Networking for Cloud Robotics: The DewROS Platform and Its Application
With the advances in networking technologies, robots can use the almost unlimited resources of large data centers, overcoming the severe limitations imposed by onboard resources: this is the vision of Cloud Robotics. In this context, we present DewROS, a framework based on the Robot Operating System...
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Format: | Article |
Language: | English |
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MDPI AG
2021-06-01
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Series: | Journal of Sensor and Actuator Networks |
Subjects: | |
Online Access: | https://www.mdpi.com/2224-2708/10/2/34 |
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author | Alessio Botta Jonathan Cacace Riccardo De Vivo Bruno Siciliano Giorgio Ventre |
author_facet | Alessio Botta Jonathan Cacace Riccardo De Vivo Bruno Siciliano Giorgio Ventre |
author_sort | Alessio Botta |
collection | DOAJ |
description | With the advances in networking technologies, robots can use the almost unlimited resources of large data centers, overcoming the severe limitations imposed by onboard resources: this is the vision of Cloud Robotics. In this context, we present DewROS, a framework based on the Robot Operating System (ROS) which embodies the three-layer, Dew-Robotics architecture, where computation and storage can be distributed among the robot, the network devices close to it, and the Cloud. After presenting the design and implementation of DewROS, we show its application in a real use-case called SHERPA, which foresees a mixed ground and aerial robotic platform for search and rescue in an alpine environment. We used DewROS to analyze the video acquired by the drones in the Cloud and quickly spot signs of human beings in danger. We perform a wide experimental evaluation using different network technologies and Cloud services from Google and Amazon. We evaluated the impact of several variables on the performance of the system. Our results show that, for example, the video length has a minimal impact on the response time with respect to the video size. In addition, we show that the response time depends on the Round Trip Time (RTT) of the network connection when the video is already loaded into the Cloud provider side. Finally, we present a model of the annotation time that considers the RTT of the connection used to reach the Cloud, discussing results and insights into how to improve current Cloud Robotics applications. |
first_indexed | 2024-03-10T10:26:15Z |
format | Article |
id | doaj.art-529ac9e88c604706996c12fd94144334 |
institution | Directory Open Access Journal |
issn | 2224-2708 |
language | English |
last_indexed | 2024-03-10T10:26:15Z |
publishDate | 2021-06-01 |
publisher | MDPI AG |
record_format | Article |
series | Journal of Sensor and Actuator Networks |
spelling | doaj.art-529ac9e88c604706996c12fd941443342023-11-21T23:59:59ZengMDPI AGJournal of Sensor and Actuator Networks2224-27082021-06-011023410.3390/jsan10020034Networking for Cloud Robotics: The DewROS Platform and Its ApplicationAlessio Botta0Jonathan Cacace1Riccardo De Vivo2Bruno Siciliano3Giorgio Ventre4Department of Electrical Engineering and Information Technology, University of Naples Federico II, Via Claudio 21, 80125 Naples, ItalyDepartment of Electrical Engineering and Information Technology, University of Naples Federico II, Via Claudio 21, 80125 Naples, ItalyDepartment of Electrical Engineering and Information Technology, University of Naples Federico II, Via Claudio 21, 80125 Naples, ItalyDepartment of Electrical Engineering and Information Technology, University of Naples Federico II, Via Claudio 21, 80125 Naples, ItalyDepartment of Electrical Engineering and Information Technology, University of Naples Federico II, Via Claudio 21, 80125 Naples, ItalyWith the advances in networking technologies, robots can use the almost unlimited resources of large data centers, overcoming the severe limitations imposed by onboard resources: this is the vision of Cloud Robotics. In this context, we present DewROS, a framework based on the Robot Operating System (ROS) which embodies the three-layer, Dew-Robotics architecture, where computation and storage can be distributed among the robot, the network devices close to it, and the Cloud. After presenting the design and implementation of DewROS, we show its application in a real use-case called SHERPA, which foresees a mixed ground and aerial robotic platform for search and rescue in an alpine environment. We used DewROS to analyze the video acquired by the drones in the Cloud and quickly spot signs of human beings in danger. We perform a wide experimental evaluation using different network technologies and Cloud services from Google and Amazon. We evaluated the impact of several variables on the performance of the system. Our results show that, for example, the video length has a minimal impact on the response time with respect to the video size. In addition, we show that the response time depends on the Round Trip Time (RTT) of the network connection when the video is already loaded into the Cloud provider side. Finally, we present a model of the annotation time that considers the RTT of the connection used to reach the Cloud, discussing results and insights into how to improve current Cloud Robotics applications.https://www.mdpi.com/2224-2708/10/2/34Cloud Roboticscomputer networksnetwork performance |
spellingShingle | Alessio Botta Jonathan Cacace Riccardo De Vivo Bruno Siciliano Giorgio Ventre Networking for Cloud Robotics: The DewROS Platform and Its Application Journal of Sensor and Actuator Networks Cloud Robotics computer networks network performance |
title | Networking for Cloud Robotics: The DewROS Platform and Its Application |
title_full | Networking for Cloud Robotics: The DewROS Platform and Its Application |
title_fullStr | Networking for Cloud Robotics: The DewROS Platform and Its Application |
title_full_unstemmed | Networking for Cloud Robotics: The DewROS Platform and Its Application |
title_short | Networking for Cloud Robotics: The DewROS Platform and Its Application |
title_sort | networking for cloud robotics the dewros platform and its application |
topic | Cloud Robotics computer networks network performance |
url | https://www.mdpi.com/2224-2708/10/2/34 |
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