Data-driven strategies for selective data transmission in sensor networks

Energy efficiency is a crucial issue for any task involving wireless sensor networks. The present paper addresses nonlinear state estimation over a centralized sensor network, i.e. a set of sensor nodes communicating with a central information fusion unit, and proposes smart data-driven strategies by which sensors decide which data transmit to the central unit so as to reduce data communication, and thus avoid congestion problems as well as prolong the network lifetime, while providing enhanced performance with respect to periodic transmission. Both measurement and estimate transmission strategies are developed. To cope with nonlinear sensors that cannot fully observe the state, suitable nonlinear observability decompositions are employed. A bearing-only tracking simulation case-study is presented in order to demonstrate the effectiveness of the proposed approach.