Paper Info
Title
Self-Adaptive and Self-Aware Mobile-Cloud Hybrid Robotics
Abstract
Many benefits of cloud computing are now well established, as both enterprise and mobile IT has been transformed by cloud computing. Backed by the virtually unbounded resources of cloud computing, battery-powered mobile robotics can also benefit from cloud computing, meeting the demands of even the most computationally and resource-intensive tasks. However, many existing mobile-cloud hybrid tasks are inefficient in terms of achieving objectives like minimizing battery power consumption and network bandwidth usage, which form a tradeoff. To counter this problem we propose a technique based on offline profiling, that allows class, method and hybrid level configurations to be applied to MC hybrid robotic tasks and measures, at runtime, how well the tasks meet these two objectives. The optimal configurations obtained from offline profiling are employed to make decisions at runtime. The decisions are based on: 1) changing the environment (i.e. WiFi signal level variation), and 2) itself in a changing environment (i.e. actual observed packet loss in the network). Our experimental evaluation considers a Python-based foraging task performed by a battery-powered and Raspberry Pi controlled Thymio robot. Analysis of our results shows that self-adaptive and self-aware systems can both achieve better optimization in a changing environment (signal level variation) than using static offloading or running the task only on a mobile device. However, a self-adaptive system struggles to perform well when the change in the environment happens within the system (network congestion). In such a case, a self-aware system can outperform, in terms of minimizing the two objectives.
Year
DOI
Venue
2018
10.1109/IoTSMS.2018.8554735
2018 Fifth International Conference on Internet of Things: Systems, Management and Security
Keywords
Field
DocType
battery-powered mobile robotics,self-aware mobile-cloud hybrid robotics,changing environment,signal level variation,MC hybrid robotic tasks,hybrid level configurations,offline profiling,battery power consumption,resource-intensive tasks,cloud computing
Computer science,Profiling (computer programming),Packet loss,Computer network,Mobile device,Bandwidth (signal processing),Network congestion,Artificial intelligence,Robot,Robotics,Distributed computing,Cloud computing
Conference
ISBN
Citations 
PageRank 
978-1-5386-9586-9
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Aamir Akbar1144.03
Peter R. Lewis225330.22