Table of Content

Artificial Intelligence for Mobile Edge Computing in IoT

Submission Deadline: 31 December 2022 Submit to Special Issue

Guest Editors

Prof. Lianyong Qi, Qufu Normal University, China
Prof. Kim-Kwang Raymond Choo, The University of Texas at San Antonio, USA
Dr. Xuyun Zhang, Macquarie University, Australia
Prof. Qiang Ni, Lancaster University, UK

Summary


With the progressive development of internet technologies and service-oriented applications, Internet of Things (IoT) plays an increasingly important role in industry 4.0. The IoT technology, which can connect billions of intelligent IoT devices, focuses on the inter-networking of various physical sensors, objects and people. Besides, motivated by the ever-increasing number of service-oriented applications and the ever-growing computing and storage capabilities, many efforts have been made to leverage external computing and storage resources to offload IoT devices computations/data to ease their computation burden and energy consumption. Among them, mobile edge computing (MEC) is a promising paradigm that powers service-oriented applications with the aim to promote the performance of real-time IoT applications.

 

IoT applications in the MEC environment will generate an unprecedented volume of data. How to handle such enormous data in an efficient, economical and secure manner is a fundamental challenge. Artificial Intelligence (AI), which can process the data generated by IoT devices at the edge networks, provides a powerful tool to deal with the analytics of IoT data in the MEC environment. However, the application of AI techniques to IoT applications in the MEC environment has yet to be investigated and explored to achieve their full potentials. In addition, security and privacy problems are also in great demand of research efforts in order to guarantee the broad applications of IoT in the MEC environment where IoT data are generated and processed at the edge networks in a distributed manner.

 

This special issue aims to share and discuss the recent advances and future trends of Artificial Intelligence for IoT applications in the MEC environment.

 

The topics of interest include, but are not limited to:

l  AI algorithms and theories for MEC in IoT

l  AI-based solutions for big data applications

l  Deep learning and online algorithms for energy efficient data sensing and processing in MEC

l  Service-oriented computing and networking systems analysis, modeling, simulation and application in MEC

l  Security, privacy and trust in MEC-based IoT applications

l  Automatic business process and resource/workflow management in MEC

l  IoT architecture, tools and applications for big data analytics

l  Integration algorithms of embedded devices in smart homes, smart traffic monitoring, smart health, smart education and smart manufacturing 




Published Papers


  • Open Access

    ARTICLE

    Edge Intelligence with Distributed Processing of DNNs: A Survey

    Sizhe Tang, Mengmeng Cui, Lianyong Qi, Xiaolong Xu
    CMES-Computer Modeling in Engineering & Sciences
    (This article belongs to this Special Issue: Artificial Intelligence for Mobile Edge Computing in IoT)
    Abstract With the rapid development of deep learning, the size of data sets and deep neural networks (DNNs) models are also booming. As a result, the intolerable long time for models’ training or inference with conventional strategies can not meet the satisfaction of modern tasks gradually. Moreover, devices stay idle in the scenario of edge computing (EC), which presents a waste of resources since they can share the pressure of the busy devices but they do not. To address the problem, the strategy leveraging distributed processing has been applied to load computation tasks from a single processor to a group of… More >

  • Open Access

    ARTICLE

    Genetic-Based Keyword Matching DBSCAN in IoT for Discovering Adjacent Clusters

    Byoungwook Kim, Hong-Jun Jang
    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1275-1294, 2023, DOI:10.32604/cmes.2022.022446
    (This article belongs to this Special Issue: Artificial Intelligence for Mobile Edge Computing in IoT)
    Abstract As location information of numerous Internet of Thing (IoT) devices can be recognized through IoT sensor technology, the need for technology to efficiently analyze spatial data is increasing. One of the famous algorithms for classifying dense data into one cluster is Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Existing DBSCAN research focuses on efficiently finding clusters in numeric data or categorical data. In this paper, we propose the novel problem of discovering a set of adjacent clusters among the cluster results derived for each keyword in the keyword-based DBSCAN algorithm. The existing DBSCAN algorithm has a problem in that… More >

    Graphic Abstract

    Genetic-Based Keyword Matching DBSCAN in IoT for Discovering Adjacent Clusters

  • Open Access

    ARTICLE

    Short Video Recommendation Algorithm Incorporating Temporal Contextual Information and User Context

