A Self-Aware and Scalable Solution for Efficient Mobile-Cloud Hybrid Robotics


Backed by the virtually unbounded resources of the cloud, 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 (MCH) robotic tasks are inefficient in terms of optimizing trade-offs between simultaneously conflicting objectives, such as minimizing both battery power consumption and network usage. To tackle this problem we propose a novel approach that can be used not only to instrument an MCH robotic task but also to search for its efficient configurations representing compromise solution between the objectives. We introduce a general-purpose MCH framework to measure, at runtime, how well the tasks meet these two objectives. The framework employs these efficient configurations to make decisions at runtime, which are based on: (1) changing of the environment (i.e., WiFi signal level variation), and (2) itself in a changing environment (i.e., actual observed packet loss in the network). Also, we introduce a novel search-based multi-objective optimization (MOO) algorithm, which works in two steps to search for efficient configurations of MCH applications. Analysis of our results shows that: (i) using self-adaptive and self-aware decisions, an MCH foraging task performed by a battery-powered robot can achieve better optimization in a changing environment than using static offloading or running the task only on the robot. However, a self-adaptive decision would fall behind when the change in the environment happens within the system. In such a case, a self-aware system can perform well, in terms of minimizing the two objectives. (ii) The Two-Step algorithm can search for better quality configurations for MCH robotic tasks of having a size from small to medium scale, in terms of the total number of their offloadable modules.

Publication DOI: https://doi.org/10.3389/frobt.2020.00102
Divisions: College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
?? 50811700Jl ??
Additional Information: © 2020 Akbar, Lewis and Wanner. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Uncontrolled Keywords: NSGA-II,code offloading,evolutionary algorithms,mobile-cloud hybrid (MCH) computing,multi-objective optimization (MOO),robotics,self-adaptive,self-aware,Computer Science Applications,Artificial Intelligence
Last Modified: 11 Jun 2024 07:19
Date Deposited: 05 Aug 2020 07:54
Full Text Link:
Related URLs: https://www.fro ... .00102/abstract (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2020-08-05
Accepted Date: 2020-06-29
Authors: Akbar, Aamir
Lewis, Peter (ORCID Profile 0000-0003-4271-8611)
Wanner, Elizabeth (ORCID Profile 0000-0001-6450-3043)



Version: Published Version

License: Creative Commons Attribution

| Preview

Export / Share Citation


Additional statistics for this record