Distributed estimation of a parametric field under energy constraint

This paper studies the problem of distributed parameter estimation in wireless sensor network under energy constraints. Optimization formulas that guarantee the best estimation performance from the available energy are derived. The network consists of sensors that are deployed over an area at random. Sensors' observations are noisy measurements of an underlying field. Sensors have limited energy for the transmission process. Each sensor processes its observation prior to transmitting it to a fusion center, where a field parameter vector is estimated. Transmission channels between the sensors and the fusion center are assumed to be noisy parallel channels. The sensors' locations, the noise probability density function, and the field characteristic function are assumed to be known at the fusion center. This work presents two strategies that can be followed for optimal energy allocation:(1) Minimizing Cramer-Rao Lower Bound of the estimates with respect to energy allocation (2) Minimizing the sensors' observations transmission error with respect to energy allocation. Simulation results which support the optimization formulas are shown.