Search Results (970)

Unmanned Aerial Vehicle (UAV)-assisted hierarchical federated learning (HFL) has emerged as a promising architecture for Internet of Things (IoT)-based smart agriculture, which enables scalable model training over large and sparse farmlands. In this setting, UAVs act as mobile edge servers, aggregating local updates from distributed agricultural IoT devices and relaying them to the cloud server. While HFL improves scalability and reduces communication overhead, it still faces critical security threats due to its reliance on public wireless channels and the vulnerability of model aggregation to malicious updates. In this paper, we propose a secure authentication scheme that integrates anomaly detection with elliptic curve cryptography (ECC)-based mutual authentication to protect both the communication and training phases. In the proposed scheme, UAVs authenticate participating clients before receiving their local models, then perform anomaly detection to identify and exclude malicious participants. If a client is found to be malicious, its identity credentials are revoked and broadcast by the cloud server to prevent future participation. The security of the proposed scheme is formally verified using Burrows–Abadi–Needham (BAN) logic, the Real-or-Random (RoR) model, and the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool, along with informal security analysis. The performance evaluation includes comparisons of security features, computation cost, and communication cost with other related schemes, and an experimental assessment of anomaly detection performance. The results demonstrate that our scheme provides strong security guarantees, low overhead, and effective malicious client detection, making it well suited for UAV-assisted HFL in smart agriculture. Full article

Internet-of-Things (IoT) sensing can improve traceability, safety, and efficiency of medical waste handling, yet many deployments remain fragmented, lack an end-to-end system architecture, and do not provide the structured data pipelines needed for artificial intelligence (AI) analytics. This paper presents a layered IoT-based system design for medical waste management that integrates: (i) Espressif Systems 32 (ESP32)-based edge devices for fill-level and Global Positioning System (GPS) telemetry; (ii) secure network communication; (iii) a cloud backend for data ingestion, storage, and analytics; and (iv) operator dashboards with event-driven alerting. The architecture extends our prior GPS-enabled tracking and route optimization by adding sensor-driven state monitoring, threshold-based decision support, and a time-series data pipeline designed for future AI-driven predictive analytics. In a 30-day pilot with five containers, the system collected one reading every 15 min (14,400 total readings). The backend demonstrated efficient processing with an average Application Programming Interface (API) response time of 45 ms, sub-50 ms database write latency, and high uptime; alerts were delivered promptly upon threshold violations. Compared with a fixed-schedule baseline, the system enabled condition-based collection scheduling with zero data loss. The proposed design emphasizes modularity, fault tolerance, and integration readiness for hospital information systems, providing a practical blueprint for scalable smart-healthcare waste logistics and a foundation for machine learning-based predictive waste management. Full article

The Internet of Medical Things (IoMT) has become a core component of modern healthcare infrastructures, enabling continuous patient monitoring, remote diagnostics, and data-driven clinical decision-making. Despite these advances, authentication in IoMT environments remains a critical security challenge, intensified by strict resource constraints of medical devices and the emerging threat posed by quantum computing to classical cryptographic techniques. This systematic review investigates authentication mechanisms in IoMT from both post-quantum and system-level perspectives. A structured literature review was conducted using a PRISMA-informed methodology across major scientific databases, including IEEE Xplore, ACM Digital Library, SpringerLink, ScienceDirect, and MDPI. From an initial set of 95 records, 63 studies were selected for qualitative synthesis following screening and eligibility assessment. To organise existing research, this study introduces a multi-dimensional classification framework that categorises authentication solutions according to cryptographic paradigm (classical, hybrid, and post-quantum), deployment architecture, system objectives, and clinical operational constraints. The comparative synthesis demonstrates important trade-offs between security strength, latency, computational overhead, and energy consumption that are frequently underexplored in the existing literature. Furthermore, the analysis identifies key research gaps related to scalability in heterogeneous medical environments, trust establishment across administrative and clinical domains, usability under strict timing constraints, and resilience against quantum-capable adversaries. Based on these findings, future research directions are outlined toward adaptive, lightweight, and context-aware post-quantum authentication frameworks designed for real-world IoMT deployments. Limitations of this review include restriction to English-language publications and selected databases. This study received no external funding, and the review protocol was not formally registered. Full article

