Quantization, Channel Compensation, and Energy Allocation for Estimation in Wireless Sensor Networks

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.