Secure Multi-Robot Information Sampling with Periodic and Opportunistic Connectivity

Abstract

Multi-robot teams are becoming an increasingly popular approach for information gathering in large geographic areas, with applications in precision agriculture, surveying the aftermath of natural disasters or tracking pollution. These robot teams are often assembled from untrusted devices not owned by the user, making the maintenance of the integrity of the collected samples an important challenge. Furthermore, such robots often operate under conditions of opportunistic, or periodic connectivity and are limited in their energy budget and computational power. In this paper, we propose algorithms that build on blockchain technology to address the data integrity problem, but also take into account the limitations of the robots' resources and communication. We evaluate the proposed algorithms along the perspective of the tradeoffs between data integrity, model accuracy, and time consumption.