Energy-Efficient Distributed Estimation Using Content-Based Wake-Up in Wireless Sensor Networks

Abstract

In this paper, we advocate applying the concept of content-based wake-up to distributed estimation in wireless sensor networks employing wake-up receivers. With distributed estimation, where sensing data of multiple nodes are used for estimating a target observation, the energy consumption can be reduced by ensuring that only a subset of nodes in the network transmit their data, such that the collected data can guarantee the required estimation accuracy. In this case, a sink needs to selectively wake up those sensor nodes whose data can contribute to the improvement of estimation accuracy. In this paper, we propose wake-up signaling called estimative sampling (ES) that can selectively activate the desired nodes by using content-based wake-up control. The ES method includes a mechanism that dynamically searches for the desired nodes over a distribution of sensing data. With numerical results obtained by computer simulations, we show that the distributed estimation with ES method achieves lower energy consumption than conventional identity-based wake-up while satisfying the required accuracy. We also show that the proposed dynamic mechanism finely controls the trade-off between delay and energy consumption to complete the distributed estimation.