Distributed state estimation over binary sensor networks with energy harvester and dynamic event-triggering protocol: a scalable design

Abstract

This paper addresses the distributed H_∞-consensus state estimation issue for a class of discrete time-varying systems operating within binary sensor networks. An integral measurement output model is developed for each node to formulate the time intervals associated with sampling. Every binary sensor is equipped with an energy harvester to improve power efficiency. Information transmission between sensor nodes and their neighboring nodes is carefully orchestrated through a dynamic event-triggering protocol. Valuable information for estimation purposes is obtained by analyzing the discrepancies between the real and predicted inputs of binary sensors. Information from neighboring nodes is only transmitted when the node’s energy level is positive and the event-triggering condition is met. Two random variables are introduced to represent the energy level and the information from neighboring nodes to be received or not, respectively. Based on the available information, a distributed estimator is constructed for every binary sensor, and the expected performance constraints are given for the dynamic characteristics of estimation errors within a finite horizon. Sufficient conditions are constructed to obtain the desired distributed estimation performance constraint, and associated estimator gains are achieved by resolving the recursive linear matrix inequalities at each node, indicating the excellent scalability of the proposed approach. Ultimately, the effectiveness of the distributed estimation algorithm proposed in this paper is validated through an extensive simulation analysis.

Keywords

Distributed state estimation / dynamic event-triggering protocol / H_∞-consensus / integral measurements / energy harvesting / sensor network

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