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Cited by 105IoT and Edge/Fog ComputingSoftware-Defined Networks and 5GEnergy Efficiency in Computing

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Computation Offloading Method Using Stochastic Games for Software-Defined-Network-Based Multiagent Mobile Edge Computing

Wu, Guowen, Wang, Hui, Zhang, Hong, Zhao, Yuhan, Yu, Shui, Shen, Shigen·2023·IEEE Internet of Things Journal
DOI 10.1109/jiot.2023.3277541
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Abstract

In the scenario of Industry 4.0, mobile smart devices (SDs) on production lines have to process massive amounts of data. These computing tasks sometimes far exceed the computing capability of SDs and require lots of energy and time to process. How to effectively reduce energy consumption and latency is necessary to be solved. To this end, we first propose a software-defined network (SDN)-based mobile edge computing (MEC) system. In the MEC system, SDs can offload computation tasks to edge servers to decrease the processing latency and avoid the waste of energy. At the same time, taking advantage of SDN’s programmability, scalability, and isolation of the control plane and the data plane, an SDN controller can manage edge devices within the MEC system. Second, based on a stochastic game, we study the computation offloading and resource allocation problems in the MEC system and establish a stochastic game-based computation offloading model. Furthermore, we prove that the multiuser stochastic game in this system can achieve Nash Equilibrium. We further consider each SD as an independent agent and design a stochastic game-based resource allocation algorithm with prioritized experience replays (SGRA-PERs) to minimize energy consumption and processing latency with Multiagent Reinforcement Learning. Experiment results demonstrate that the proposed SGRA-PER is superior to MADDPG,$Q$-Mix, and MAPPO algorithms, which can significantly reduce the processing delay and energy consumption…

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  • Bagaa et al. proposed an IoT security framework based on machine learning, realizing transfer of security threats through SDN and NFV, and tested it with a self-developed intrusion detection system.

    “BAGAAet al. [105] proposed an IoT security framework based on machine learning, which realized the transfer of security threats through the combination of SDN and NFV and tested the performance of the new model through a self-developed intrusion detection system.”

    From A Survey of IoT Privacy Security: Architecture, Technology, Challenges, and Trends (2024) · cites this paper as [105]

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Metadata

Authors
Wu, G., Wang, H., Zhang, H., Zhao, Y., Yu, S., & Shen, S.
Affiliations
  • Donghua University (CN)
  • University of Technology Sydney (AU)
  • Huzhou Normal University (CN)
ORCID
Venue
IEEE Internet of Things Journal
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2023-10-15
Volume
10
Issue
20
Pages
17620-17634
ISSN
  • 2327-4662 (ISSN-L)
  • 2372-2541
DOI
10.1109/jiot.2023.3277541
OpenAlex
W4377079722
Cited by
105
References
1
Citations by year
2026: 22 · 2025: 26 · 2024: 42 · 2023: 15
FWCI
11.68
Type
journal article
Language
en
Open access
closed
Licence
Locations
IEEE Internet of Things Journal (publishedVersion)
Funders
Zhejiang Provincial Natural Science Foundation of China (LZ22F020002)
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
  • Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
  • Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
Keywords
computation offloading, mobile edge computing, software-defined networking, stochastic games, multi-agent reinforcement learning, resource allocation, energy consumption, processing delay, Industry 4.0, smart devices, Nash equilibrium, prioritized experience replay, MADDPG, MAPPO

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