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Electronics
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Secure Distributed Computing and Learning for Future Internet of Things
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Secure Distributed Computing and Learning for Future Internet of Things

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

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 11847

Special Issue Editors

Prof. Dr. Wen Sun

E-Mail Website
Guest Editor
School of Cybersecurity, Northwestern Polytechnical University, Xi'an 710072, China
Interests: artificial intelligence; distributed intelligence; artificial intelligence security
Dr. Lexi Xu

E-Mail Website
Guest Editor
Research Institute, China Unicom, Beijing 100048, China
Interests: big data security; artificial intelligence; internet of things; mobile communication
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hui Xia

E-Mail Website
Guest Editor
College of Computer Science and Technology, Ocean University of China (OUC), Qingdao 266100, China
Interests: Internet of Things; IoT security; AI security; privacy protection and federated learning
Special Issues, Collections and Topics in MDPI journals
Dr. Libin Yang

E-Mail Website
Guest Editor
School of Cybersecurity, Northwestern Polytechnical University, Xi’an 710072, China
Interests: Internet of Things; cyber security; federated learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The future Internet of things (IoT) era is predicted to lead to an explosive proliferation of device connectivity; meanwhile, the future IoT aims to establish the comprehensive support of delay-sensitive and learning-aware services. Serving massive and widely distributed IoT devices, centralized computing undoubtedly faces difficulty regarding computing capability and network reliability. Unlike performing computing and storage in a cluster of clouds, distributed computing and learning emphasize leveraging local computing power and storage from a diverse range of nearby devices, i.e., local terminals and edge servers, to provide efficient learning and intelligence for applications including smart cities, automatic driving, intelligent transportation and disaster warning. Benefiting from distributed computing and learning, the future IoT will provide timely intelligence and high scalability by efficiently utilizing the computing capability and storage of IoT devices.

However, to comprehensively realize distributed computing and learning for the future IoT, significant challenges need to be addressed. First, efficient dispatching of computing and learning requires the orchestration and collaboration of all IoT devices. As diverse and heterogeneous IoT devices have different capabilities of computing, storage and communication, flexible and adaptive distributed computing and learning architecture needed to be adopted to satisfy corresponding tasks. Moreover, frequent data exchange causes unpredictable challenges for security and privacy. In traditional distributed computing architecture, computing nodes engage in frequent data exchange to synchronize the training model and parameters, prompting opportunistic data exposure to combat this issue. Secure distributed computing and learning is clearly needed for the future IoT. Existing IoT architecture should be addressed by employing state-of-the-art enabling techniques, such as federated learning and the blockchain.

This Special Issue welcomes all submissions forcing on the latest advances and trends in secure distributed computing and learning for the future IoT. We seek high-quality and original research papers offering emerging theories, promising architecture and potential applications regarding secure distributed computing and learning that will benefit the future IoT. Topics of interest for this Special Issue include, but are not limited to:

  • Advanced hybrid computation–communication architecture for the future IoT;
  • Enabling techniques enhancing secure distributed computing and learning for future IoT;
  • Performance analysis of secure distributed computing and learning for future IoT;
  • Resource allocation/management in secure distributed computing and learning for future IoT;
  • Applications and testbeds of secure distributed computing and learning for future IoT.

Prof. Dr. Wen Sun
Dr. Lexi Xu
Prof. Dr. Hui Xia
Dr. Libin Yang
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

  • resource allocation and optimization
  • federated learning and blockchain
  • artificial intelligence security
  • distributed computing
  • IoT security

Published Papers (6 papers)