The Industrial Internet of Things (IIoT) enhances industrial efficiency but also introduces substantial security challenges. Authentication is a key building block for securing IIoT networks. However, many recent IoT authentication schemes rely on offline registration and transmit temporary identity credentials in plaintext during registration, which exposes them to privileged-user attacks and limits their practicality in complex deployment scenarios. To address these issues, this paper presents an efficient three-factor authentication scheme with secure online registration for IIoT. The proposed scheme leverages Intel Software Guard Extensions (SGX) to protect the registration master key and support online registration. In addition, a dynamic credential update mechanism is introduced to mitigate privileged-user attacks. The security of the scheme is validated through ProVerif-based formal verification and informal security analysis, while its performance is evaluated through comparative analysis and NS-3 simulations. The results demonstrate that the proposed scheme provides enhanced security with low overhead, making it suitable for IIoT environments. Full article

The increasing complexity and heterogeneity of Internet of Things (IoT) systems pose significant challenges for systematic security and vulnerability assessment. From a knowledge-centric perspective, IoT security analysis requires transforming heterogeneous asset information into structured and interpretable security knowledge. In this paper, we propose a structured methodology for vulnerability analysis that models the attack surface of an IoT system by explicitly linking asset characteristics to known vulnerabilities, security controls, and countermeasures. The approach starts with a visual representation of the system architecture, where hardware, software, and communication components are identified and described through their technical characteristics. These characteristics are automatically mapped to relevant vulnerabilities, security controls, and countermeasures using a dedicated software tool called AVCA (Asset Vulnerabilities and Countermeasures Analyzer). The tool generates graph-based analytical representations that model vulnerabilities–countermeasures relationships in compliance with the Cloud Security Alliance (CSA) IoT Security Framework. From these graphs, attack–countermeasure trees are derived to provide a clear and interpretable representation of potential threats and mitigation strategies. The proposed methodology was evaluated through a case study involving a representative IoT system and an exploratory applicability experiment with participants with different levels of experience in IoT and cybersecurity. The results suggest that the approach is feasible and practically applicable for supporting security analysts in the systematic assessment of IoT attack surfaces, vulnerability identification, and selection of appropriate countermeasures under the evaluated conditions. This work highlights the role of structured and interpretable knowledge extraction as a foundation for knowledge-centric and interpretable IoT security analysis. Full article

The recent advancements in technologies such as artificial intelligence (AI), computer vision (CV), and Internet of Things (IoT) have significantly extended various fields, particularly in surveillance systems. These innovations enable real-time facial recognition processing, enhancing security and ensuring safety. However, mobile robots are commonly employed in surveillance systems to handle risky tasks that are beyond human capability. In this paper, we present a prototype of a cost-effective mobile surveillance robot built on the Raspberry PI 4, designed for integration into various industrial environments. This smart robot detects intruders using IoT and face recognition technology. The proposed system is equipped with a passive infrared (PIR) sensor and a camera for capturing live-streaming video and photos, which are sent to the control room through IoT technology. Additionally, the system uses face recognition algorithms to differentiate between company staff and potential intruders. The face recognition method combines high-order statistical features and evidence theory to improve facial recognition accuracy and robustness. High-order statistical features are used to capture complex patterns in facial images, enhancing discrimination between individuals. Evidence theory is employed to integrate multiple information sources, allowing for better decision-making under uncertainty. This approach effectively addresses challenges such as variations in lighting, facial expressions, and occlusions, resulting in a more reliable and accurate face recognition system. When the system detects an unfamiliar individual, it sends out alert notifications and emails to the control room with the captured picture using IoT. A web interface has also been set up to control the robot from a distance through Wi-Fi connection. The proposed face recognition method is evaluated, and a comparative analysis with existing techniques is conducted. Experimental results with 400 test images of 40 individuals demonstrate the effectiveness of combining various attribute images in improving human face recognition performance. Experimental results indicate that the algorithm can identify human faces with an accuracy of 98.63%. Full article

