# Networking for Cloud Robotics: The DewROS Platform and Its Application
## 7 Discussion and Conclusions  
Cloud Robotics is emerging as a very promising research field and requires stronger competencies in Cloud Computing and Networking. In this paper, we provided a few contributions in this important research area, in terms of a platform that we developed for real applications and experimental analysis, which was performed thanks to its application in a real use case. The platform we developed is called DewROS and is based on the Dew Robotics paradigm--where the computations are split among the robot, more powerful devices close to it, and the Cloud--and on the Robot Operating System. This allows it to be deployed on real robots using ROS and talk to real Cloud services by Google and Amazon. We described the design and implementation choices backing DewROS and tested them in a real use case, provided by a European Research Project called SHERPA. SHERPA focuses on search and rescue in hostile environments using a fleet of robots (drones and rovers). Thanks to DewROS, we enhanced SHERPA with Cloud services and performed a large experimental campaign with the aim of studying the possibility of performing video  
Figure 19: Response time using the UNI, HOME and SAT network connections, in case of a pre-loaded video on Google Cloud Service server, with different video lengths (short and long).  
annotation in the Cloud using Google and Amazon services. Our experiments allow us to understand the effect of several variables, such as video size, length, network connection performance and Cloud service, on the time and quality of the annotations performed in the Cloud. We have shown that label detection mode and video length do not have a significant impact on the response time of Google Cloud Video Intelligence. We have also seen that this is not true for the byte size of the video to be analyzed. Furthermore, we have observed that the Internet connection characteristics currently in use have a high impact on the response time, and this also occurs when the video is already available in the Cloud. Moreover, we discovered and modeled the impact of the Round Trip Time of the network connection used to reach the Cloud on the video annotation time, even if videos are already available in the Cloud. We believe that this paper provides important and currently missing contributions to the research field of Cloud Robotics.  
Conceptualization, A.B.; Data curation, R.D.V.; Funding acquisition, B.S. and G.V.; Investigation, A.B. and R.D.V.; Resources, J.C.; Supervision, A.B., B.S. and G.V.; Writing-original draft, R.D.V.; Writing-review & editing, A.B. and J.C. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by MIUR through the \"ICT for Health\" project, Dipartimento di Eccellenza (2018-2022) \"Ingegneria Elettrica e delle Tecnologie dell'Informazione\" and by Cisco Systems through the Sponsored Research Agreement \"Research Project for Industry 4.0\".  
Not applicable.  
The data underlying this article will be shared on reasonable request from the corresponding author.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} RTT & Round Trip Time \\ ROS & Robot Operating System \\ VM & Virtual Machine \\ UAV & Unmanned Aerial Vehicle \\ S\&R & Search and Rescue \\ IoT & Internet of Things \\ API & Application Program Interface \\ Wi-Fi & Wireless Fidelity or IEEE 802.11 \\ P2P & Peer-to-Peer \\ OpenCv & Open Computer Vision \\ GPS & Global Positioning System \\ CVI & Cloud Video Intelligence \\ AR & Amazon Rekognition \\ FTTH & Fiber To The Home \\ HD & High Definition \\ SD & Standard Definition \\ DC & Dew Computing \\ WLAN & IEEE 802.11 wireless LAN \\ WAN & Wide Area Network \\ GCS & Google Cloud Storage \\ \end{tabular}