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Electronics
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Emerging and New Technologies in Mobile Edge Computing Networks
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Emerging and New Technologies in Mobile Edge Computing Networks

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 November 2024 | Viewed by 5361

Special Issue Editors

Dr. Mingjie Feng

E-Mail Website
Guest Editor
Associate Professor, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: wireless networks; heterogeneous networks; mmWave communications; massive MIMO; mobile edge computing
Dr. Yaohua Sun

E-Mail Website
Guest Editor
The State Key Laboratory of Networking and Switching Technology (SKL-NST), Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: AI-driven wireless networks; low-Earth-orbit satellite communication

Special Issue Information

Dear Colleagues,

In recent years, we have witnessed the emergence of various delay-sensitive mobile Internet of Things (IoT) applications (e.g., autonomous driving, augmented/virtual reality, and remote operation). Typically, these applications require the execution of computationally intensive tasks with stringent delay requirements. Meanwhile, with limited processing capability, mobile devices may not be able to timely execute these computational tasks. Mobile edge computing (MEC) is a promising solution to deal with this challenge. With computing servers deployed at the network edge (e.g., base station), mobile devices can offload computational tasks to these severs for fast processing. Due to these prospects, MEC is expected to play an important role in the sixth-generation (6G) mobile systems.

Although MEC exhibits great potential for supporting future IoT applications, various issues need to be addressed to unleash the full potential of MEC. This Special Issue seeks to collect innovative and original works on novel architecture, analysis, design, and prototypes for MEC. We welcome original research papers on topics including, but not limited to, the following:

  • Resource allocation;
  • Integration of sensing, communication, and computing;
  • Energy-efficient design;
  • Novel network architecture;
  • Computation offloading strategy;
  • Integration with other technologies;
  • Machine learning-based optimization;
  • Design and optimization for heterogenous tasks;
  • Cooperation mechanism;
  • Task partitioning and assignment;
  • Application in multimedia services;
  • Storage optimization;
  • Testbed and prototype;
  • Applications in 6G.

Dr. Mingjie Feng 
Dr. Yaohua Sun
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mobile edge computing
  • task offloading
  • resource allocation
  • sixth-generation (6G) mobile systems
  • Internet of Things (IoT)
  • delay-sensitive applications

Published Papers (6 papers)