The manufacturing sector drives industrialisation and contributes substantially to economic growth and employment creation. Despite this, it faces the challenges of diminishing size and lack of competitiveness, mainly due to operational uncertainty. The study developed an approach to managing operational uncertainty using Industry 4.0 and 5.0 technologies. It employed a multimethod quantitative design based on the post-positivist paradigm, with data collected from 22 experts and 262 responses from a manufacturing firms’ survey. The study employed an integrated fuzzy decision-making trial and evaluation laboratory (DEMATEL) with partial least squares structural equation modelling (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA). The fuzzy DEMATEL results reveal that growing geopolitical tension, cost-of-living-driven consumer behavioural change, pandemic turbulence, lack of energy stability and security, and the entrenched power of large firms are causal dimensions of operational uncertainty. Industry 4.0 and 5.0 technologies, with capabilities for scenario planning and supply chain integration, flexible production and mass customisation, real-time system and process monitoring and response, root cause analysis, and sustainable solutions, can manage operational uncertainty. These technologies include artificial intelligence (AI), the Internet of Things (IoT), big data analytics, and, to a lesser extent, advanced robotics, blockchain, and augmented and virtual reality (AR/VR). This study advanced configuration theory and a new integrated methodology (fuzzy-DEMATEL-PLS-SEM-fsQCA) to develop solutions for sustained performance during operational uncertainty in manufacturing. This research offers valuable information to advance the subject, make meaningful changes in day-to-day manufacturing operations, and promote practical real-world problem solving. Full article

Automated production lines are increasingly being expanded with Industrial Internet of Things (IIoT) devices, creating complex Cyber-Physical Systems (CPSs) that connect physical production with control and information infrastructure. However, the convergence of Information Technology (IT) and Operational Technology (OT) layers creates new entry points for attacks targeting communication availability. Most existing studies analyze Distributed Denial of Service (DDoS) attacks primarily in simulation or testbed environments, with limited experimental verification of their impact on real-world production systems. This article presents an experimental evaluation of the impact of DDoS and Distributed Reflection Denial of Service (DRDoS) attacks carried out directly on a physical automated production line with integrated IIoT infrastructure during real operation. Three attack scenarios (TCP SYN flood, TCP ACK flood, and ICMP reflected attack) were implemented, targeting Programmable Logic Controllers (PLCs), Radio-Frequency Identification (RFID) subsystems, and selected IIoT devices. The results showed rapid degradation of deterministic PROFINET communication, disruption of the link between the OT and IT layers, loss of digital product representation, and physical interruption of the production process. Based on the findings, a minimally invasive security solution based on perimeter protection was designed and experimentally verified. The results emphasize the need to design IIoT-based manufacturing systems with an emphasis on network segmentation and architectural separation of the IT and OT layers. Full article

The Internet of Things (IoT) is increasingly embedded in critical infrastructures across healthcare, energy, transportation, and industrial automation, yet its pervasiveness introduces substantial security and resilience challenges. This paper presents a comprehensive review of recent advances in IoT resilience, focusing on developments reported between 2022 and 2025. A layered taxonomy is proposed to organize resilience strategies across hardware, network, learning, application, and governance layers, addressing adversarial, environmental, and hybrid stressors. The survey systematically classifies and compares more than forty representative studies encompassing deep learning under adversarial attack, generative and ensemble intrusion detection, hardware and protocol-level defenses, federated and distributed learning, and trust and governance-based approaches. A comparative analysis shows that while adversarial training, GAN-based augmentation, and decentralized learning improve robustness, their evidence is often confined to specific datasets or attack scenarios, with limited validation in large-scale deployments. The study highlights challenges in benchmarking adaptivity, cross-layer integration, and explainable resilience, concluding with future directions for creating antifragile IoT systems that can self-heal and adapt to evolving cyber–physical threats. Full article