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Research

17 pages, 8392 KiB  
Article
Blockchain-Enabled Intelligent Dispatching and Credit-Based Bidding for Microgrids
by Yingming Zeng, Lan Wei, Yage Cheng, Haibin Zhang, Wen Sun and Bing Wang
Electronics 2023, 12(13), 2868; https://doi.org/10.3390/electronics12132868 - 28 Jun 2023
Cited by 1 | Viewed by 817
Abstract
As a new direction of smart grids, the smart microgrid is a self-sufficient energy system that can generate and distribute energy in limited areas. However, existing work faces issues such as data privacy security, single-power supply mode, and unreasonable scheduling, which bring challenges [...] Read more.
As a new direction of smart grids, the smart microgrid is a self-sufficient energy system that can generate and distribute energy in limited areas. However, existing work faces issues such as data privacy security, single-power supply mode, and unreasonable scheduling, which bring challenges to the application of smart microgrids. In light of this, we formalize a blockchain-based smart microgrid system, preserving the tracking capability of the system and ensuring the privacy of user data. In addition, we propose an intelligent dispatching scheme, in which meteorological factors are considered in power prediction and a prediction results-based intelligent allocation algorithm is designed. Furthermore, we introduce a credit bidding mechanism, which can make companies participate in the dispatching more fairly and proportionately. Numerical results show that our proposed scheme performs well in terms of prediction results and the cost of intelligent dispatching. Full article
(This article belongs to the Special Issue Secure Distributed Computing and Learning for Future Internet of Things)
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17 pages, 1008 KiB  
Article
A Novel Multi-Attack IDS Framework for Intelligent Connected Terminals Based on Over-the-Air Signature Updates
by Beibei Li, Wei Hu, Xue Qu and Yiwei Li
Electronics 2023, 12(10), 2267; https://doi.org/10.3390/electronics12102267 - 17 May 2023
Cited by 1 | Viewed by 870
Abstract
Modern terminals are developing toward intelligence and ubiquitous connection. Such ICTs (intelligent connected terminals) interact more frequently with the outside world and expose new attack surfaces. IDSs (intrusion detection systems) play a vital role in protecting ICT security. Multi-attack IDSs that can cover [...] Read more.
Modern terminals are developing toward intelligence and ubiquitous connection. Such ICTs (intelligent connected terminals) interact more frequently with the outside world and expose new attack surfaces. IDSs (intrusion detection systems) play a vital role in protecting ICT security. Multi-attack IDSs that can cover both intra-terminal and inter-terminal networks are a promising research direction for improving detection accuracy and the strength of security protection. However, a major challenge is the frequent dynamic signature updates across the network boundary, which cause significant computational overheads and result in losses in detection performance. In light of this, we propose a novel IDS framework based on OTA (over-the-air) signature updates to implement multi-attack detection. It updates the attack signatures of the target ICTs and adds the new attack signatures to the signature database in order to minimize the local memory storage and computing resources. It employs a CNN (convolutional neural network) based on an auto-encoder to achieve multi-attack detection, which can ensure the detection accuracy of multi-attacks with the multiple classification function. We evaluated our framework on four types of real-world ICT attack data, drawing comparisons with four widely used IDS schemes, and demonstrated the non-negligible superiority of our scheme over all benchmarks in terms of accuracy, recall, precision, and F1-score. Our work represents an important step toward an IDS that can detect multi-attacks in both intra-terminal and inter-terminal networks. Full article
(This article belongs to the Special Issue Secure Distributed Computing and Learning for Future Internet of Things)
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19 pages, 2952 KiB  
Article
Blockchain-Based Method for Pre-Authentication and Handover Authentication of IoV Vehicles
by Qiang Li, Wenlong Su, Peng Zhang, Xinzhou Cheng, Mingxin Li and Yuanni Liu
Electronics 2023, 12(1), 139; https://doi.org/10.3390/electronics12010139 - 28 Dec 2022
Cited by 2 | Viewed by 1809
Abstract
The Internet of Vehicles (IoV) is an important supporting technology for intelligent transportation systems that connects traffic participants, such as vehicles, pedestrians, and roads, through wireless networks and enables information exchange to enhance traffic safety and improve traffic efficiency. The IoV is a [...] Read more.
The Internet of Vehicles (IoV) is an important supporting technology for intelligent transportation systems that connects traffic participants, such as vehicles, pedestrians, and roads, through wireless networks and enables information exchange to enhance traffic safety and improve traffic efficiency. The IoV is a unique network that involves many network security risks, which must be controlled through authentication, encryption, and other protective measures. To solve problems, such as high computing overhead and low handover authentication efficiency of the existing vehicle access authentication of the IoV, a compact consensus pre-authentication and handover authentication method was designed based on blockchain features such as decentralization and security. The proposed method is based on ensuring authentication security and reduces the consensus time, saves computing resources, and effectively solves the problems of high computing cost and high communication cost arising from frequent vehicle authentication handovers. A performance and security analysis demonstrates that our approach can reduce the computational overhead by up to 88.14% for a vehicle and by more than 60% for a roadside unit (RSU). The overall communication overhead of the solution is reduced by up to 71.31%. The data illustrate that the proposed method can safely and significantly improve the efficiency of vehicle handover authentication. Full article
(This article belongs to the Special Issue Secure Distributed Computing and Learning for Future Internet of Things)
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20 pages, 2471 KiB  
Article
Blockchain Federated Learning for In-Home Health Monitoring
by Komal Farooq, Hassan Jamil Syed, Samar Othman Alqahtani, Wamda Nagmeldin, Ashraf Osman Ibrahim and Abdullah Gani
Electronics 2023, 12(1), 136; https://doi.org/10.3390/electronics12010136 - 28 Dec 2022
Cited by 6 | Viewed by 2840
Abstract
