research-article

EECDN: Energy-efficient Cooperative DNN Edge Inference in Wireless Sensor Networks

Authors:

Jigang WuAuthors Info & Claims

ACM Transactions on Internet Technology, Volume 22, Issue 4

Article No.: 109, Pages 1 - 30

https://doi.org/10.1145/3544969

Published: 14 November 2022 Publication History

Abstract

Multi-access edge computing (MEC) is emerging to improve the quality of experience of mobile devices including internet of things sensors by offloading computing intensive tasks to MEC servers. Existing MEC-enabled cooperative computation offloading works focus on the optimization of total energy consumption but fail to exploit multi-relay diversity and min-max fairness of energy consumption on participated sensors. We explore a typical wireless sensor network with multi-source, multi-relay, and one edge server, where relay nodes can provide both cooperative communication and computation services. We divide the energy efficiency optimization problem into two sub-problems: One is to minimize the weighted average total energy consumption per time slot, and the other is to minimize the maximum weighted energy consumption. For the first sub-problem, we propose an optimal algorithm named as optimal weighted average total energy consumption algorithm (OTCA) based on bipartite matching. For the second sub-problem, greedy algorithm for fairness guarantee (GAF) is proposed with an approximation ratio of (1 + ε), where ε is a small positive constant. Extensive numerical results show that OTCA outperforms the baseline algorithms by 26.7–77.4% on the average total weighted energy consumption while GAF outperforms benchmark algorithms by 30.7–84.4%. NS-3 simulation experiments comply with numerical results.

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Cited By

Samtani SRaff EAnderson H(2024)Applied Machine Learning for Information SecurityDigital Threats: Research and Practice10.1145/36520295:1(1-5)Online publication date: 5-Apr-2024
https://dl.acm.org/doi/10.1145/3652029
Qu SZhao SLi BHe YCai XZhang LWang YTsafrir DMusuvathi MGupta RAbu-Ghazaleh N(2024)CIM-MLC: A Multi-level Compilation Stack for Computing-In-Memory AcceleratorsProceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 210.1145/3620665.3640359(185-200)Online publication date: 27-Apr-2024
https://dl.acm.org/doi/10.1145/3620665.3640359
Li HLi XFan QHe QWang XLeung V(2024)Distributed DNN Inference With Fine-Grained Model Partitioning in Mobile Edge Computing NetworksIEEE Transactions on Mobile Computing10.1109/TMC.2024.335787423:10(9060-9074)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1109/TMC.2024.3357874
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Index Terms

EECDN: Energy-efficient Cooperative DNN Edge Inference in Wireless Sensor Networks
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing theory, concepts and paradigms
      1. Mobile computing
2. Networks
  1. Network services
    1. Network management

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cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 22, Issue 4

November 2022

642 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3561988

Editor:
Ling Liu
TU Wien, Austria

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Publication History

Published: 14 November 2022

Online AM: 23 June 2022

Accepted: 14 June 2022

Revised: 03 April 2022

Received: 03 July 2021

Published in TOIT Volume 22, Issue 4

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Guangzhou Basic and Applied Basic Research Foundation
Guangzhou Municipal Science and Technology Bureau
Natural Science foundation of Guangdong Province, China
National Natural Science Foundation of China
Guangdong Basic and Applied Basic Research Foundation

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Qu SZhao SLi BHe YCai XZhang LWang YTsafrir DMusuvathi MGupta RAbu-Ghazaleh N(2024)CIM-MLC: A Multi-level Compilation Stack for Computing-In-Memory AcceleratorsProceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 210.1145/3620665.3640359(185-200)Online publication date: 27-Apr-2024
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Li HLi XFan QHe QWang XLeung V(2024)Distributed DNN Inference With Fine-Grained Model Partitioning in Mobile Edge Computing NetworksIEEE Transactions on Mobile Computing10.1109/TMC.2024.335787423:10(9060-9074)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1109/TMC.2024.3357874
Chang SDeng SWu YMa WZhou H(2023)Online Energy Balancing Strategy Based on Lyapunov Optimization in Mobile CrowdsensingIEEE Transactions on Industrial Informatics10.1109/TII.2022.322761819:9(9266-9279)Online publication date: Sep-2023
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