Vulnerability severity assessment plays a critical role in cybersecurity risk management by quantifying risk based on vulnerability disclosure reports. However, interpreting these reports and assigning reliable risk levels remains challenging in Internet of Things (IoT) environments. This paper proposes an IoT vulnerability severity prediction framework aligned with the Common Vulnerability Scoring System (CVSS). The framework is based on a lightweight transformer architecture. It uses a distilled version of Bidirectional Encoder Representations from Transformers (BERT). The model is fine-tuned using transfer learning to capture contextual semantic information from vulnerability descriptions. The lightweight design preserves computational efficiency. Experimental evaluation on an IoT vulnerability dataset shows strong and consistent performance across all severity classes. The proposed model achieves double-digit improvements across key evaluation metrics. In most cases, the improvement exceeds 20% compared with traditional machine learning and baseline deep learning approaches. These results show that lightweight transformer models are well suited for IoT security. They provide a practical and effective solution for automated vulnerability severity classification in resource- and data-constrained environments. Full article

The proliferation of Internet of Things (IoT) devices and services creates not only significant benefits but also new security threats. Classical information encryption techniques are not suitable for resource-constrained edge modules, thereby generating the demand for lightweight and efficient data protection algorithms. This work presents a novel dynamical parameter estimation scheme for chaotic oscillators, applied to physical-layer authentication (PLA). The proposed approach relies on the receiver’s capability to estimate a selected parameter of the transmitter’s oscillator determined by circuit configuration from the received chaotic signal using a locally synchronized oscillator, thereby enabling secure authentication based on a hardware-encoded identifier. The scheme is intended to complement a chaos-based wireless sensor network (WSN) architecture, where sensor nodes (SNs) implement analog chaotic oscillators, and the gateway operates discrete-time models. The Vilnius chaotic oscillator was chosen to validate the proposed PLA scheme. A rigorous bifurcation analysis of analytical, SPICE and discrete oscillator models was first conducted to identify parameter regions that preserve chaotic dynamics, establishing correspondence between models to guarantee the feasibility of parameter estimation across implementations. The digital realization of the parameter estimator demonstrated accurate and stable operation, with a small and nearly constant estimation relative error not exceeding 1.01%. Key performance metrics were analyzed, including estimation time, precision, and noise robustness. A tradeoff between estimation speed and accuracy was identified, particularly under noisy channel conditions. Finally, the influence of the receiver’s native oscillator parameter on distinguishable transmitter parameter ranges was demonstrated, highlighting the configurability and security potential of the proposed system against unauthorized transmissions. Full article

Since the deployment of the Internet of Things (IoT), it has transformed everyday life by enabling intelligent environments that improve efficiency and automate services in domains such as agriculture, healthcare, smart cities, and industry. However, the rapid proliferation of IoT devices has introduced significant security challenges, largely driven by the heterogeneity of devices, resource constraints, and the increasing exposure of network communications. This work proposes a lightweight security protection mechanism for IoT networks based on trust modelling. The proposed approach integrates machine learning techniques to evaluate IoT node behavior using network-layer (Layer 3) traffic features under different labeling granularities, including binary, categorical, and subcategorical classifications. By focusing on network-layer observations, the model remains applicable across heterogeneous IoT devices while preserving a low computational footprint. In addition, the Common Vulnerability Scoring System (CVSS) is incorporated as a standardized vulnerability severity metric, enabling the integration of probabilistic security evidence with contextual information about potential impact. This combination allows the estimation of trust to reflect not only the likelihood of anomalous behavior but also its associated severity. Experimental evaluation was conducted using a representative IoT traffic dataset, multiple preprocessing strategies, and several classical machine learning models. The results demonstrate that aggregating traffic-based intrusion detection outputs with vulnerability severity metrics enables a more robust, flexible, and interpretable trust estimation process. This approach supports the early identification of potentially compromised nodes while maintaining scalability and efficiency, making it suitable for deployment in heterogeneous IoT environments. Full article

