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

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

Deadline for manuscript submissions: 15 July 2025 | Viewed by 9564

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Prof. Dr. Shaoen Wu

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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

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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
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Prof. Dr. Periklis Chatzimisios

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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
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Prof. Dr. Mahmoud Daneshmand

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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

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Related Special Issue

Intelligence, Security, Trust and Privacy Advances in IoT, Bigdata and 5G Networks in Sensors (11 articles)

Published Papers (9 papers)

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Research

33 pages, 1020 KiB

Open AccessArticle

Reinforcement Q-Learning-Based Adaptive Encryption Model for Cyberthreat Mitigation in Wireless Sensor Networks

by Sreeja Balachandran Nair Premakumari, Gopikrishnan Sundaram, Marco Rivera, Patrick Wheeler and Ricardo E. Pérez Guzmán

Sensors 2025, 25(7), 2056; https://doi.org/10.3390/s25072056 - 26 Mar 2025

Viewed by 360

Abstract

The increasing prevalence of cyber threats in wireless sensor networks (WSNs) necessitates adaptive and efficient security mechanisms to ensure robust data transmission while addressing resource constraints. This paper proposes a reinforcement learning-based adaptive encryption framework that dynamically scales encryption levels based on real-time network conditions and threat classification. The proposed model leverages a deep learning-based anomaly detection system to classify network states into low, moderate, or high threat levels, which guides encryption policy selection. The framework integrates dynamic Q-learning for optimizing energy efficiency in low-threat conditions and double Q-learning for robust security adaptation in high-threat environments. A Hybrid Policy Derivation Algorithm is introduced to balance encryption complexity and computational overhead by dynamically switching between these learning models. The proposed system is formulated as a Markov Decision Process (MDP), where encryption level selection is driven by a reward function that optimizes the trade-off between energy efficiency and security robustness. The adaptive learning strategy employs an

ϵ

-greedy exploration-exploitation mechanism with an exponential decay rate to enhance convergence in dynamic WSN environments. The model also incorporates a dynamic hyperparameter tuning mechanism that optimally adjusts learning rates and exploration parameters based on real-time network feedback. Experimental evaluations conducted in a simulated WSN environment demonstrate the effectiveness of the proposed framework, achieving a 30.5% reduction in energy consumption, a 92.5% packet delivery ratio (PDR), and a 94% mitigation efficiency against multiple cyberattack scenarios, including DDoS, black-hole, and data injection attacks. Additionally, the framework reduces latency by 37% compared to conventional encryption techniques, ensuring minimal communication delays. These results highlight the scalability and adaptability of reinforcement learning-driven adaptive encryption in resource-constrained networks, paving the way for real-world deployment in next-generation IoT and WSN applications. 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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26 pages, 6053 KiB

Open AccessCommunication

Hybrid Reliable Clustering Algorithm with Heterogeneous Traffic Routing for Wireless Sensor Networks

by Sreenu Naik Bhukya and Chandra Sekhara Rao Annavarapu

Sensors 2025, 25(3), 864; https://doi.org/10.3390/s25030864 - 31 Jan 2025

Viewed by 610

Abstract

Wireless sensor networks (WSNs) are vulnerable to several challenges. Congestion control, the utilization of trust to ensure security, and the incorporation of clustering schemes demand much attention. Algorithms designed to deal with congestion control fail to ensure security and address challenges faced due to congestion in the network. To resolve this issue, a Hybrid Trust-based Congestion-aware Cluster Routing (HTCCR) protocol is proposed to effectively detect attacker nodes and reduce congestion via optimal routing through clustering. In the proposed HTCCR protocol, node probability is determined based on the trust factor, queue congestion status, residual energy (RE), and distance from the mobile base station (BS) by using hybrid K-Harmonic Means (KHM) and the Enhanced Gravitational Search Algorithm (EGSA). Sensor nodes select cluster heads (CHs) with better fitness values and transmit data through them. The CH forwards data to a mobile sink once the sink comes into the range of CH. Priority-based data delivery is incorporated to effectively control packet forwarding based on priority level, thus decreasing congestion. It is evident that the propounded HTCCR protocol offers better performance in contrast to the benchmarked TBSEER, CTRF, and TAGA based on the average delay, packet delivery ratio (PDR), throughput, detection ratio, packet loss ratio (PLR), overheads, and energy through simulations. The proposed HTCCR protocol involves 2.5, 2.3, and 1.7 times less delay; an 18.1%, 12.5%, and 5.5% better detection ratio; 2.9, 2.6, and 1.8 times less energy; a 2.2, 1.9, and 1.5 times lower PLR; a 14.5%, 10.5%, and 5.2% better PDR; a 30.7%, 28.5%, and 18.4% better throughput; and 2.27, 1.91, and 1.66 times lower routing overheads in contrast to the TBSEER, CTRF, and TAGA protocols, respectively. The HTCCR protocol involves 4.1% less delay for the ‘

