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Electronics
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New Challenges in Cloud–Fog–Edge Computing and Mobile Intelligence
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New Challenges in Cloud–Fog–Edge Computing and Mobile Intelligence

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (15 October 2023) | Viewed by 5022

Special Issue Editors

Dr. Huan Li

E-Mail Website
Guest Editor
College of Computer Science and Technology, Zhejiang University, Zheda Road 38, Hangzhou, China
Interests: Internet-of-Things; data management and query processing; edge computing; spatiotemporal data modelling
Dr. Dalin Zhang

E-Mail Website
Guest Editor
Department of Computer Science, Aalborg University, 9220 Aalborg, Denmark
Interests: deep learning; mobile computing; pervasive computing; Internet of Things; brain–computer interface; health informatics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xu Cheng

E-Mail Website
Guest Editor
Department of Manufacturing and Civil Engineering NTNU, Smart Innovation Norway, 1783 Halden, Norway
Interests: pattern recognition; application of artificial intelligence technology in water transportation systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Expansions in Internet-of-Things (IoT) and mobile devices are revolutionizing the paradigms for computing and intelligent services. To harness the massive distributed computing potential of IoT and mobile devices, researchers have been working to implement cloud–fog–edge computing architectures and artificial intelligence algorithms on these devices for innovative applications. In parallel with the continuous progress of related techniques, new concerns are also emerging, including the heterogeneity of computing nodes, fluctuations in computing resources, balancing energy consumption and quality of services, and safeguarding data security and user privacy.

The Electronics Special Issue on “New Challenges in Cloud–Fog–Edge Computing and Mobile Intelligence” aims to explore the cutting-edge research in this field. The focus of the issue is to address the most pressing and challenging problems oriented to cloud–fog–edge computing and mobile intelligence, including, but not limited to

  • architectures for energy- and resource-efficient cloud–fog–edge computing;
  • novel mobile intelligence algorithms and models;
  • big data management in cloud–fog–edge computing environments;
  • real-time data processing and analysis in cloud–fog–edge computing environments;
  • distributed machine learning in cloud–fog–edge computing environments;
  • model training and inference on edge and mobile devices;
  • context-awareness in IoT and mobile environments;
  • blockchain-based security and privacy in mobile intelligence;
  • secure and efficient data sharing in cloud–fog–edge computing systems;
  • emerging applications of smart wearable healthcare systems;
  • emerging applications of smart energy management systems;
  • emerging applications of smart traffic management systems.

We welcome submissions of original research articles, reviews, and case studies on these topics and others relevant. Submissions must not have been previously published or be under consideration for publication elsewhere. The articles will be peer-reviewed and selected based on their relevance, quality, and originality. The selected papers are expected to supplement existing literature by presenting new and innovative solutions to the major challenges in this field.

Dr. Huan Li
Dr. Dalin Zhang
Dr. Xu Cheng
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

  • cloud–fog–edge computing
  • mobile computing
  • Internet-of-Things (IoT)
  • big data processing
  • distributed machine learning
  • mobile intelligence algorithms
  • data security and privacy

Published Papers (4 papers)

