A4 Refereed article in a conference publication
Decentralized Vision-Based Byzantine Agent Detection in Multi-robot Systems with IOTA Smart Contracts
Authors: Salimpour Sahar, Keramat Farhad, Peña Queralta Jorge, Westerlund Tomi
Editors: Guy-Vincent Jourdan, Laurent Mounier, Carlisle Adams, Florence Sèdes, Joaquin Garcia-Alfaro
Conference name: International Symposium on Foundations and Practice of Security
Publishing place: Cham
Publication year: 2023
Journal: Lecture 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 title: Lecture Notes in Computer Science
Volume: 13877
First page : 322
Last page: 337
ISBN: 978-3-031-30121-6
eISBN: 978-3-031-30122-3
ISSN: 0302-9743
eISSN: 1611-3349
DOI: https://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 address: https://arxiv.org/abs/2210.03441
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.