Joint estimation in sensor networks under energy constraints

We consider the problem of optimal power scheduling for the decentralized estimation of a noise-corrupted signal in an inhomogeneous sensor network. Sensor observations are first quantized into discrete messages, then transmitted to the fusion center where a final estimate is generated. Based on the sensor noise levels and channel gains from sensors to the fusion center, optimal quantization levels and transmit power levels at local sensors can be chosen to minimize the total transmitting power, while ensuring a given Mean Squared Error (MSE) performance. The proposed optimal power scheduling scheme suggests that the sensors with bad channels or poor observation qualities should decrease their quantization resolutions or simply become inactive in order to conserve power. For the remaining active sensors, their optimal quantization and transmit power levels are determined jointly by individual channel gains, local observation noise variance, and the targeted MSE performance. Numerical examples show that up to 60% energy savings is possible when compared with the uniform quantization strategy.