Energy-Efficient Sequential Estimation Via Sensor Ordering

Abstract

Estimation problems in wireless sensor networks (WSNs) typically involve collecting and processing data from distributed sensors at the fusion center to infer the state of an environment. However, not all measurements contribute equally to estimation accuracy. In this work, we incorporate the concept of ordered transmission into sequential estimation to select the most informative measurements from different sensors, while ensuring the desired estimation quality. We analyze a general estimation problem with different estimator choices and derive stopping rules for collecting measurements. Then, we derive the expected number of transmissions required for our ordered transmission-based sequential estimation scheme and compare it with that of a conventional sequential estimation scheme with unordered transmissions. To validate the proposed protocol, we apply it to a radar-based WSN for target localization and velocity estimation, designing an ordered transmission strategy and a sequential stopping rule. Simulation results show that our protocol requires fewer transmissions compared to conventional sequential estimation while maintaining similar estimation accuracy in general WSNs. In a radar-based WSN, the proposed protocol achieves reliable estimation with reduced communication overhead and improved response time.