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Research

14 pages, 1266 KiB  
Article
A Federated Network Intrusion Detection System with Multi-Branch Network and Vertical Blocking Aggregation
by Yunhui Wang, Weichu Zheng, Zifei Liu, Jinyan Wang, Hongjian Shi, Mingyu Gu and Yicheng Di
Electronics 2023, 12(19), 4049; https://doi.org/10.3390/electronics12194049 - 27 Sep 2023
Cited by 1 | Viewed by 868
Abstract
The rapid development of cloud–fog–edge computing and mobile devices has led to massive amounts of data being generated. Also, artificial intelligence technology, like machine learning and deep learning, is widely used to mine the value of the data. Specifically, detecting attacks on the [...] Read more.
The rapid development of cloud–fog–edge computing and mobile devices has led to massive amounts of data being generated. Also, artificial intelligence technology, like machine learning and deep learning, is widely used to mine the value of the data. Specifically, detecting attacks on the cloud–fog–edge computing system using mobile devices is essential. External attacks on network press organizations led to anomaly flow in network traffic. The network intrusion detection system (NIDS) has been an effective method for detecting anomaly flow. However, the NIDS is hard to deploy in distributed networks because network flow data are kept private. Existing methods cannot obtain an accurate NIDS under such a federated scenario. To construct an NIDS while preserving data privacy, we propose a combined model that integrates binary classifiers into a whole network based on simple classifier networks to specify the type of attack on anomalous data and offer instruction to other security system components. We also introduce federated learning (FL) methods into our system and design a new aggregation algorithm named vertical blocking aggregation (FedVB) according to our model structure. Our experiments demonstrate that our system can be more effective than simple multi-classifiers in terms of accuracy and significantly reduce communication and computation overhead when applying FedVB. Full article
(This article belongs to the Special Issue New Challenges in Cloud–Fog–Edge Computing and Mobile Intelligence)
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20 pages, 6339 KiB  
Article
Jointly Optimize Partial Computation Offloading and Resource Allocation in Cloud-Fog Cooperative Networks
by Wenle Bai and Ying Wang
Electronics 2023, 12(15), 3224; https://doi.org/10.3390/electronics12153224 - 26 Jul 2023
Cited by 2 | Viewed by 840
Abstract
Fog computing has become a hot topic in recent years as it provides cloud computing resources to the network edge in a distributed manner that can respond quickly to intensive tasks from different user equipment (UE) applications. However, since fog resources are also [...] Read more.
Fog computing has become a hot topic in recent years as it provides cloud computing resources to the network edge in a distributed manner that can respond quickly to intensive tasks from different user equipment (UE) applications. However, since fog resources are also limited, considering the number of Internet of Things (IoT) applications and the demand for traffic, designing an effective offload strategy and resource allocation scheme to reduce the offloading cost of UE systems is still an important challenge. To this end, this paper investigates the problem of partial offloading and resource allocation under a cloud-fog coordination network architecture, which is formulated as a mixed integer nonlinear programming (MINLP). Bring in a new weighting metric-cloud resource rental cost. The optimization function of offloading cost is defined as a weighted sum of latency, energy consumption, and cloud rental cost. Under the fixed offloading decision condition, two sub-problems of fog computing resource allocation and user transmission power allocation are proposed and solved using convex optimization techniques and Karush-Kuhn-Tucker (KKT) conditions, respectively. The sampling process of the inner loop of the simulated annealing (SA) algorithm is improved, and a memory function is added to obtain the novel simulated annealing (N-SA) algorithm used to solve the optimal value offloading problem corresponding to the optimal resource allocation problem. Through extensive simulation experiments, it is shown that the N-SA algorithm obtains the optimal solution quickly and saves 17% of the system cost compared to the greedy offloading and joint resource allocation (GO-JRA) algorithm. Full article
(This article belongs to the Special Issue New Challenges in Cloud–Fog–Edge Computing and Mobile Intelligence)
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17 pages, 5878 KiB  
Article
Inverse Dynamics Modeling and Simulation Analysis of Multi-Flexible-Body Spatial Parallel Manipulators
by Qingyun Zhang and Xinhua Zhao
Electronics 2023, 12(9), 2038; https://doi.org/10.3390/electronics12092038 - 28 Apr 2023
Viewed by 1183
Abstract
Taking a spatial parallel robot with flexible joints and links as the research object, a dynamic modeling method for a multi-flexible-body robot system is proposed. Its effectiveness is verified by comparing the numerical model with a simulation model. (1) Background: The elastic deformation [...] Read more.
Taking a spatial parallel robot with flexible joints and links as the research object, a dynamic modeling method for a multi-flexible-body robot system is proposed. Its effectiveness is verified by comparing the numerical model with a simulation model. (1) Background: The elastic deformation of the flexible joints and links in the multi-flexible-body spatial parallel robot under high-speed operation and the coupling effect between the flexible and the rigid components substantially affect the system stability and trajectory accuracy. Therefore, it is necessary to analyze the dynamic characteristics of multi-flexible-body robot systems by establishing accurate dynamic models. (2) Methods: First, the finite element method was used to discretize the flexible joints and links. Subsequently, according to the floating frame of the reference coordinate method, the deformation coordinates of the flexible joints and links were described. The first six modal information were retained to develop a dynamic model considering the coupling effect between the flexible joint and rigid link and between the flexible joint and link. Second, a dynamic model of the end-effector with small displacement changes was established according to the coordination matrix. Furthermore, a dynamic model of rigid links was established based on the vector method and combined with the higher-order dynamic model of flexible joints and links to form the dynamic model of multi-flexible-body kinematic chains. Finally, the dynamic model of the three kinematic chains was assembled with that of the end-effector to obtain an accurate dynamic model of the multi-flexible-body robot systems. (3) Results: The motion trajectory of the multi-flexible-body robot floats around the fully rigid spatial parallel robot in a certain range. Its range of travel in the x, y, and z directions was 0 to 3.14, 0 to 4.06, and 0 to 0.483 mm, respectively. With increasing angular velocity, the maximum absolute amplitude of the driving torque of each branch chain also increases, whereas its motion trend remains unchanged. (4) Conclusions: The proposed dynamic modeling method and its simulation model for multi-flexible-body robots are correct, which can lay a solid foundation for further control performance analysis. Full article
(This article belongs to the Special Issue New Challenges in Cloud–Fog–Edge Computing and Mobile Intelligence)
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13 pages, 3777 KiB  
Article
Tightly Coupled 3D Lidar Inertial SLAM for Ground Robot
by Daosheng Li, Bo Sun, Ruyu Liu and Ruilei Xue
Electronics 2023, 12(7), 1649; https://doi.org/10.3390/electronics12071649 - 31 Mar 2023
Cited by 1 | Viewed by 1427
Abstract
This paper proposes a robotic state estimation and map construction method. The traditional lidar SLAM methods are affected by sensor measurement noise, which causes the estimated trajectory to drift, especially along the altitude direction caused by lidar noise. In this paper, ground parameters [...] Read more.
This paper proposes a robotic state estimation and map construction method. The traditional lidar SLAM methods are affected by sensor measurement noise, which causes the estimated trajectory to drift, especially along the altitude direction caused by lidar noise. In this paper, ground parameters in the environment are extracted to construct the ground factors to compress the trajectory estimation drifting along the altitude direction using the characteristics of constant robot pose relative to the ground. Our method uses tightly coupled lidar and inertial to obtain low-drift lidar odometry factors by factor graph optimization. The optimized lidar odometry factors are then added to a global factor graph, together with ground, loop closure, and GPS factors to obtain accurate robot state estimation and mapping after factor graph optimization. The experimental results show that our method has comparable results with advanced lidar SLAM methods, and even performs better in some complex and large-scale environments. Full article
(This article belongs to the Special Issue New Challenges in Cloud–Fog–Edge Computing and Mobile Intelligence)
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