Sensor Selection and Power Allocation for Distributed Estimation in Sensor Networks: Beyond the Star Topology

Authors:
G. Thatte;U. Mitra
Affiliations:
Dept. of Electr. Eng., Southern California Univ., Los Angeles, CA;-
Venue:
IEEE Transactions on Signal Processing - Part I
Year:
2008

Citing 0
Cited 5

Power constrained distributed estimation with cluster-based sensor collaboration

IEEE Transactions on Wireless Communications
Asymptotic distortion exponents for the estimation of time-varying channels in multihop sensor networks

ACM Transactions on Sensor Networks (TOSN)
Adaptive power allocation in wireless sensor networks with spatially correlated data and analog modulation: perfect and imperfect CSI

EURASIP Journal on Wireless Communications and Networking - Special issue on signal processing-assisted protocols and algorithms for cooperating objects and wireless sensor networks
Wireless MIMO Sensor Network with Power Constraint WLS/BLUE Estimators

Wireless Personal Communications: An International Journal
Cost minimization of measurement devices under estimation accuracy constraints in the presence of Gaussian noise

Digital Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Optimal power allocation for distributed parameter estimation in a wireless sensor network with a fusion center under a total network power constraint is considered. For the simple star topology, an analysis of the effect of the measurement noise variance on the optimal power allocation policy is presented. The optimal solution evolves from sensor selection, to water- filling, to channel inversion as the measurement noise variance increases; in the last solution, the sensor with the weakest channel signal-to-noise ratio (SNR) is allocated the largest fraction of the total power. Relaying nodes are then introduced to form the more complex branch, tree, and linear topologies. The optimal power allocation strategies for these complex topologies are then considered for both amplify-and-forward and estimate-and-forward transmission protocols. Analytical solutions for these cases appear to be intractable, and thus asymptotically optimal (for increasing measurement noise variance) solutions are derived. The solutions to this asymptotic problem offer near-optimal performance even for modest measurement noise. The optimal limiting power policy for the leaf nodes in branch and tree topologies is channel inversion, whereas in linear networks, the optimal solution is a form of weighted channel inversion. The results are extended to a multipath channel model and to the estimation of a vector of random parameters.