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Research

18 pages, 2677 KiB  
Article
Deep Reinforcement Learning-Based Task Offloading and Load Balancing for Vehicular Edge Computing
by Zhoupeng Wu, Zongpu Jia, Xiaoyan Pang and Shan Zhao
Electronics 2024, 13(8), 1511; https://doi.org/10.3390/electronics13081511 - 16 Apr 2024
Viewed by 234
Abstract
Vehicular edge computing (VEC) effectively reduces the computational burden on vehicles by offloading tasks from resource-constrained vehicles to edge nodes. However, non-uniformly distributed vehicles offloading a large number of tasks cause load imbalance problems among edge nodes, resulting in performance degradation. In this [...] Read more.
Vehicular edge computing (VEC) effectively reduces the computational burden on vehicles by offloading tasks from resource-constrained vehicles to edge nodes. However, non-uniformly distributed vehicles offloading a large number of tasks cause load imbalance problems among edge nodes, resulting in performance degradation. In this paper, we propose a deep reinforcement learning-based decision scheme for task offloading and load balancing with the optimization objective of minimizing the system cost considering the split offloading of tasks and the load dynamics of edge nodes. First, we model the mutual interaction between mobile vehicles and Mobile Edge Computing (MEC) servers using a Markov decision process. Second, the optimal task-offloading and resource allocation decision is obtained by utilizing the twin delayed deep deterministic policy gradient algorithm (TD3), and server load balancing is achieved through edge collaboration using a server selection algorithm based on the technique for order preference by similarity to the ideal solution (TOPSIS). Finally, we have conducted extensive simulation experiments and compared the results with several other baseline schemes. The proposed scheme can more effectively reduce the system cost and increase the system resource utilization. Full article
(This article belongs to the Special Issue Emerging and New Technologies in Mobile Edge Computing Networks)
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16 pages, 3321 KiB  
Article
Research on Multi-DAG Satellite Network Task Scheduling Algorithm Based on Cache-Composite Priority
by Zhiguo Liu, Luxi Zhang, Lin Wang, Xiaoqi Dong and Junlin Rong
Electronics 2024, 13(4), 763; https://doi.org/10.3390/electronics13040763 - 15 Feb 2024
Viewed by 523
Abstract
The problem of multiple DAGs sharing satellite constellation resources has gradually attracted widespread attention. Due to the limited computing resources and energy consumption of satellite networks, it is necessary to formulate a reasonable multi-DAG task scheduling scheme to ensure the fairness of each [...] Read more.
The problem of multiple DAGs sharing satellite constellation resources has gradually attracted widespread attention. Due to the limited computing resources and energy consumption of satellite networks, it is necessary to formulate a reasonable multi-DAG task scheduling scheme to ensure the fairness of each workflow under the premise of considering latency and energy consumption. Therefore, in this paper, we propose a multi-DAG satellite network task scheduling algorithm based on cache-composite priority under the Software-Defined Networking satellite network architecture. The basic idea of this algorithm lies in the DAG selection phase, where not only are task priorities computed but also the concept of fair scheduling is introduced, so as to prevent the excessively delayed scheduling of low-priority DAG tasks. In addition, the concept of public subtasks is introduced to reduce the system overhead caused by repetitive tasks. The experimental results show that the hybrid scheduling strategy proposed in this paper can meet the demand of DAG scheduling and improve the degree of task completion while effectively reducing the task latency and energy consumption. Full article
(This article belongs to the Special Issue Emerging and New Technologies in Mobile Edge Computing Networks)
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32 pages, 2268 KiB  
Article
A Fast and Efficient Task Offloading Approach in Edge-Cloud Collaboration Environment
by Linyuan Liu, Haibin Zhu, Tianxing Wang and Mingwei Tang
Electronics 2024, 13(2), 313; https://doi.org/10.3390/electronics13020313 - 10 Jan 2024
Viewed by 797
Abstract
Edge-cloud collaboration fully utilizes the advantages of sufficient computing resources in cloud computing and the low latency of edge computing and better meets the needs of various Internet of Things (IoT) application scenarios. An important research challenge for edge-cloud collaboration is how to [...] Read more.
Edge-cloud collaboration fully utilizes the advantages of sufficient computing resources in cloud computing and the low latency of edge computing and better meets the needs of various Internet of Things (IoT) application scenarios. An important research challenge for edge-cloud collaboration is how to offload tasks to edge and cloud quickly and efficiently, taking into account different task characteristics, resource capabilities, and optimization objectives. To address the above challenge, we propose a fast and efficient task offloading approach in edge-cloud collaboration systems that can achieve a near-optimal solution with a low time overhead. First, it proposes an edge-cloud collaborative task offloading model that aims to minimize time delay and resource cost while ensuring the reliability requirements of the tasks. Then, it designs a novel Preprocessing-Based Task Offloading (PBTO) algorithm to quickly obtain a near-optimal solution to the Task Offloading problem in Edge-cloud Collaboration (TOEC) systems. Finally, we conducted extended simulation experiments to compare the proposed PBTO algorithm with the optimal method and two heuristic methods. The experimental results show that the total execution time of the proposed PBTO algorithm is reduced by 87.23%, while the total cost is increased by only 0.0004% compared to the optimal method. The two heuristics, although better than PBTO in terms of execution time, have much lower solution quality, e.g., their total costs are increased by 69.27% and 85.54%, respectively, compared to the optimal method. Full article
(This article belongs to the Special Issue Emerging and New Technologies in Mobile Edge Computing Networks)
