the main parameters that may influence the relevance of the participation in cooperation for target tracking and the transmitting power between one sensor and the cluster head (CH). The proposed algorithm is designed to: i) avoid the problem lot of computing times and operation counts, and ii) reduce the communication cost and the estimation error, which leads to a significant reduction of energy consumption and an accurate target tracking. The computation of these criteria is based on the predictive information provided by the QVF algorithm. The simulation results show that the NSGA-II -based QVF algorithm outperforms the standard quantized variational filtering algorithm and the centralized quantized particle filter.