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.
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Acknowledgment
This research work is supported by the R3Swarms project funded by the Secure Systems Research Center (SSRC), Technology Innovation Institute (TII).
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Salimpour, S., Keramat, F., Queralta, J.P., Westerlund, T. (2023). Decentralized Vision-Based Byzantine Agent Detection in Multi-robot Systems with IOTA Smart Contracts. In: Jourdan, GV., Mounier, L., Adams, C., Sèdes, F., Garcia-Alfaro, J. (eds) Foundations and Practice of Security. FPS 2022. Lecture Notes in Computer Science, vol 13877. Springer, Cham. https://doi.org/10.1007/978-3-031-30122-3_20
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