Fast Deterministic Gathering with Detection on Arbitrary Graphs: The Power of Many Robots

Over the years, much research involving mobile computational entities has been performed. From modeling actual microscopic (and smaller) robots, to modeling software processes on a network, many important problems have been studied in this context. Gathering is one such fundamental problem in this area. The problem of gathering k robots, initially arbitrarily placed on the nodes of an n-node graph, asks that these robots coordinate and communicate in a local manner, as opposed to global, to move around the graph, find each other, and settle down on a single node as fast as possible. A more difficult problem to solve is gathering with detection, where once the robots gather, they must subsequently realize that gathering has occurred and then terminate. In this paper, we propose a deterministic approach to solve gathering with detection for any arbitrary connected graph that is faster than existing deterministic solutions for even just gathering (without the requirement of detection) for arbitrary graphs. In contrast to earlier work on gathering, it leverages the fact that there are more robots present in the system to achieve gathering with detection faster than those previous papers that focused on just gathering. The state of the art solution for deterministic gathering [Ta-Shma and Zwick, TALG, 2014] takes O˜(n 5 log ℓ) rounds, where ℓ is the smallest label among robots and O˜ hides a polylog factor. We design a deterministic algorithm for gathering with detection with the following trade-offs depending on how many robots are present: (i) when k ≥ ⌊n/2⌋ + 1, the algorithm takes O(n 3 ) rounds, (ii) when k ≥ ⌊n/3⌋+1, the algorithm takes O(n 4 log n) rounds, and (iii) otherwise, the algorithm takes O˜(n 5 ) rounds. The algorithm is not required to know k, but only

Multichannel Distributed Coordination for Wireless Sensor Networks: Convergence Delay and Energy Consumption Aspects

This thesis develops new approaches for distributed coordination of data-intensive communications between wireless sensor nodes. In particular, the topic of synchronization, and its dual primitive, desynchronization at the Medium Access Control (MAC) or the Application (APP) layer of the OSI stack, is studied in detail. In Chapters 1 and 2, the related literature on the problem of synchronization is overviewed and the main approaches for distributed (de)synchronization at the MAC or APP layers are analyzed, designed and implemented on IEEE802.15.4- enabled wireless sensor nodes. Beyond the experimental validation of distributed (de)synchronization approaches, the three main contributions of this thesis, corresponding to the related publications found below, are: • establishing for the first time the expected time for convergence to distributed time division multiple access (TDMA) operation under the two main desynchronization models proposed in the literature and validating the derived estimates via a real-world implementation (Chapter 3); • proposing the extension of the main desynchronization models towards multi-hop and multi-channel operation; the latter is achieved by extending the concept of reactive listening to multi-frequency operation (Chapter 4 and 5). • analyzing the energy consumption of the distributed TDMA approach under different transmission probability density functions (Chapter 6 and 7). Conclusions and items for future work in relation to the proposals of this thesis are described in Chapter 8

Fifth Conference on Artificial Intelligence for Space Applications

The Fifth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: automation for Space Station; intelligent control, testing, and fault diagnosis; robotics and vision; planning and scheduling; simulation, modeling, and tutoring; development tools and automatic programming; knowledge representation and acquisition; and knowledge base/data base integration

LIPIcs, Volume 251, ITCS 2023, Complete Volume

LIPIcs, Volume 251, ITCS 2023, Complete Volum