A Survey of Computation Offloading for Mobile Systems

By Karthik Kumar, Jibang Liu, Yung-Hsiang Lu, Bharat K. Bhargava. Journal on spesial topics in mobile networks and applications, 2012

896 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/562836843897bcb98801838b44f3e5ff09fe212a

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In Computer Networks and Communications

In Information Systems

Citations in the corpus

A Survey on Mobile Augmented Reality With 5G Mobile Edge Computing: Architectures, Applications, and Technical Aspects (2021, background): “The second approach is to offload the computations to an external entity [219], which is the aspect this paper focuses.”
A Survey of Wearable Devices and Challenges (2017, background): “tional tasks to more energy efficient hardware or infrastructure have been widely proposed in the literature as a means of reducing energy consumption in wearables [251].”
Edge Computing for the Internet of Things: A Case Study (2018, background): “, processing-heavy tasks are sent for execution in the cloud which, in turn, sends the results back to the devices [38].”
Heterogeneity in Mobile Cloud Computing: Taxonomy and Open Challenges (2014, background): “However, cloudbased application offloading cannot always save energy with current developments and demands further efforts [6], [60], [61].”
Edge-computing-driven Internet of Things: A Survey (2022, background): “The ECDriven-IoT has many hardware and protocol problems, so the computing capability of edge nodes is limited and how to compute efficiently is still a tricky challenge in the ECDrivenIoT [90, 104].”
Delay-optimal computation task scheduling for mobile-edge computing systems (2016, background): “, the device energy consumption and the exe cution delay, could be greatly improved [1], [2].”
Joint Offloading and Trajectory Design for UAV-Enabled Mobile Edge Computing Systems (2019, background): “Mobile edge computing (MEC) appeared as a prospective solution has received great attention [3].”
Federated Learning in Edge Computing: A Systematic Survey (2022, methodology): “In addition, the authors of [67] investigated the techniques for computational offloading.”
Mobile Cloud Computing: A Survey, State of Art and Future Directions (2013, methodology): “[5] indicates that, in the last fifteen years, research focus has moved from making offloading feasible to more intelligent techniques for making offloading decisions (e.”
Dynamic Computation Offloading for Mobile-Edge Computing With Energy Harvesting Devices (2016, background): “In contrast, MEC has the potential to significantly reduce latency, avoid congestion and prolong the battery lifetime of mobile devices, by offloading the computation tasks from the mobile devices to a physically proximate MEC server [5]–[7].”
Survey of Fog Computing: Fundamental, Network Applications, and Research Challenges (2018, background): “moving the computation over the network also consumes energy [187].”
A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future (2020, background): “In this regard, efficient computational offloading will be a necessity [46], [47].”
Mobile-Edge Computing: Partial Computation Offloading Using Dynamic Voltage Scaling (2016, background): “2599530 mobile devices to powerful computing platforms in network edge [1]–[3].”