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Intelligence, Security, Trust and Privacy Advances in IoT, Bigdata and 5G Networks (3rd Edition)

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

Deadline for manuscript submissions: 20 September 2026 | Viewed by 1724

Special Issue Editors

Prof. Dr. Jinbo Xiong

E-Mail Website
Guest Editor
College of Mathematics and Informatics, Fujian Normal University, Fuzhou 350117, China
Interests: cloud data security and privacy protection; mobile data security; big data security
Special Issues, Collections and Topics in MDPI journals
Dr. Shaoen Wu

E-Mail Website
Guest Editor
Department of Computer Science, Ball State University, Muncie, IN 47304, USA
Interests: cross-layer architectures and protocols; collaborative and cooperative wireless networking; wireless information security; multi-sensory systems; internet of things; collaborative and autonomous UAVs; energy-friendly smart building; smart health; fault-tolerance; hybrid cloud; ubiquitous cloud
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, Babbio Center, Stevens Institute of Technology, Hoboken, NJ 07030, USA
Interests: big data analytics; machine learning; artificial intelligence
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 the 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) the following:

  • Intelligent data processing, algorithms, and models;
  • 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 networks and edge/fog computing;
  • Federated learning, reinforcement learning, and meta learning;
  • Adaptive access control model, authentication, and authorization;
  • Unmanned aerial vehicle networking, communication, and security;
  • Intelligent transportation systems, communication, and security;
  • Connected healthcare technology and applications;
  • Security, trust, and privacy in smart grids and smart energy;
  • Trusted execution environments and hardware and chip security;
  • Intelligent processing and security in connected autonomous vehicles;
  • Blockchain technologies and applications.

Prof. Dr. Jinbo Xiong
Dr. Shaoen Wu
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 250 words) can be sent to the Editorial Office for assessment.

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.

Keywords

  • artificial intelligence (AI)
  • security
  • privacy
  • cybersecurity
  • IoT
  • sensor network
  • 5G/6G

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Related Special Issue

Published Papers (4 papers)

