Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
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
Maximum lifetime routing in wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
Minimum energy decentralized estimation in a wireless sensor network with correlated sensor noises
EURASIP Journal on Wireless Communications and Networking
Network correlated data gathering with explicit communication: NP-completeness and algorithms
IEEE/ACM Transactions on Networking (TON)
Estimation Diversity and Energy Efficiency in Distributed Sensing
IEEE Transactions on Signal Processing
IEEE Journal on Selected Areas in Communications
A tutorial on cross-layer optimization in wireless networks
IEEE Journal on Selected Areas in Communications
Cooperative routing for distributed detection in large sensor networks
IEEE Journal on Selected Areas in Communications
An experimental framework for data gathering and analysis in wireless sensor networks
WiNTECH '11 Proceedings of the 6th ACM international workshop on Wireless network testbeds, experimental evaluation and characterization
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We study the problem of maximum lifetime in wireless sensor networks that are entitled with the task of estimating an unknown parameter or process. Sensors take measurements and transfer them in multi-hop fashion to a fusion center (FC) for Maximum Likelihood (ML) estimation. To engineer the network for lifetime maximization while adhering to estimation error specifications, the number of measurements by each sensor per unit of time (namely, sensor measurement rate) and the routes to the FC are controlled. Sensor spatial correlation, measurement accuracies, link qualities and energy reserves affect sensor measurement rates and the routes to the FC, and, in turn, measurement rates and sensor characteristics impact estimation error. We show that the problem can be decomposed into separate optimization problems where each sensor autonomously takes its measurement rate and routing decisions, and we propose an iterative primal-dual algorithm with low-overhead signaling for solving it. Our work optimally captures the fundamental tradeoff between network lifetime and estimation quality and yields a solution based on distributed sensor coordination.