Fast state estimation under sensor attacks: a senor categorization approach

TL;DR

This paper introduces a fast state estimation algorithm for sensor networks under attacks by categorizing sensors into analytic types based on measurement equivalence, reducing computational complexity and improving efficiency.

Contribution

It proposes a novel sensor categorization approach that leverages measurement equivalence to enhance secure state estimation speed and accuracy.

Findings

High speed performance demonstrated due to fewer sensor types

Effective attack location identification without estimation accuracy loss

Significant reduction in computational complexity

Abstract

In a sensor network, some sensors usually provide the same or equivalent measurement information, which is not taken into account by the existing secure state estimation methods against sparse sensor attacks such that the computational efficiency of these methods needs to be further improved. In this paper, by considering the observation equivalence of sensor measurement, a concept of analytic sensor types is introduced based on the equivalent class to develop a fast state estimation algorithm. By testing the measurement data of a sensor type, the attack location information can be extracted to exclude some mismatching search candidates, without loss of estimation correctness. This confirms high speed performance of the proposed algorithm, since the number of sensor types is usually far less than the number of sensors.