Search Results (184)

Ever-increasing computational demand introduced by the expanding scale of Internet of Things (IoT) devices poses significant concerns in terms of energy consumption in a fog–cloud environment. Due to the limited resources of IoT devices, energy-efficient task offloading becomes even more challenging for time-sensitive tasks. In this paper, we propose a reinforcement learning-based framework, namely Adaptive Q-learning-based Energy-aware Task Offloading (AQETO), that dynamically manages the energy consumption of fog nodes in a fog–cloud network. Concurrently, it considers IoT task delay tolerance and allocates computational resources while satisfying deadline requirements. The proposed approach dynamically determines energy states of each fog node using Q-learning depending on workload fluctuations. Moreover, AQETO prioritizes allocation of the most urgent tasks to minimize delays. Extensive experiments demonstrate the effectiveness of AQETO in terms of the minimization of fog node energy consumption and delay and the maximization of system efficiency. Full article

The Internet of Things (IoT) continues to expand across critical infrastructures, enabling automation, efficiency, and data driven decision making; yet, reliable device identification from network traffic remains hampered by severe class imbalance that skews learning and degrades performance. Synthetic data generation offers a promising remedy, particularly in privacy-sensitive security settings where access to representative traffic is limited. This paper advances the state of the art by proposing a framework that unites graph-conditioned generative modeling with large language model (LLM) guidance to produce realistic, semantically valid synthetic network traffic for imbalanced classification. First, we construct feature relationship graphs derived from Pearson correlation, Spearman rank correlation, and mutual information to capture inter-feature dependencies, and use these graphs to condition a Wasserstein GAN (WGAN), thereby preserving structural properties of real traffic during generation. Second, we employ an LLM to define class-specific semantic constraints, including admissible feature ranges, attribute correlations, and protocol level rules, which are enforced as soft guidance to steer the generator toward label-consistent and standards-compliant samples. Third, we institute a dual validation loop that combines LLM-based feedback on constraint satisfaction with evaluation of classifiers trained on datasets balanced by our method versus the traditional SMOTE technique. Lastly, extensive experiments demonstrate that jointly leveraging structural (graph) and semantic (LLM) conditioning yields higher-fidelity synthetic traffic and delivers consistent gains in macro-F1 and balanced accuracy for network traffic classification, highlighting the framework’s utility for security analytics under data scarcity and privacy constraints. Full article

In wireless sensor networks (WSNs), node localization technology serves as a critical foundation for Internet of Things (IoT) applications such as environmental monitoring and ecological protection. High-precision localization has long been a key challenge in IoT applications. However, traditional multi-hop localization algorithms suffer from insufficient localization accuracy in complex environments due to path tortuosity and error accumulation. To address this issue, this paper proposes DV-Hop-HLPT, a multi-hop localization algorithm based on a tortuosity model and a hierarchical strategy for reliable anchor nodes. The algorithm employs a hierarchical localization strategy to expand the anchor node set, incorporating high-precision localized nodes into the anchor node collection through received signal strength indication (RSSI) calibration and evaluating their reliability. To address the multi-hop path tortuosity problem, the algorithm constructs a tortuosity weight model by analyzing path information between anchor nodes, enabling dynamic correction of multi-hop path lengths. Combined with an incremental shortest path first (ISPF) algorithm to limit search depth, the approach enhances adaptability to dynamic networks. Finally, utilizing the tortuosity model and anchor node reliability, the unknown node coordinates are solved through regularized weighted least squares method. Experimental results demonstrate that under square and C-shaped network topologies, DV-Hop-HLPT reduces average normalized localization error by 50.15% and 70.95%, respectively, compared with DV-Hop, and shows significant improvements over other enhanced algorithms, effectively addressing the localization accuracy degradation problem caused by sparse anchor nodes in complex environments. Full article

Cold chain logistics is a critical process for ensuring product safety and quality assurance; however, existing systems face significant challenges due to centralized data structures, limited transparency, and low reliability. The objective of this study is to develop a blockchain infrastructure based on Hyperledger Fabric, integrated with IoT technologies, to address these issues. In the proposed system, secure collaboration among producers, carrier, and retailer organizations is achieved through role-based access control and authorization mechanisms, while environmental data collected from IoT sensors are immutably recorded on the blockchain. Performance tests conducted with Hyperledger Caliper demonstrated that the system maintained stable operation even under high transaction loads. In particular, query transactions achieved the most efficient results, reaching 442 transactions per second at a send rate of 500 TPS and 818 transactions per second at a send rate of 1000 TPS, with corresponding average latencies of 0.21 and 0.26 s, respectively. The absence of failed transactions further reinforced the reliability of the system. In addition, scalability experiments were conducted to assess how the system performs as the network expands with additional peer nodes across organizations. The results confirmed that the proposed architecture maintains improved latency and throughput under both intra-organizational and network-wide scaling scenarios. The results demonstrate that the proposed system provides a reliable, transparent, and scalable infrastructure even under low hardware configurations, contributing to the rapid and trustworthy verification of product history and environmental conditions in cold chain applications. Full article

