Minimum energy decentralized estimation in a wireless sensor network with correlated sensor noises
EURASIP Journal on Wireless Communications and Networking
A space-time diffusion scheme for peer-to-peer least-squares estimation
Proceedings of the 5th international conference on Information processing in sensor networks
A scheme for robust distributed sensor fusion based on average consensus
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Stochastic binary sensor networks for noisy environments
International Journal of Sensor Networks
Power constrained distributed estimation with cluster-based sensor collaboration
IEEE Transactions on Wireless Communications
Optimal rate allocation for multi-sensor distributed estimation
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Quantization, channel compensation, and energy allocation for estimation in wireless sensor networks
WiOPT'09 Proceedings of the 7th international conference on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks
A collaborative sensor-fault detection scheme for robust distributed estimation in sensor networks
IEEE Transactions on Communications
Adaptive fast consensus algorithm for distributed sensor fusion
Signal Processing
Performance limit for distributed estimation systems with identical one-bit quantizers
IEEE Transactions on Signal Processing
Nonparametric one-bit quantizers for distributed estimation
IEEE Transactions on Signal Processing
Low-complexity one-dimensional edge detection in wireless sensor networks
EURASIP Journal on Wireless Communications and Networking - Special issue on signal processing-assisted protocols and algorithms for cooperating objects and wireless sensor networks
Optimizing network lifetime for distributed tracking with wireless sensor networks
Proceedings of the 6th ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks
Quantization, channel compensation, and optimal energy allocation for estimation in sensor networks
ACM Transactions on Sensor Networks (TOSN)
| Hi-index | 0.08 |
Consider a decentralized estimation problem whereby an ad hoc network of K distributed sensors wish to cooperate to estimate an unknown parameter over a bounded interval [-U,U]. Each sensor collects one noise-corrupted sample, performs a local data quantization according to a fixed (but possibly probabilistic) rule, and transmits the resulting discrete message to its neighbors. These discrete messages are then percolated in the network and used by each sensor to form its own minimum mean squared error (MMSE) estimate of the unknown parameter according to a fixed fusion rule. In this paper, we propose a simple probabilistic local quantization rule: each sensor quantizes its observation to the first most significant bit (MSB) with probability 1/2, the second MSB with probability 1/4, and so on. Assuming the noises are uncorrelated and identically distributed across sensors and are bounded to [-U,U], we show that this local quantization strategy together with a fusion rule can guarantee a MSE of 4U2/K, and that the average length of local messages is bounded (no more than 2.5 bits). Compared with the worst case Cramer-Rao lower bound of U2/K (even for the centralized counterpart), this is within a factor of at most 4 to the minimum achievable MSE. Moreover, the proposed scheme is isotropic and universal in the sense that the local quantization rules and the final fusion rules are independent of sensor index, noise distribution, network size, or topology. In fact, the proposed scheme allows sensors in the network to operate identically and autonomously even when the network undergoes changes in size or topology.