C_{1}

’ and ‘

C_{2}

’ RT packets, and the average throughput of RT is 10.4% better when compared with NRT. 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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28 pages, 2127 KiB

Open AccessArticle

ElasticPay: Instant Peer-to-Peer Offline Extended Digital Payment System

by Annapureddy Venkata Sai Kumar Reddy and Gourinath Banda

Sensors 2024, 24(24), 8034; https://doi.org/10.3390/s24248034 - 16 Dec 2024

Viewed by 1104

Abstract

The widespread reliance on paper-based currency poses significant drawbacks, such as counterfeiting, lack of transparency, and environmental impacts. While Central Bank Digital Currencies (CBDCs) address many of these issues, their dependence on continuous internet connectivity limits their usability in scenarios with poor or no network access. To overcome such limitations, this paper introduces ElasticPay, a novel Peer-to-Peer (P2P) Offline Digital Payment System that leverages advanced hardware security measures realised through Trusted Platform Modules (TPMs), Trusted Execution Environments (TEEs), and Secure Elements (SEs). ElasticPay ensures transaction privacy, unforgeability, and immediate settlement while preventing double spending. Our approach integrates robust recovery mechanisms and provides a scalable solution for diverse environments. Extensive experimentation validates the system’s reliability and practicality, highlighting its potential to advance secure and inclusive CBDC ecosystems. We demonstrate the proposed solution implementation on the iPhone mobilephone because it has an inbuilt Secure Enclave, which is an integrated implementation of the necessary TPM, TEE, and SE functionalities. 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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22 pages, 5047 KiB

Open AccessArticle

Attention-Based Malware Detection Model by Visualizing Latent Features Through Dynamic Residual Kernel Network

by Mainak Basak, Dong-Wook Kim, Myung-Mook Han and Gun-Yoon Shin

Sensors 2024, 24(24), 7953; https://doi.org/10.3390/s24247953 - 12 Dec 2024

Cited by 1 | Viewed by 1082

Abstract

In recent years, significant research has been directed towards the taxonomy of malware variants. Nevertheless, certain challenges persist, including the inadequate accuracy of sample classification within similar malware families, elevated false-negative rates, and significant processing time and resource consumption. Malware developers have effectively evaded signature-based detection methods. The predominant static analysis methodologies employ algorithms to convert the files. The analytic process is contingent upon the tool’s functionality; if the tool malfunctions, the entire process is obstructed. Most dynamic analysis methods necessitate the execution of a binary file within a sandboxed environment to examine its behavior. When executed within a virtual environment, the detrimental actions of the file might be easily concealed. This research examined a novel method for depicting malware as images. Subsequently, we trained a classifier to categorize new malware files into their respective classifications utilizing established neural network methodologies for detecting malware images. Through the process of transforming the file into an image representation, we have made our analytical procedure independent of any software, and it has also become more effective. To counter such adversaries, we employ a recognized technique called involution to extract location-specific and channel-agnostic features of malware data, utilizing a deep residual block. The proposed approach achieved remarkable accuracy of 99.5%, representing an absolute improvement of 95.65% over the equal probability benchmark. 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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20 pages, 1355 KiB

