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Intelligence, Security, Trust and Privacy Advances in IoT, Bigdata and 5G Networks (2nd Edition)

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 20 September 2024 | Viewed by 1819

Special Issue Editors

Prof. Dr. Shaoen Wu

E-Mail Website
Guest Editor
School of Information Technology, Illinois State University, Normal, IL 62790, USA
Interests: Internet of Things; wireless networking and sensing; smart health; cybersecurity
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jinbo Xiong

E-Mail Website
Guest Editor
College of Mathematics and Informatics, Fujian Normal University, Fuzhou 350117, China
Interests: IoT security; privacy protection; big data security; secure deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Periklis Chatzimisios

E-Mail Website
Guest Editor
Department of Science and Technology, International Hellenic University, 570 01 Nea Moudania, Greece
Interests: IEEE 802.11 standards; Internet of Things; low-power protocols; smart cities
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mahmoud Daneshmand

E-Mail Website
Guest Editor
School of Business, Stevens Institute of Technology, Hoboken, NJ 07030, USA
Interests: data science; artificial intelligence, big data; machine learning; IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the era of the Internet of Things (IoT) and big data, widespread applications of various intelligent terminals and wearable sensing devices promote many novel information transmission approaches and information service modes. Currently, 5G-enabled derivatives, such as smart city, smart transportation, connected healthcare and smart energy, are developing rapidly. While it provides enhanced convenience to peoples’ lives, it also faces severe security and privacy challenges. Various data-driven intelligent applications should be combined with diversified privacy protection technologies, attach importance to personal and organizational identity and data privacy protection, and strive to build a secure ecological environment for the whole lifecycle of information flow.

This Special Issue organizes the latest intelligent data processing strategies, trust management, privacy protection techniques, and attack and defense methods for IoT, Blockchain and 5G ubiquitous networks. Technical contribution papers, industrial case studies and review papers are welcome. The proposed topics include (but are not limited to):

  • Intelligent data processing, algorithm, and model;
  • Security, trust and privacy in IoT, big data and 5G networks;
  • Multimedia networking, communication and security;
  • Data fusion of heterogeneous sensor data and multi-mode data;
  • Secure machine learning and deep learning;
  • Privacy-preserving data mining;
  • Trust and privacy representation, measurement and management;
  • Privacy computing methods, models and algorithms;
  • Security, trust and privacy in wireless sensor network, edge/fog computing;
  • Federated learning, reinforcement learning and meta learning;
  • Adaptive access control model, authentication and authorization;
  • Unmanned aerial vehicles networking, communication and security;
  • Intelligent transportation system, communication and security;
  • Connected healthcare technology and applications;
  • Security, trust and privacy in smart grid and smart energy;
  • Trusted execution environments, hardware and chip security;
  • Intelligent processing and security in connected autonomous vehicles;
  • Blockchain technologies and applications.

Prof. Dr. Shaoen Wu
Prof. Dr. Jinbo Xiong
Prof. Dr. Periklis Chatzimisios
Prof. Dr. Mahmoud Daneshmand
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. Sensors 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 2600 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.

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Related Special Issue

Published Papers (2 papers)

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Research

18 pages, 2281 KiB  
Article
A Secure Data Aggregation Algorithm Based on a Trust Mechanism
by Changtao Liu and Jun Ye
Sensors 2024, 24(13), 4352; https://doi.org/10.3390/s24134352 - 4 Jul 2024
Viewed by 360
Abstract
Due to the uniqueness of the underwater environment, traditional data aggregation schemes face many challenges. Most existing data aggregation solutions do not fully consider node trustworthiness, which may result in the inclusion of falsified data sent by malicious nodes during the aggregation process, [...] Read more.
Due to the uniqueness of the underwater environment, traditional data aggregation schemes face many challenges. Most existing data aggregation solutions do not fully consider node trustworthiness, which may result in the inclusion of falsified data sent by malicious nodes during the aggregation process, thereby affecting the accuracy of the aggregated results. Additionally, because of the dynamically changing nature of the underwater environment, current solutions often lack sufficient flexibility to handle situations such as node movement and network topology changes, significantly impacting the stability and reliability of data transmission. To address the aforementioned issues, this paper proposes a secure data aggregation algorithm based on a trust mechanism. By dynamically adjusting the number and size of node slices based on node trust values and transmission distances, the proposed algorithm effectively reduces network communication overhead and improves the accuracy of data aggregation. Due to the variability in the number of node slices, even if attackers intercept some slices, it is difficult for them to reconstruct the complete data, thereby ensuring data security. Full article
(This article belongs to the Special Issue Intelligence, Security, Trust and Privacy Advances in IoT, Bigdata and 5G Networks (2nd Edition))
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23 pages, 2569 KiB  
Article
Explainable Learning-Based Timeout Optimization for Accurate and Efficient Elephant Flow Prediction in SDNs
by Ling Xia Liao, Changqing Zhao, Roy Xiaorong Lai and Han-Chieh Chao
Sensors 2024, 24(3), 963; https://doi.org/10.3390/s24030963 - 1 Feb 2024
Viewed by 842
Abstract
Accurately and efficiently predicting elephant flows (elephants) is crucial for optimizing network performance and resource utilization. Current prediction approaches for software-defined networks (SDNs) typically rely on complete traffic and statistics moving from switches to controllers. This leads to an extra control channel bandwidth [...] Read more.
Accurately and efficiently predicting elephant flows (elephants) is crucial for optimizing network performance and resource utilization. Current prediction approaches for software-defined networks (SDNs) typically rely on complete traffic and statistics moving from switches to controllers. This leads to an extra control channel bandwidth occupation and network delay. To address this issue, this paper proposes a prediction strategy based on incomplete traffic that is sampled by the timeouts for the installation or reactivation of flow entries. The strategy involves assigning a very short hard timeout (Tinitial) to flow entries and then increasing it at a rate of r until flows are identified as elephants or out of their lifespans. Predicted elephants are switched to an idle timeout of 5 s. Logistic regression is used to model elephants based on a complete dataset. Bayesian optimization is then used to tune the trained model Tinitial and r over the incomplete dataset. The process of feature selection, model learning, and optimization is explained. An extensive evaluation shows that the proposed approach can achieve over 90% generalization accuracy over 7 different datasets, including campus, backbone, and the Internet of Things (IoT). Elephants can be correctly predicted for about half of their lifetime. The proposed approach can significantly reduce the controller–switch interaction in campus and IoT networks, although packet completion approaches may need to be applied in networks with a short mean packet inter-arrival time. Full article
(This article belongs to the Special Issue Intelligence, Security, Trust and Privacy Advances in IoT, Bigdata and 5G Networks (2nd Edition))
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