    Weihua Liu, Haoyang Wan, Boyuan Yan
    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 239-258, 2023, DOI:10.32604/cmes.2022.022827
    (This article belongs to this Special Issue: Artificial Intelligence for Mobile Edge Computing in IoT)
    Abstract With the popularity of 5G and the rapid development of mobile terminals, an endless stream of short video software exists. Browsing short-form mobile video in fragmented time has become the mainstream of user’s life. Hence, designing an efficient short video recommendation method has become important for major network platforms to attract users and satisfy their requirements. Nevertheless, the explosive growth of data leads to the low efficiency of the algorithm, which fails to distill users’ points of interest on one hand effectively. On the other hand, integrating user preferences and the content of items urgently intensify the requirements for platform… More >

    Graphic Abstract

    Short Video Recommendation Algorithm Incorporating Temporal Contextual Information and User Context

  • Open Access

    REVIEW

    Intelligent Identification over Power Big Data: Opportunities, Solutions, and Challenges

    Liang Luo, Xingmei Li, Kaijiang Yang, Mengyang Wei, Jiong Chen, Junqian Yang, Liang Yao
    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1565-1595, 2023, DOI:10.32604/cmes.2022.021198
    (This article belongs to this Special Issue: Artificial Intelligence for Mobile Edge Computing in IoT)
    Abstract The emergence of power dispatching automation systems has greatly improved the efficiency of power industry operations and promoted the rapid development of the power industry. However, with the convergence and increase in power data flow, the data dispatching network and the main station dispatching automation system have encountered substantial pressure. Therefore, the method of online data resolution and rapid problem identification of dispatching automation systems has been widely investigated. In this paper, we perform a comprehensive review of automated dispatching of massive dispatching data from the perspective of intelligent identification, discuss unresolved research issues and outline future directions in this… More >

  • Open Access

    ARTICLE

    Intelligent Traffic Scheduling for Mobile Edge Computing in IoT via Deep Learning

    Shaoxuan Yun, Ying Chen
    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1815-1835, 2023, DOI:10.32604/cmes.2022.022797
    (This article belongs to this Special Issue: Artificial Intelligence for Mobile Edge Computing in IoT)
    Abstract Nowadays, with the widespread application of the Internet of Things (IoT), mobile devices are renovating our lives. The data generated by mobile devices has reached a massive level. The traditional centralized processing is not suitable for processing the data due to limited computing power and transmission load. Mobile Edge Computing (MEC) has been proposed to solve these problems. Because of limited computation ability and battery capacity, tasks can be executed in the MEC server. However, how to schedule those tasks becomes a challenge, and is the main topic of this piece. In this paper, we design an efficient intelligent algorithm… More >

  • Open Access

    REVIEW

    A Review of the Current Task Offloading Algorithms, Strategies and Approach in Edge Computing Systems

    Abednego Acheampong, Yiwen Zhang, Xiaolong Xu, Daniel Appiah Kumah
    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 35-88, 2023, DOI:10.32604/cmes.2022.021394
    (This article belongs to this Special Issue: Artificial Intelligence for Mobile Edge Computing in IoT)
    Abstract Task offloading is an important concept for edge computing and the Internet of Things (IoT) because computationintensive tasks must be offloaded to more resource-powerful remote devices. Task offloading has several advantages, including increased battery life, lower latency, and better application performance. A task offloading method determines whether sections of the full application should be run locally or offloaded for execution remotely. The offloading choice problem is influenced by several factors, including application properties, network conditions, hardware features, and mobility, influencing the offloading system’s operational environment. This study provides a thorough examination of current task offloading and resource allocation in edge… More >

  • Open Access

    ARTICLE

    Efficient UAV-Based MEC Using GPU-Based PSO and Voronoi Diagrams

    Mohamed H. Mousa, Mohamed K. Hussein
    CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.2, pp. 413-434, 2022, DOI:10.32604/cmes.2022.020639
    (This article belongs to this Special Issue: Artificial Intelligence for Mobile Edge Computing in IoT)
    Abstract Mobile-Edge Computing (MEC) displaces cloud services as closely as possible to the end user. This enables the edge servers to execute the offloaded tasks that are requested by the users, which in turn decreases the energy consumption and the turnaround time delay. However, as a result of a hostile environment or in catastrophic zones with no network, it could be difficult to deploy such edge servers. Unmanned Aerial Vehicles (UAVs) can be employed in such scenarios. The edge servers mounted on these UAVs assist with task offloading. For the majority of IoT applications, the execution times of tasks are often… More >

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