Open AccessArticle

Context-Aware Trust and Reputation Routing Protocol for Opportunistic IoT Networks

by Jagdeep Singh, Sanjay Kumar Dhurandher, Isaac Woungang and Han-Chieh Chao

Sensors 2024, 24(23), 7650; https://doi.org/10.3390/s24237650 - 29 Nov 2024

Cited by 2 | Viewed by 1221

Abstract

In opportunistic IoT (OppIoT) networks, non-cooperative nodes present a significant challenge to the data forwarding process, leading to increased packet loss and communication delays. This paper proposes a novel Context-Aware Trust and Reputation Routing (CATR) protocol for opportunistic IoT networks, which leverages the probability density function of the beta distribution and some contextual factors, to dynamically compute the trust and reputation values of nodes, leading to efficient data dissemination, where malicious nodes are effectively identified and bypassed during that process. Simulation experiments using the ONE simulator show that CATR is superior to the Epidemic protocol, the so-called beta-based trust and reputation evaluation system (denoted BTRES), and the secure and privacy-preserving structure in opportunistic networks (denoted PPHB+), achieving an improvement of 22%, 15%, and 9% in terms of average latency, number of messages dropped, and average hop count, respectively, under varying number of nodes, buffer size, time to live, and message generation interval. 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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19 pages, 3531 KiB

Open AccessArticle

EKV-VBQ: Ensuring Verifiable Boolean Queries in Encrypted Key-Value Stores

by Yuxi Li, Jingjing Chen, Fucai Zhou and Dong Ji

Sensors 2024, 24(21), 6792; https://doi.org/10.3390/s24216792 - 22 Oct 2024

Viewed by 787

Abstract

To address the deficiencies in privacy-preserving expressive query and verification mechanisms in outsourced key-value stores, we propose EKV-VBQ, a scheme designed to ensure verifiable Boolean queries over encrypted key-value data. We have integrated blockchain and homomorphic Xor operations and pseudo-random functions to create a secure and verifiable datastore, while enabling efficient encrypted Boolean queries. Additionally, we have designed a lightweight verification protocol using bilinear map accumulators to guarantee the correctness of Boolean query results. Our security analysis demonstrates that EKV-VBQ is secure against adaptive chosen label attacks (IND-CLA) and guarantees Integrity and Unforgeability under the bilinear q-strong Diffie–Hellman assumption. Our performance evaluations showed reduced server-side storage overhead, efficient proof generation, and a significant reduction in user-side computational complexity by a factor of log n. Finally, GPU-accelerated optimizations significantly enhance EKV-VBQ’s performance, reducing computational overhead by up to 50%, making EKV-VBQ highly efficient and suitable for deployment in environments with limited computational resources. 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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30 pages, 1978 KiB

Open AccessArticle

RDSC: Range-Based Device Spatial Clustering for IoT Networks

by Fouad Achkouty, Laurent Gallon and Richard Chbeir

Sensors 2024, 24(17), 5851; https://doi.org/10.3390/s24175851 - 9 Sep 2024

Cited by 1 | Viewed by 1137

Abstract

The growth of the Internet of Things (IoT) has become a crucial area of modern research. While the increasing number of IoT devices has driven significant advancements, it has also introduced several challenges, such as data storage, data privacy, communication protocols, complex network topologies, and IoT device management. In essence, the management of IoT devices is becoming more and more challenging, especially with the limited capacity and power of the IoT devices. The devices, having limited capacities, cannot store the information of the entire environment at once. In addition, device power consumption can affect network performance and stability. The devices’ sensing areas with device grouping and management can simplify further networking tasks and improve response quality with data aggregation and correction techniques. In fact, most research papers are looking forward to expanding network lifetimes by relying on devices with high power capabilities. This paper proposes a device spatial clustering technique that covers crucial challenges in IoT. Our approach groups the dispersed devices to create clusters of connected devices while considering their coverage, their storage capacities, and their power. A new clustering protocol alongside a new clustering algorithm is introduced, resolving the aforementioned challenges. Moreover, a technique for non-sensed area extraction is presented. The efficiency of the proposed approach has been evaluated with extensive experiments that gave notable results. Our technique was also compared with other clustering algorithms, showing the different results of these algorithms. 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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17 pages, 2281 KiB

Open AccessArticle

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 954

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, 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

Open AccessArticle

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 1355

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 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 (

T_{i n i t i a l}

) 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

T_{i n i t i a l}

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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