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14 pages, 1075 KiB  
Article
Joint Optimization of Resource Utilization, Latency and UAV Trajectory in the Power Information Acquisition System
by Yong Xiao, Xin Jin, Boyang Huang, Junhao Feng and Zhengmin Kong
Electronics 2023, 12(18), 3861; https://doi.org/10.3390/electronics12183861 - 12 Sep 2023
Viewed by 703
Abstract
In order to reduce the peak–valley difference of the power grid load, reasonably arrange users’ electricity consumption time and realize the intelligent management of the power grid, we construct a user electricity consumption information acquisition system based on unmanned aerial vehicles (UAVs) by [...] Read more.
In order to reduce the peak–valley difference of the power grid load, reasonably arrange users’ electricity consumption time and realize the intelligent management of the power grid, we construct a user electricity consumption information acquisition system based on unmanned aerial vehicles (UAVs) by using a sensor network. In order to improve the service quality of the system and reduce the system delay, this paper comprehensively considers the factors that affect the user’s electricity consumption information collection system, such as the UAV trajectory, the unloading decision of the data receiving point and so on. Therefore, this paper puts forward an effective iterative optimization algorithm for joint UAV trajectory and unloading decisions based on a deep Q network (DQN), in order to obtain the optimal UAV trajectory and unloading decision design, acquire the optimal solution to minimize the time delay of the monitoring system and maximize the service quality of the user electricity information collection system, thus ensuring the stable operation of the user electricity information collection system. In this paper, different complexity algorithms are used to solve this problem. Compared with the greedy algorithm, the proposed algorithm, CDQN, improves the system service quality by approximately 2% and reduces the system delay by approximately 16%, so that the user’s electricity consumption information can be analyzed and processed faster. Full article
(This article belongs to the Special Issue Emerging and New Technologies in Mobile Edge Computing Networks)
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22 pages, 2335 KiB  
Article
Joint Latency-Oriented, Energy Consumption, and Carbon Emission for a Space–Air–Ground Integrated Network with Newly Designed Power Technology
by Yonghao Wang, Bo Li, Jiahao He, Jiaxing Dai, Yidong Liu and Yuxin Yang
Electronics 2023, 12(17), 3537; https://doi.org/10.3390/electronics12173537 - 22 Aug 2023
Viewed by 768
Abstract
Ubiquitous connectivity is envisaged for the space–air–ground-integrated network (SAGIN) of future communication to meet the needs of quality of service (QoS), green communication, and “dual carbon” targeting. However, the offloading and computation of massive latency-sensitive tasks dramatically increase the energy consumption of the [...] Read more.
Ubiquitous connectivity is envisaged for the space–air–ground-integrated network (SAGIN) of future communication to meet the needs of quality of service (QoS), green communication, and “dual carbon” targeting. However, the offloading and computation of massive latency-sensitive tasks dramatically increase the energy consumption of the network. To address these issues, we first propose a SAGIN architecture with energy-harvesting devices, where the base station (BS) is powered by both renewable energy (RE) and the conventional grid. The BS explores wireless power transfer (WPT) technology to power an unmanned aerial vehicle (UAV) for stable network operation. RE sharing between neighboring BSs is designed to fully utilize RE to reduce carbon emissions. Secondly, on the basis of task offloading decisions, the UAV trajectory, and the RE sharing ratio, we construct cost functions with joint latency-oriented, energy consumption, and carbon emission. Then, we develop a twin delayed deep deterministic policy gradient (TD3PG) algorithm based on deep reinforcement learning to minimize the cost function. Finally, simulation results demonstrate that the proposed algorithm outperforms the benchmark algorithm in terms of reducing latency, energy saving, and lower carbon emissions. Full article
(This article belongs to the Special Issue Emerging and New Technologies in Mobile Edge Computing Networks)
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15 pages, 1680 KiB  
Article
Smart Parking System Based on Edge-Cloud-Dew Computing Architecture
by Yuan-Chih Yu
Electronics 2023, 12(13), 2801; https://doi.org/10.3390/electronics12132801 - 25 Jun 2023
Cited by 4 | Viewed by 1554
Abstract
In a smart parking system, the license plate recognition service controls the car’s entry and exit and plays the core role in the parking lot system. When the Internet is interrupted, the parking lot’s business will also be interrupted. Hence, we proposed an [...] Read more.
In a smart parking system, the license plate recognition service controls the car’s entry and exit and plays the core role in the parking lot system. When the Internet is interrupted, the parking lot’s business will also be interrupted. Hence, we proposed an Edge-Cloud-Dew architecture for the mobile industry in order to tackle this critical problem. The architecture has an innovative design, including LAN-level deployment, Platform-as-a-Dew Service (PaaDS), the dew version of license plate recognition, and the dew type of machine learning model training. Based on these designs, the architecture presents many benefits, such as: (1) reduced maintenance and deployment issues and increased dew service reliability and sustainability; (2) effective release of the network constraint on cloud computing and increase in the horizontal and vertical scalability of the system; (3) enhancement of dew computing to resolve the heavy computing process problem; and (4) proposal of a dew type of machine learning training mechanism without requiring periodic retraining, but with acceptable accuracy. Finally, business owners can reduce their burdens when introducing machine learning technology. Our research goal is to make parking systems smarter in edge computing through the integration of cloud and dew architecture technology. Full article
(This article belongs to the Special Issue Emerging and New Technologies in Mobile Edge Computing Networks)
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