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Advanced Edge Intelligence Collaborative Technology over Wireless Communications

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: closed (15 December 2024) | Viewed by 15236

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Special Issue Editors

Prof. Dr. Changqiao Xu

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Guest Editor

School of Computer Science, Beijing University of Post and Telecommunication, Beijing 100876, China
Interests: heterogeneous network transmission; network architecture design

Dr. Tengfei Cao

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Department of Computer Technology and Application, Qinghai University, Xining 810016, China
Interests: edge computing; privacy protection over wireless communications

Dr. Hao Hao

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Shandong Computer Science Center (National Supercomputing Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
Interests: edge caching; edge computing; reinforcement learning

Dr. Xingyan Chen

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Financial Intelligence and Financial Engineering Key Laboratory of Sichuan Province, School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu, China
Interests: multimedia communications; content delivery; multi-agent reinforcement learning

Dr. Han Xiao

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State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: edge intelligence; AI communication
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Special Issue Information

Dear Colleagues,

The combination of artificial intelligence and edge computing gives birth to edge intelligence. Edge intelligence helps to solve the key problem of artificial intelligence landing in the "last mile". It has broad application prospects in the industrial Internet of Things, smart cities, unmanned driving, epidemic prevention and control, and other fields, and has attracted high attention from governments, industry, and academia. Specifically, how to effectively carry out the model training and reasoning of artificial intelligence in the resource-limited edge network is the current international frontier academic hotspot. With the 10 billion level of edge device networking in the future and the continuous upgrading of edge device computing power, it has become one of the most potent ways to achieve AI model training and reasoning at the edge by using various kinds of collaboration, such as cloud-end collaboration, edge-end collaboration, cloud-edge end collaboration, and end-end collaboration. Additionally, it is still in the early stage of development, there are still many frontier problems to be solved, and research opportunities and challenges coexist.

In order to promote the research and application of edge intelligence and report the latest achievements and progress in the architecture, models, algorithms, application practices, and other aspects of edge intelligence in a timely, centralized, and comprehensive manner, Electronics plans to publish the column "Advanced Edge Intelligence Collaborative Technology over Wireless Communications" in the second issue of 2023, hoping to provide a platform for experts and scholars in related fields to exchange, cooperate, and publish the latest frontier scientific research achievements, promoting the deep integration of academia and industry. Experts, scholars, and researchers in relevant fields are welcome to contribute actively!

The topics are listed as follows:

Collaborative privacy protection in wireless networks;
Federal learning and privacy protection;
Credibility driven in collaborative game optimization;
Cooperation caching optimization;
AoI optimization for computation offloading;
Multi-agent reinforcement learning;
Federal learning incentive mechanism;
Federated learning and cloud edge collaboration;
Intelligent-distributed drone video processing;
Wireless network optimization for advanced edge intelligence;
Advanced edge intelligence for immersive communications;
Advanced edge intelligence in emerging applications, such as the Internet of things, autonomous vehicle systems, intelligent reflecting surfaces, and virtual reality systems.

Prof. Dr. Changqiao Xu
Dr. Tengfei Cao
Dr. Hao Hao
Dr. Xingyan Chen
Dr. Han Xiao
Guest Editors

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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.

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Keywords

edge computing
wireless network
federal learning
intelligent network
collaborative optimization

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Published Papers (10 papers)

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Research

21 pages, 724 KiB

Open AccessArticle

Network Traffic Prediction in an Edge–Cloud Continuum Network for Multiple Network Service Providers

by Ying Hu, Ben Liu, Jianyong Li, Liang Zhu, Jihui Han, Zengyu Cai and Jie Zhang

Electronics 2024, 13(17), 3515; https://doi.org/10.3390/electronics13173515 - 4 Sep 2024

Viewed by 797

Abstract

Network function virtualization (NFV) allows the dynamic configuration of virtualized network functions to adapt services to complex and real-time network environments to improve network performance. The dynamic nature of physical networks creates significant challenges for virtual network function (VNF) migration and energy consumption, especially in edge–cloud continuum networks. This challenge can be addressed by predicting network traffic and proactively migrating VNFs using the predicted values. However, historical network traffic data are held by network service providers, and different network service providers are reluctant to share historical data due to privacy concerns; in addition, network resource providers that own the underlying networks are unable to effectively predict network traffic. To address this challenge, we apply a federated learning (FL) framework to enable network resource providers to no longer need historical network traffic data to be able to effectively predict network traffic. Further, to enable the predicted network traffic to lead to better migration effects, such as reducing the number of migrations, decreasing energy consumption, and increasing the request acceptance rate, we apply the predicted values of the network traffic to the network environment and feed the migration results of the network environment on the multiple factors described above to the neural network model. To obtain the migration results of the network environment, we analyzed and developed mathematical models for edge–cloud continuum networks with multiple network service providers. The effectiveness of our algorithm is evaluated through extensive simulations, and the results show a significant reduction in the number of migrated nodes and energy consumption, as well as an increase in the acceptance rate of the service function chain (SFC), compared with the commonly used scheme that uses only the difference between the predicted and actual traffic to define the loss function. Full article

