Genetic algorithm convergence study for sensor network optimization
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on evolutionary algorithms
Wireless Sensor Networks: An Information Processing Approach
Wireless Sensor Networks: An Information Processing Approach
Entropy-based sensor selection heuristic for target localization
Proceedings of the 3rd international symposium on Information processing in sensor networks
Spatio-temporal correlation: theory and applications for wireless sensor networks
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: In memroy of Olga Casals
Utility based sensor selection
Proceedings of the 5th international conference on Information processing in sensor networks
Telos: enabling ultra-low power wireless research
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Sensor scheduling and efficient algorithm implementation for target tracking
Sensor scheduling and efficient algorithm implementation for target tracking
Collaborative in-network processing for target tracking
EURASIP Journal on Applied Signal Processing
D-optimal design of a monitoring network for parameter estimation of distributed systems
Journal of Global Optimization
Sensor selection via convex optimization
IEEE Transactions on Signal Processing
Estimation of Spatially Distributed Processes Using Mobile Spatially Distributed Sensor Network
SIAM Journal on Control and Optimization
ACC'09 Proceedings of the 2009 conference on American Control Conference
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A fundamental question for observation systems based on wireless sensor network (WSN) is to achieve the best tradeoff between estimation precision and many design factors. One challenge of this kind is to select a small number of sensors, if possible, to observe the environment, and thus conserve precious onboard battery energy by transmitting only valuable data to the base station. Although many sensor selection methods have been proposed, the analysis on this problem is relatively limited. Based on the theory of optimal experimental design and convex analysis, we present some feasibility analysis on the optimal sensor selection problem. Equipped with Fisher information matrix, we show that there exists a threshold, which we named Carathéodory's limit, such that the optimal estimation is always feasible as far as the number of selected sensors is no less than that limit. We also investigated on the difference between the total sample number and the total sensor number. Discussions on some necessary conditions of sensor density and sensor deployment patterns are included too. We argue that sensor selection methods have potentials to save significant amount of energy for a large class of embedded wireless sensor networks without sacrificing estimation accuracy.