Distributed systems and mobile computing / / Giovanni Viglietta.

• VerfasserIn: Viglietta, Giovanni,
• Place / Publishing House: Basel : : MDPI - Multidisciplinary Digital Publishing Institute,, [2022]; ©2022
• Year of Publication: 2022
• Language: English
• Physical Description: 1 online resource (112 pages)

The book is about Distributed Systems and Mobile Computing. This is a branch of Computer Science devoted to the study of systems whose components are in different physical locations and have limited communication capabilities. Such components may be static, often organized in a network, or may be able to move in a discrete or continuous environment. The theoretical study of such systems has applications ranging from swarms of mobile robots (e.g., drones) to sensor networks, autonomous intelligent vehicles, the Internet of Things, and crawlers on the Web. The book includes five articles. Two of them are about networks: the first one studies the formation of networks by agents that interact randomly and have the ability to form connections; the second one is a study of clustering models and algorithms. The three remaining articles are concerned with autonomous mobile robots operating in continuous space. One article studies the classical gathering problem, where all robots have to reach a common location, and proposes a fast algorithm for robots that are endowed with a compass but have limited visibility. The last two articles deal with the evacuations problem, where two robots have to locate an exit point and evacuate a region in the shortest possible time.

Table of Contents

About the Editor -- Preface to "Distributed Systems and Mobile Computing" -- On the Distributed Construction of Stable Networks in Polylogarithmic Parallel Time -- Clustering Optimization of LoRa Networks for Perturbed Ultra-Dense IoT Networks -- Time-Optimal Gathering under Limited Visibility with One-Axis Agreement -- Robot Evacuation on a Line Assisted by a Bike -- A Multi-Objective Optimization Problem on Evacuating 2 Robots from the Disk in the Face-to-Face Model; Trade-Offs between Worst-Case and Average-Case Analysis.