(This article belongs to the Special Issue Advanced Edge Intelligence Collaborative Technology over Wireless Communications)

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17 pages, 1357 KiB

Open AccessArticle

Network Traffic Classification Model Based on Spatio-Temporal Feature Extraction

by Cheng Wang, Wei Zhang, Hao Hao and Huiling Shi

Electronics 2024, 13(7), 1236; https://doi.org/10.3390/electronics13071236 - 27 Mar 2024

Cited by 1 | Viewed by 1403

Abstract

The demand for encrypted communication is increasing with the continuous development of secure and trustworthy networks. In edge computing scenarios, the requirement for data processing security is becoming increasingly high. Therefore, the accurate identification of encrypted traffic has become a prerequisite to ensure edge intelligent device security. Currently, encrypted network traffic classification relies on single-feature extraction methods. These methods have simple feature extraction, making distinguishing encrypted network data flows and designing compelling manual features challenging. This leads to low accuracy in multi-classification tasks involving encrypted network traffic. This paper proposes a hybrid deep learning model for multi-classification tasks to address this issue based on the synergy of dilated convolution and gating unit mechanisms. The model comprises a Gated Dilated Convolution (GDC) module and a CA-LSTM module. The GDC module completes the spatial feature extraction of encrypted network traffic through dilated convolution and gating unit mechanisms. In contrast, the CA-LSTM module focuses on extracting temporal network traffic features. By employing a collaborative approach to extract spatio-temporal features, the model ensures feature extraction diversity, guarantees robustness, and effectively enhances the feature extraction rate. We evaluate our multi-classification model using the ISCX VPN-nonVPN public dataset. Experimental results show that the proposed method achieves an accuracy rate of over 95% and a recall rate of over 90%, significantly outperforming existing methods. Full article

(This article belongs to the Special Issue Advanced Edge Intelligence Collaborative Technology over Wireless Communications)

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20 pages, 1308 KiB

Open AccessArticle

Distributed Multi-Agent Approach for Achieving Energy Efficiency and Computational Offloading in MECNs Using Asynchronous Advantage Actor-Critic

by Israr Khan, Salman Raza, Razaullah Khan, Waheed ur Rehman, G. M. Shafiqur Rahman and Xiaofeng Tao

Electronics 2023, 12(22), 4605; https://doi.org/10.3390/electronics12224605 - 10 Nov 2023

Cited by 2 | Viewed by 1502

Abstract

Mobile edge computing networks (MECNs) based on hierarchical cloud computing have the ability to provide abundant resources to support the next-generation internet of things (IoT) network, which relies on artificial intelligence (AI). To address the instantaneous service and computation demands of IoT entities, AI-based solutions, particularly the deep reinforcement learning (DRL) strategy, have been intensively studied in both the academic and industrial fields. However, there are still many open challenges, namely, the lengthening convergence phenomena of the agent, network dynamics, resource diversity, and mode selection, which need to be tackled. A mixed integer non-linear fractional programming (MINLFP) problem is formulated to maximize computing and radio resources while maintaining quality of service (QoS) for every user’s equipment. We adopt the advanced asynchronous advantage actor-critic (A3C) approach to take full advantage of distributed multi-agent-based solutions for achieving energy efficiency in MECNs. The proposed approach, which employs A3C for computing offloading and resource allocation, is shown through numerical results to significantly reduce energy consumption and improve energy efficiency. This method’s effectiveness is further shown by comparing it to other benchmarks. Full article

(This article belongs to the Special Issue Advanced Edge Intelligence Collaborative Technology over Wireless Communications)

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18 pages, 2194 KiB

Open AccessArticle

Differential Privacy-Based Spatial-Temporal Trajectory Clustering Scheme for LBSNs

by Liang Zhu, Tingting Lei, Jinqiao Mu, Jingzhe Mu, Zengyu Cai and Jianwei Zhang

Electronics 2023, 12(18), 3767; https://doi.org/10.3390/electronics12183767 - 6 Sep 2023

