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

Aamir Akbar*, Peter Lewis, Elizabeth Wanner

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Article number102
JournalFrontiers in Robotics and AI
Publication statusPublished - 5 Aug 2020

Bibliographical note

© 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.


  • code offloading
  • evolutionary algorithms
  • mobile-cloud hybrid (MCH) computing
  • multi-objective optimization (MOO)
  • robotics
  • self-adaptive
  • self-aware


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