Attack-resilient Fusion of Sensor Data with Uncertain Delays

Yanfeng Chen, Tianyu Zhang, Fanxin Kong, Lin Zhang, Qingxu Deng

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Malicious attackers may disrupt the safety of autonomous systems through compromising sensors to feed wrong measurements to the controller. This article proposes attack-resilient sensor fusion that combines local sensor readings and shared sensing information from multiple sources. The method results in higher resilience against sensor attacks through jointly considering sensing noise and uncertain communication delay. To be specific, we first identify the considerable impact of the delay on determining attacked sensors. Second, we present a novel two-dimensional abstract sensor model, where each measurement is augmented as a probabilistic interval based on the convolution of the noise and delay. Third, we propose a fusion algorithm that admits the fused value with highest joint probability distribution of the intervals to tolerate corrupted measurements. Finally, we demonstrate the effectiveness of our method in a vehicle-platoon case study using extensive simulations and testbed experiments.

Original languageEnglish (US)
Article number39
JournalTransactions on Embedded Computing Systems
Volume21
Issue number4
DOIs
StatePublished - Aug 23 2022

Keywords

  • Autonomous systems
  • attack-resilience
  • security
  • sensor fusion
  • uncertain delay

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture

Fingerprint

Dive into the research topics of 'Attack-resilient Fusion of Sensor Data with Uncertain Delays'. Together they form a unique fingerprint.

Cite this