Cited by 2 | Viewed by 1234

Abstract

Location privacy preserving for location-based social networks (LBSNs) has been attracting a great deal of attention. Existing location privacy protection methods are disadvantaged by issues such as information leakage and low data availability, which are no longer suitable for the current diverse and personalized location-based services. To address these issues, we propose a differential privacy-based spatial-temporal trajectory clustering (DP-STTC) scheme, which mainly transforms the existing location privacy protection mechanism into a spatial-temporal trajectory protection mechanism by adjusting the privacy parameters. Then, the trajectories were clustered to uncover users with similar trajectory characteristics. Finally, experiments were conducted on two real datasets. The experimental results show that our DP-STTC scheme can not only achieve better accuracy in trajectory clustering, but also protect user privacy. Full article

(This article belongs to the Special Issue Advanced Edge Intelligence Collaborative Technology over Wireless Communications)

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15 pages, 1150 KiB

Open AccessArticle

Seismic Data Query Algorithm Based on Edge Computing

by Tenglong Quan, Huifeng Zhang, Yonghao Yu, Yongwei Tang, Fushun Liu and Hao Hao

Electronics 2023, 12(12), 2728; https://doi.org/10.3390/electronics12122728 - 19 Jun 2023

Cited by 3 | Viewed by 1407

Abstract

Edge computing can reduce the transmission pressure of wireless networks in earthquakes by pushing computing functionalities to network edges and avoiding the data transmission to cloud servers. However, this also leads to the scattered storage of data content in each edge server, increasing the difficulty of content search. This paper investigates the seismic data query problem supported by edge computing. We first design a lookup mechanism based on bloom filter, which can quickly determine if there is the information that we need on a particular edge server. Then, the MEC-based data query problem is formulated as an optimization problem whose goal is to minimize the long-term average task delay with the constraints of computing capacity of edge servers. To reduce the complexity of problem, we further transform it as a Markov Decision Process by defining state space, action space and reward function. A novel DQN-based seismic data query algorithm is proposed to solve problem effectively. Extensive simulation-based testing shows that the proposed algorithm performances better when compared with two state-of-the-art solutions. Full article

(This article belongs to the Special Issue Advanced Edge Intelligence Collaborative Technology over Wireless Communications)

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24 pages, 1019 KiB

Open AccessArticle

A Novel Video-Spreading Strategy Based on the Joint Estimation of Social Influence and Sharing Capacity in Wireless Networks

by Shijie Jia, Xiaoyan Su and Zongzheng Liang

Electronics 2023, 12(10), 2214; https://doi.org/10.3390/electronics12102214 - 12 May 2023

Viewed by 1179

Abstract

In this paper, we propose a novel video-sharing strategy based on the joint estimation of social influence and sharing capacity in wireless networks (SSISC), which promotes the scale and efficiency of video spread and ensures the balance of supply and demand. SSISC designs an estimation model of video-sharing gains by investigating social influence levels, sharing capacities (including capacities of information dispatching and video delivery), and predicted expansion scale. Some social parameters (e.g., centrality of degree and betweenness, and average shortest distance) and some parameters of sharing performance (e.g., number of forwarded messages and cached videos, the average time of transmission and freeze) are used to evaluate social influence, capacities of information dispatching, and video delivery; video interest levels, social relationship levels, and historical push success rates are used to predict video proliferation scale. A video-spreading strategy based on the assistance of spread nodes is designed, which controls the process of video push according to available bandwidth and push priority to balance supply and demand and ensure user experience quality. Extensive tests show how SSISC achieves much better performance results in comparison with other state-of-the-art solutions. Full article

(This article belongs to the Special Issue Advanced Edge Intelligence Collaborative Technology over Wireless Communications)

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27 pages, 1529 KiB

Open AccessArticle

A Novel Epidemic-Based Video Diffusion Strategy Using Awareness of Sociality and Mobility in Wireless Networks

by Shijie Jia, Ruiling Zhang, Xiaoyan Su and Liuke Liang

Electronics 2023, 12(6), 1305; https://doi.org/10.3390/electronics12061305 - 9 Mar 2023

Cited by 2 | Viewed by 1243

Abstract

Social networks open up a new channel of video sharing and promote the scale and efficiency of video diffusion. Adjustable and scalable video diffusion is significant for the quality and performance of the service of video systems. In this paper, we propose a novel epidemic-based video diffusion strategy using awareness of sociality and mobility in wireless networks (EVDSM). EVDSM constructs a video diffusion model with the consideration of interest preference, social influence, and user mobility according to the roles and the propagation process of the Epidemic model. EVDSM designs an estimation method of interest preference according to content similarity and preference discrimination between videos; EVDSM designs an estimation method of user roles by investigation of interest preference and social influence to identify the video sharing behaviors of users and define the roles of users; EVDSM designs an estimation method of user mobility in terms of data transmission time and path structure stability. EVDSM proposes a control strategy of video diffusion, which formulates priority-based pairing between infectors and candidate infectors to achieve joint optimization of pairing success rate and delivery performance. The simulation results show how EVDSM achieves much better performance results in comparison with other state-of-the-art solutions. Full article