In the current situation of the Internet of Things (IoT) with its billions of interconnected devices, security in this low-resource environment is paramount. A Physical Unclonable Function (PUF) is a very useful cryptographic primitive which allows us to extract unique information from a particular device in a non-reproducible way. This allows us to use a PUF in cryptography for authentication or secret-key generation. Ring Oscillators (ROs) and Transient Effect Ring Oscillators (TEROs) are oscillating structures used in both FPGAs and ASICs to build PUFs. In this paper we present an FPGA implementation of a PUF based on what we call the “TERORO” cell (TERO + RO), which is a hybrid structure that allows us to use the different functionalities of both RO and TERO in a single building block. We assess all the possible methods of extracting bits of information from the PUF based on TERORO cells. Finally, we tested the circuit and presented experimental results in terms of its uniqueness, uniformity, and reliability. In RO-counter mode, we obtain $49.74\%$ uniqueness, $54.66\%$ uniformity, and $97.81\%$ reliability across devices, while TERO-based XOR mixing achieves $52.83\%$ uniformity, $45.79\%$ uniqueness, and $93.15\%$ reliability. The FPGA footprint is 142 LUTs, 36 registers, and 82 slices. Full article

Healthcare 5.0 and the Internet of Medical Things (IoMT) is emerging as a scalable model for the delivery of customised healthcare and chronic disease management, through Remote Patient Monitoring (RPM) in patient smart home environments. Large-scale RPM initiatives are being rolled out by healthcare providers (HCPs); however, the constrained nature of IoMT devices and proximity to poorly administered smart home technologies create a cyber risk for highly personalised patient data. The recent Network and Information Systems (NIS 2) directive requires HCPs to improve their cyber risk management approaches, mandating heavy penalties for non-compliance. Current research into cyber risk management in smart home-based RPM does not address scalability. This research examines scalability through the lens of the Non-adoption, Abandonment, Scale-up, Spread and Sustainability (NASSS) framework and develops a novel Scalability Index (SI), informed by a PRISMA guided systematic literature review. Our search strategy identified 57 studies across major databases including ACM, IEEE, MDPI, Elsevier, and Springer, authored between January 2016 and March 2025 (final search 21 March 2025), which focussed on cyber security risk management in the RPM context. Studies focussing solely on healthcare institutional settings were excluded. To mitigate bias, a sample of the papers (30/57) were assessed by two other raters; the resulting Cohen’s Kappa inter-rater agreement statistic (0.8) indicating strong agreement on study selection. The results, presented in graphical and tabular format, provide evidence that most cyber risk approaches do not consider scalability from the HCP perspective. Applying the SI to the 57 studies in our review resulted in a low to medium scalability potential of most cyber risk management proposals, indicating that they would not support the requirements of NIS 2 in the RPM context. A limitation of our work is that it was not tested in a live large-scale setting. However, future research could validate the proposed SI, providing guidance for researchers and practitioners in enhancing cyber risk management of large-scale RPM initiatives. Full article

The agricultural sector, a vital industry for human survival and a primary source of food and raw materials, faces increasing pressure due to global population growth and environmental strains. Productivity, efficiency, and sustainability constraints are preventing traditional farming methods from adequately meeting the growing demand for food. Precision farming has emerged as a transformative paradigm to address these issues. It integrates advanced technologies to improve decision making, optimize yield, and conserve resources. This approach leverages technologies such as wireless sensor networks, the Internet of Things (IoT), robotics, drones, artificial intelligence (AI), and cloud computing to provide effective and cost-efficient agricultural services. Smart sensor technologies are foundational to precision farming. They offer crucial information regarding soil conditions, plant growth, and environmental factors in real time. This review explores the status, challenges, and prospects of smart sensor technologies in precision farming. The integration of smart sensors with the IoT and AI has significantly transformed how agricultural data is collected, analyzed, and utilized to optimize yield, conserve resources, and enhance overall farm efficiency. The review delves into various types of smart sensors used, their applications, and emerging technologies that promise to further innovate data acquisition and decision making in agriculture. Despite progress, challenges persist. They include sensor calibration, data privacy, interoperability, and adoption barriers. To fully realize the potential of smart sensors in ensuring global food security and promoting sustainable farming, the challenges need to be addressed. Full article