Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Minimizing communication costs in hierarchically-clustered networks of wireless sensors
Computer Networks: The International Journal of Computer and Telecommunications Networking
Distributed average consensus with least-mean-square deviation
Journal of Parallel and Distributed Computing
IEEE Transactions on Signal Processing
Rate-Constrained Distributed Estimation in Wireless Sensor Networks
IEEE Transactions on Signal Processing
Bandwidth-constrained distributed estimation for wireless sensor Networks-part I: Gaussian case
IEEE Transactions on Signal Processing
Consensus in Ad Hoc WSNs With Noisy Links—Part I: Distributed Estimation of Deterministic Signals
IEEE Transactions on Signal Processing
Power scheduling of universal decentralized estimation in sensor networks
IEEE Transactions on Signal Processing
Constrained Decentralized Estimation Over Noisy Channels for Sensor Networks
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Sequential signal encoding from noisy measurements using quantizers with dynamic bias control
IEEE Transactions on Information Theory
Universal decentralized estimation in a bandwidth constrained sensor network
IEEE Transactions on Information Theory
Decentralized estimation in an inhomogeneous sensing environment
IEEE Transactions on Information Theory
IEEE Journal on Selected Areas in Communications
Low-complexity algorithms for event detection in wireless sensor networks
IEEE Journal on Selected Areas in Communications - Special issue on simple wireless sensor networking solutions
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In clustered networks of wireless sensor motes, each mote collects noisy observations of the environment, quantizes these observations into a local estimate of finite length, and forwards them through one or more noisy wireless channels to the Cluster Head (CH). The measurement noise is assumed to be zero-mean and have finite variance. Each wireless hop is assumed to be a Binary Symmetric Channel (BSC) with a known crossover probability. We propose a novel scheme that uses dithered quantization and channel compensation to ensure that each motes' local estimate received by the CH is unbiased. The CH then fuses these unbiased local estimates into a global one using a Best Linear Unbiased Estimator (BLUE). The energy allocation problem at each mote and among different sensor motes are also discussed. Simulation results show that the proposed scheme can achieve much smaller mean square error (MSE) than two other common schemes while using the same amount of energy. The sensitivity of the proposed scheme to errors in estimates of the crossover probability of the BSC channel is studied by both analysis and simulation.