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Research

20 pages, 3345 KB  
Article
Secure Fog Computing for Remote Health Monitoring with Data Prioritisation and AI-Based Anomaly Detection
by Kiran Fahd, Sazia Parvin, Antony Di Serio and Sitalakshmi Venkatraman
Sensors 2025, 25(23), 7329; https://doi.org/10.3390/s25237329 (registering DOI) - 2 Dec 2025
Viewed by 81
Abstract
Smart remote health monitoring requires time-critical medical data of patients from IoT-enabled cyber–physical systems (CPSs) to be securely transmitted and analysed in real time for early interventions and personalised patient care. Existing cloud architectures are insufficient for smart health systems due to their [...] Read more.
Smart remote health monitoring requires time-critical medical data of patients from IoT-enabled cyber–physical systems (CPSs) to be securely transmitted and analysed in real time for early interventions and personalised patient care. Existing cloud architectures are insufficient for smart health systems due to their inherent issues with latency, bandwidth, and privacy. Fog architectures using data storage closer to edge devices introduce challenges in data management, security, and privacy for effective monitoring of a patient’s sensitive and critical health data. These gaps found in the literature form the main research focus of this study. As an initial modest step to advance research further, we propose an innovative fog-based framework which is the first of its kind to integrate secure communication with intelligent data prioritisation (IDP) integrated into an AI-based enhanced Random Forest anomaly and threat detection model. Our experimental study to validate our model involves a simulated smart healthcare scenario with synthesised health data streams from distributed wearable devices. Features such as heart rate, SpO2, and breathing rate are dynamically prioritised using AI strategies and rule-based thresholds so that urgent health anomalies are transmitted securely in real time to support clinicians and medical experts for personalised early interventions. We establish a successful proof-of-concept implementation of our framework by achieving high predictive performance measures with an initial high score of 93.5% accuracy, 90.8% precision, 88.7% recall, and 89.7% F1-score. Full article
(This article belongs to the Special Issue Intelligence, Security, Trust and Privacy Advances in IoT, Bigdata and 5G Networks (3rd Edition))
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28 pages, 1569 KB  
Article
Privacy-Preserving Hierarchical Fog Federated Learning (PP-HFFL) for IoT Intrusion Detection
by Md Morshedul Islam, Wali Mohammad Abdullah and Baidya Nath Saha
Sensors 2025, 25(23), 7296; https://doi.org/10.3390/s25237296 (registering DOI) - 30 Nov 2025
Viewed by 235
Abstract
The rapid expansion of the Internet of Things (IoT) across critical sectors such as healthcare, energy, cybersecurity, smart cities, and finance has increased its exposure to cyberattacks. Conventional centralized machine learning-based Intrusion Detection Systems (IDS) face limitations, including data privacy risks, legal restrictions [...] Read more.
The rapid expansion of the Internet of Things (IoT) across critical sectors such as healthcare, energy, cybersecurity, smart cities, and finance has increased its exposure to cyberattacks. Conventional centralized machine learning-based Intrusion Detection Systems (IDS) face limitations, including data privacy risks, legal restrictions on cross-border data transfers, and high communication overhead. To overcome these challenges, we propose Privacy-Preserving Hierarchical Fog Federated Learning (PP-HFFL) for IoT intrusion detection, where fog nodes serve as intermediaries between IoT devices and the cloud, collecting and preprocessing local data, thus training models on behalf of IoT clusters. The framework incorporates a Personalized Federated Learning (PFL) to handle heterogeneous, non-independent, and identically distributed (non-IID) data and leverages differential privacy (DP) to protect sensitive information. Experiments on RT-IoT 2022 and CIC-IoT 2023 datasets demonstrate that PP-HFFL achieves detection accuracy comparable to centralized systems, reduces communication overhead, preserves privacy, and adapts effectively across non-IID data. This hierarchical approach provides a practical and secure solution for next-generation IoT intrusion detection. Full article
(This article belongs to the Special Issue Intelligence, Security, Trust and Privacy Advances in IoT, Bigdata and 5G Networks (3rd Edition))
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32 pages, 613 KB  
Article
End-to-End Privacy-Aware Federated Learning for Wearable Health Devices via Encrypted Aggregation in Programmable Networks
by Huzaif Khan, Rahul Kavati, Sriven Srilakshmi Pulkaram and Ali Jalooli
Sensors 2025, 25(22), 7023; https://doi.org/10.3390/s25227023 - 17 Nov 2025
Viewed by 477
Abstract
The widespread use of wearable Internet of Things (IoT) devices has transformed modern healthcare through the real-time monitoring of physiological signals. However, real- time responsiveness and data privacy are big challenges. Federated Learning (FL) keeps direct data exposure to a minimum but is [...] Read more.
The widespread use of wearable Internet of Things (IoT) devices has transformed modern healthcare through the real-time monitoring of physiological signals. However, real- time responsiveness and data privacy are big challenges. Federated Learning (FL) keeps direct data exposure to a minimum but is susceptible to inference attacks on model updates and heavy communication overhead. In-network computing (INC) solutions currently offer greater efficiency but without cryptographic security, whereas homomorphic encryption (HE) offers high privacy but at the expense of latency and scalability. To bridge this gap, we present Edge-Assisted Homomorphic Federated Learning (EAH-FL), a framework that unifies Cheon–Kim–Kim–Song (CKKS) HE with in-network aggregation. Lightweight clients outsource encryption and decryption to trusted edge devices, whereas programmable switches carry out aggregation in the encrypted domain. Massive-scale simulations over realistic healthcare data sets demonstrate that EAH-FL preserves near-plaintext model accuracy (F1-score > 0.93), delivers packet delivery ratios > 0.95, and converges well for various client scales. The encryption expense is mostly incurred by the edge layer rather than resource-constrained wearables. Through the use of encryption, in- network acceleration, and smart routing, EAH-FL provides the first practical solution that achieves strong privacy, low latency, and scalability for real-time federated learning in healthcare in a single solution. These results validate its viability as a deployable and secure building block for next-generation digital health monitoring. Full article
(This article belongs to the Special Issue Intelligence, Security, Trust and Privacy Advances in IoT, Bigdata and 5G Networks (3rd Edition))
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13 pages, 962 KB  
Article
Enhancing Cyber Situational Awareness Through Dynamic Adaptive Symbology: The DASS Framework
by Nicholas Macrino, Sergio Pallas Enguita and Chung-Hao Chen
Sensors 2025, 25(20), 6300; https://doi.org/10.3390/s25206300 - 11 Oct 2025
Viewed by 706
Abstract
The static nature of traditional military symbology, such as MIL-STD-2525D, hinders effective real-time threat detection and response in modern cybersecurity operations. This research introduces the Dynamic Adaptive Symbol System (DASS), a novel framework enhancing cyber situational awareness in military and enterprise environments. The [...] Read more.
The static nature of traditional military symbology, such as MIL-STD-2525D, hinders effective real-time threat detection and response in modern cybersecurity operations. This research introduces the Dynamic Adaptive Symbol System (DASS), a novel framework enhancing cyber situational awareness in military and enterprise environments. The DASS addresses static symbology limitations by employing a modular Python 3.10 architecture that uses machine learning-driven threat detection to dynamically adapt symbol visualization based on threat severity and context. Empirical testing assessed the DASS against a MIL-STD-2525D baseline using active cybersecurity professionals. Results show that the DASS significantly improves threat identification rates by 30% and reduces response times by 25%, while achieving 90% accuracy in symbol interpretation. Although the current implementation focuses on virus-based scenarios, the DASS successfully prioritizes critical threats and reduces operator cognitive load. Full article
(This article belongs to the Special Issue Intelligence, Security, Trust and Privacy Advances in IoT, Bigdata and 5G Networks (3rd Edition))
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