This research combines two emerging technologies, the IoT and blockchain, and investigates their potential and use in the healthcare sector. In healthcare, IoT technology can be utilized for purposes such as remotely monitoring patients’ health. This paper details ongoing research towards individualized health [...] Read more.
This research combines two emerging technologies, the IoT and blockchain, and investigates their potential and use in the healthcare sector. In healthcare, IoT technology can be utilized for purposes such as remotely monitoring patients’ health. This paper details ongoing research towards individualized health monitoring using wearable gadgets. The goal of improving healthcare facilities and improvement of the quality of life of citizens naturally brings up Internet of Things (IoT) technologies for consideration. Health observation is exceptionally critical in terms of avoidance, especially since the early determination of illnesses can minimize trouble and treatment costs. The cornerstones of intelligent, integrated, and individualized healthcare are continuous monitoring of physical signs and evaluation of medical data. To build a more reliable and robust IoMT model, the study will monitor the application of blockchain technology in federated learning (FL). A viable way to address the heterogeneity problem in federated learning is to design the system, data, and model tiers to lessen heterogeneity and produce a high-quality, tailored model for each endpoint. Blockchain-based federated learning allows for smarter simulations, lower latency, and lower power consumption while maintaining privacy at the same time. This solution provides another immediate benefit: in addition to having a shared model upgrade, the updated model on phones will now be used automatically, giving personalized knowledge about the phone is used. Full article
(This article belongs to the Special Issue Secure Distributed Computing and Learning for Future Internet of Things)
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16 pages, 3218 KiB  
Article
An Improved Multimodal Trajectory Prediction Method Based on Deep Inverse Reinforcement Learning
by Ting Chen, Changxin Guo, Hao Li, Tao Gao, Lei Chen, Huizhao Tu and Jiangtian Yang
Electronics 2022, 11(24), 4097; https://doi.org/10.3390/electronics11244097 - 08 Dec 2022
Cited by 1 | Viewed by 2077
Abstract
With the rapid development of artificial intelligence technology, the deep learning method has been introduced for vehicle trajectory prediction in the internet of vehicles, since it provides relative accurate prediction results, which is one of the critical links to guarantee security in the [...] Read more.
With the rapid development of artificial intelligence technology, the deep learning method has been introduced for vehicle trajectory prediction in the internet of vehicles, since it provides relative accurate prediction results, which is one of the critical links to guarantee security in the distributed mixed-driving scenario. In order to further enhance prediction accuracy by making full utilization of complex traffic scenes, an improved multimodal trajectory prediction method based on deep inverse reinforcement learning is proposed. Firstly, a fused dilated convolution module for better extracting raster features is introduced into the existing multimodal trajectory prediction network backbone. Then, a reward update policy with inferred goals is improved by learning the state rewards of goals and paths separately instead of original complex rewards, which can reduce the requirement for predefined goal states. Furthermore, a correction factor is introduced in the existing trajectory generator module, which can better generate diverse trajectories by penalizing trajectories with little difference. Abundant experiments on the current popular public dataset indicate that the prediction results of our proposed method are a better fit with the basic structure of the given traffic scenario in a long-term prediction range, which verifies the effectiveness of our proposed method. Full article
(This article belongs to the Special Issue Secure Distributed Computing and Learning for Future Internet of Things)
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31 pages, 10865 KiB  
Article
Using Machine Learning and Software-Defined Networking to Detect and Mitigate DDoS Attacks in Fiber-Optic Networks
by Sulaiman Alwabisi, Ridha Ouni and Kashif Saleem
Electronics 2022, 11(23), 4065; https://doi.org/10.3390/electronics11234065 - 06 Dec 2022
Cited by 7 | Viewed by 2505
Abstract
Fiber optic networks (FONs) are considered the backbone of telecom companies worldwide. However, the network elements of FONs are scattered over a wide area and managed through a centralized controller based on intelligent devices and the internet of things (IoT), with actuators used [...] Read more.
Fiber optic networks (FONs) are considered the backbone of telecom companies worldwide. However, the network elements of FONs are scattered over a wide area and managed through a centralized controller based on intelligent devices and the internet of things (IoT), with actuators used to perform specific tasks at remote locations. During the COVID-19 pandemic, many telecom companies advised their employees to manage the network using the public internet (e.g., working from home while connected to an IoT network). Theses IoT devices mostly have weak security algorithms that are easily taken-over by hackers, and therefore can generate Distributed Denial of Service (DDoS) attacks in FONs. A DDoS attack is one of the most severe cyberattack types, and can negatively affect the stability and quality of managing networks. Nowadays, software-defined networks (SDN) constitute a new approach that simplifies how the network can be managed through a centralized controller. Moreover, machine learning algorithms allow the detection of incoming malicious traffic with high accuracy. Therefore, combining SDN and ML approaches can lead to detecting and stopping DDoS attacks quickly and efficiently, especially compared to traditional methods. In this paper, we evaluated six ML models: Logistic Regression, K-Nearest Neighbor, Support Vector Machine, Naive Bayes, Decision Tree, and Random Forest. The accuracy reached 100% while detecting DDoS attacks in FON with two approaches: (1) using SVM with three features (SOS, SSIP, and RPF) and (2) using Random Forest with five features (SOS, SSIP, RPF, SDFP, and SDFB). The training time for the first approach was 14.3 s, whereas the second approach only requires 0.18 s; hence, the second approach was utilized for deployment. Full article
(This article belongs to the Special Issue Secure Distributed Computing and Learning for Future Internet of Things)
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