A4 Refereed article in a conference publication

Decentralized Vision-Based Byzantine Agent Detection in Multi-robot Systems with IOTA Smart Contracts




AuthorsSalimpour Sahar, Keramat Farhad, Peña Queralta Jorge, Westerlund Tomi

EditorsGuy-Vincent Jourdan, Laurent Mounier, Carlisle Adams, Florence Sèdes, Joaquin Garcia-Alfaro

Conference nameInternational Symposium on Foundations and Practice of Security

Publishing placeCham

Publication year2023

JournalLecture Notes in Computer Science

Book title Foundations and Practice of Security: 15th International Symposium, FPS 2022, Ottawa, ON, Canada, December 12–14, 2022, Revised Selected Papers

Series titleLecture Notes in Computer Science

Volume13877

First page 322

Last page337

ISBN978-3-031-30121-6

eISBN978-3-031-30122-3

ISSN0302-9743

eISSN1611-3349

DOIhttps://doi.org/10.1007/978-3-031-30122-3_20

Web address https://link.springer.com/chapter/10.1007/978-3-031-30122-3_20

Preprint addresshttps://arxiv.org/abs/2210.03441


Abstract

Multiple opportunities lie at the intersection of multi-robot systems and distributed ledger technologies (DLTs). In this work, we investigate the potential of new DLT solutions such as IOTA, for detecting anomalies and byzantine agents in multi-robot systems in a decentralized manner. Traditional blockchain approaches are not applicable to real-world networked and decentralized robotic systems where connectivity conditions are not ideal. To address this, we leverage recent advances in partition-tolerant and byzantine-tolerant collaborative decision-making processes with IOTA smart contracts. We show how our work in vision-based anomaly and change detection can be applied to detecting byzantine agents within multiple robots operating in the same environment. We show that IOTA smart contracts add a low computational overhead while allowing to build trust within the multi-robot system. The proposed approach effectively enables byzantine robot detection based on the comparison of images submitted by the different robots and detection of anomalies and changes between them.



Last updated on 2025-27-03 at 21:50