(This article belongs to the Special Issue Advanced Edge Intelligence Collaborative Technology over Wireless Communications)

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27 pages, 1442 KiB

Open AccessArticle

A Novel Video Propagation Strategy Fusing User Interests and Social Influences Based on Assistance of Key Nodes in Social Networks

by Shijie Jia, Tianyin Wang, Xiaoyan Su and Liuke Liang

Electronics 2023, 12(3), 532; https://doi.org/10.3390/electronics12030532 - 19 Jan 2023

Cited by 2 | Viewed by 1513

Abstract

Accurate video launching and propagation is significant for promotion and distribution of videos. In this paper, we propose a novel video propagation strategy that fuses user interests and social influences based on the assistance of key nodes in social networks (VPII). VPII constructs an estimation model for video distribution capacities in the process of video propagation by investigating interest preference and influence of social users: (1) An estimation method of user preferences for video content is designed by integrating a comparative analysis between current popular videos and historical popular videos. (2) An estimation method to determine the distribution capacities of videos is designed according to scale and importance of neighbor nodes covered. VPII further designs a multi-round video propagation strategy with the assistance of the selected key nodes, which enables these nodes to implement accurate video launching by estimating weighted levels based on available bandwidth and node degree centrality. The video propagation can effectively promote the scale and speed of video sharing and efficiently utilize network resources. Simulations-based testing shows how VPII outperforms other state-of-the-art solutions in terms of startup delay, caching hit ratio, caching cost and higher control overhead. Full article

(This article belongs to the Special Issue Advanced Edge Intelligence Collaborative Technology over Wireless Communications)

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16 pages, 3433 KiB

Open AccessArticle

Knowledge-Driven Location Privacy Preserving Scheme for Location-Based Social Networks

by Liang Zhu, Xiaowei Liu, Zhiyong Jing, Liping Yu, Zengyu Cai and Jianwei Zhang

Electronics 2023, 12(1), 70; https://doi.org/10.3390/electronics12010070 - 24 Dec 2022

Cited by 3 | Viewed by 2128

Abstract

Location privacy-preserving methods for location-based services in mobile communication networks have received great attention. Traditional location privacy-preserving methods mostly focus on the researches of location data analysis in geographical space. However, there is a lack of studies on location privacy preservation by considering the personalized features of users. In this paper, we present a Knowledge-Driven Location Privacy Preserving (KD-LPP) scheme, in order to mine user preferences and provide customized location privacy protection for users. Firstly, the UBPG algorithm is proposed to mine the basic portrait. User familiarity and user curiosity are modelled to generate psychological portrait. Then, the location transfer matrix based on the user portrait is built to transfer the real location to an anonymous location. In order to achieve customized privacy protection, the amount of privacy is modelled to quantize the demand of privacy protection of target user. Finally, experimental evaluation on two real datasets illustrates that our KD-LPP scheme can not only protect user privacy, but also achieve better accuracy of privacy protection. Full article

(This article belongs to the Special Issue Advanced Edge Intelligence Collaborative Technology over Wireless Communications)

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23 pages, 1373 KiB

Open AccessArticle

A Novel Geo-Social-Aware Video Edge Delivery Strategy Based on Modeling of Social-Geographical Dynamic in an Urban Area

by Shijie Jia, Yan Cui and Ruiling Zhang

Electronics 2022, 11(24), 4230; https://doi.org/10.3390/electronics11244230 - 19 Dec 2022

Viewed by 1323

Abstract

Social networks change the way and approaches of video spread and promote range and speed of video spread, which results in frequent traffic blowout and a heavy load on the networks. The social and geographical communication efficiency determines the efficiency of video sharing, which enables the eruptible traffic to be offloaded in underlaying networks to relieve the load of networks and ensure the user quality of the experience. In this paper, we propose a novel geo-social-aware video edge delivery strategy based on the modeling of the social-geographical dynamic in urban area (GSVD). By investigating the frequency of sharing behaviors, social communication efficiency, and efficiency of social sub-network consisting of one-hop social neighbors of users, GSVD estimates the interactive and basic social relationship to calculate the closeness of the social relationship between mobile users. GSVD makes use of grid partition and coding subarea to express the geographical location of mobile users and designs a calculation method of coding-based geographical distance. GSVD considers the dynamic update of social distance and geographical location and designs a measurement of video delivery quality in terms of delivery delay and playback continuity. A strategy of video delivery with the consideration of adapting to social-geographical dynamic is designed, which effectively promotes the efficiency of video sharing. Extensive tests show how GSVD achieves much better performance results in comparison with other state of the art solutions. Full article

(This article belongs to the Special Issue Advanced Edge Intelligence Collaborative Technology over Wireless Communications)

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