University of Oulu

G. Lee, W. Saad, M. Bennis, C. Kim and M. Jung, "An Online Framework for Ephemeral Edge Computing in the Internet of Things," in IEEE Transactions on Wireless Communications, vol. 22, no. 3, pp. 1992-2007, March 2023, doi: 10.1109/TWC.2022.3208096

An online framework for ephemeral edge computing in the Internet of Things

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Author: Lee, Gilsoo1; Saad, Walid1; Bennis, Mehdi2;
Organizations: 1Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, USA
2Centre for Wireless Communications, University of Oulu, Oulu, Finland
3Wireless@VT, Department of Electrical and Computer Engineering, Arlington, VA, USA
4Department of Electronics and Information Engineering, Sejong University, Seoul, South Korea
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.6 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2023-04-27


In the Internet of Things (IoT) environment, edge computing can be initiated at anytime and anywhere. However, in an IoT environment, edge computing sessions are often ephemeral, i.e., they last for a short period of time and can often be discontinued once the current application usage is completed or the edge devices leave the system due to factors such as mobility. Therefore, in this paper, the problem of ephemeral edge computing in an IoT is studied by considering scenarios in which edge computing operates within a limited time period. To this end, a novel online framework is proposed in which a source edge node offloads its computing tasks from sensors within an area to neighboring edge nodes for distributed task computing, within the limited period of time of an ephemeral edge computing system. The online nature of the framework allows the edge nodes to optimize their task allocation and decide on which neighbors to use for task processing, even when the tasks are revealed to the source edge node in an online manner, and the information on future task arrivals is unknown. The proposed framework essentially maximizes the number of computed tasks by jointly considering the communication and computation latency. To solve the joint optimization, an online greedy algorithm is proposed and solved by using the primal-dual approach. Since the primal problem provides an upper bound of the original dual problem, the competitive ratio of the online approach is analytically derived as a function of the task sizes and the data rates of the edge nodes. Simulation results show that the proposed online algorithm can achieve a near-optimal task allocation with an optimality gap that is no higher than 7.1% compared to the offline, optimal solution with complete knowledge of all tasks.

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Series: IEEE transactions on wireless communications
ISSN: 1536-1276
ISSN-E: 1558-2248
ISSN-L: 1536-1276
Volume: 22
Issue: 3
Pages: 1992 - 2007
DOI: 10.1109/TWC.2022.3208096
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was supported in part by the USA National Science Foundation under Grant CNS-1814477; and in part by the Basic Science Research Program, National Research Foundation of Korea (NRF), Ministry of Education, under Grant NRF-2021R1C1C1012950.
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