The Internet of Things (IoT) continues to expand rapidly, with the Routing Protocol for Low-Power and Lossy Networks (RPL) serving as its core communication backbone. However, RPL remains vulnerable to a range of insider routing attacks such as the Version Number Attack (VNA) and Hello Flooding Attack (HFA), particularly in constrained IoT environments. In our previous work, IbiboRPLChain, we proposed a blockchain-based authentication mechanism to secure communication between routing and sensor nodes. This paper presents an evolved framework, IbiboRPLChain II, which integrates smart contracts, decentralised authentication nodes, and composite blockchain mechanisms to improve network resilience, scalability, and security. Our experiments, conducted using Cooja and Contiki OS, evaluate the system across multiple simulation seeds, demonstrating significant gains in Packet Delivery Ratio (PDR), energy efficiency, and delay mitigation. IbiboRPLChain II proves to be a robust solution for secure, lightweight, and scalable RPL-based IoT environments. Full article

As the Internet of Things (IoT) continues to expand into mission-critical and long-lived applications, securing low-power wide-area networks (LPWANs) such as Narrowband IoT (NB-IoT) against both classical and quantum threats becomes imperative. Existing NB-IoT security mechanisms terminate at the core network, leaving transmission payloads exposed. This paper proposes HySecure, an FPGA-based hybrid cryptographic platform that integrates both classical elliptic curve and post-quantum schemes to achieve end-to-end (E2E) security for NB-IoT communication. Our architecture, built upon the lightweight RISC-V PULPino platform, incorporates hardware accelerators for X25519, Kyber, Ed25519, and Dilithium. We design a hybrid key establishment protocol combining ECDH and Kyber through HKDF, and a dual-signature scheme using EdDSA and Dilithium to ensure authenticity and integrity during handshake. Cryptographic functions are evaluated on FPGA, achieving a 32.2× to 145.4× speedup. NS-3 simulations under realistic NB-IoT configurations demonstrate acceptable latency and throughput for the proposed hybrid schemes, validating their practicality for secure constrained IoT deployments and communications. Full article

The Internet of Medical Things (IoMT) is the latest advancement in the Internet of Things (IoT). Researchers are increasingly drawn to its vast potential applications in secure healthcare systems. The growing use of internet-connected medical device sensors has significantly transformed healthcare, necessitating the development of robust methodologies to assess their integrity. As access to computer networks continues to expand, these sensors have become vulnerable to a wide range of security threats, thereby compromising their integrity. To prevent such lapses, it is essential to understand the complexities of the operational environment and to systematically identify technical vulnerabilities. This paper proposes a unified hesitant fuzzy-based healthcare system for assessing IoMT sensor integrity. The approach integrates the hesitant fuzzy Analytic Network Process (ANP) and the hesitant fuzzy Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). In this study, a hesitant fuzzy ANP is employed to construct a comprehensive network that illustrates the interrelationships among various integrity criteria. This network incorporates expert input and accounts for inherent uncertainties. The research also offers sensitivity analysis and comparative evaluations to show that the suggested method can analyse many medical device sensors. The unified hesitant fuzzy-based healthcare system presented here offers a systematic and valuable tool for informed decision-making in healthcare. It strengthens both the integrity and security of healthcare systems amid the rapidly evolving landscape of medical technology. Healthcare stakeholders and beyond can significantly benefit from adopting this integrated fuzzy-based approach as they navigate the challenges of modern healthcare. Full article

With the advancement of technologies such as 5G, digital twins, and edge computing, the Internet of Things (IoT) as a critical component of intelligent systems is profoundly driving the transformation of various industries toward digitalization and intelligence. However, the exponential growth of network connection nodes has expanded the attack exposure surface of IoT devices. The IoT devices with limited storage and computing resources struggle to cope with new types of attacks, and IoT devices lack mature authorization and authentication mechanisms. It is difficult for traditional data-sharing solutions to meet the security requirements of cloud-based shared data. Therefore, this paper proposes a blockchain-based multi-authority IoT data-sharing scheme with attribute-based searchable encryption for intelligent system (BM-ABSE), aiming to address the security, efficiency, and verifiability issues of data sharing in an IoT environment. Our scheme decentralizes management responsibilities through a multi-authority mechanism to avoid the risk of single-point failure. By utilizing the immutability and smart contract function of blockchain, this scheme can ensure data integrity and the reliability of search results. Meanwhile, some decryption computing tasks are outsourced to the cloud to reduce the computing burden on IoT devices. Our scheme meets the static security and IND-CKA security requirements of the standard model, as demonstrated by theoretical analysis, which effectively defends against the stealing or tampering of ciphertexts and keywords by attackers. Experimental simulation results indicate that the scheme has excellent computational efficiency on resource-constrained IoT devices, with core algorithm execution time maintained in milliseconds, and as the number of attributes increases, it has a controllable performance overhead. Full article

Satellite–terrestrial integrated communication systems (STICSs) are envisioned to provide ubiquitous, seamless connectivity in next-generation (6G) wireless communication networks for massive-scale Internet of Things (IoT) deployments. This global coverage extends beyond densely populated areas to remote regions (e.g., polar zones, open oceans, deserts) and disaster-prone areas, supporting diverse IoT applications, including remote sensing, smart cities, intelligent agriculture/forestry, environmental monitoring, and emergency reporting. Random access signals, which constitute the initial transmission from access IoT devices to base station for unscheduled transmissions or network entry in terrestrial networks (TNs), encounter significant challenges in STICSs due to inherent satellite characteristics: wide coverage, large-scale access, substantial round-trip delay, and high carrier frequency offset (CFO). Consequently, conventional TN preamble designs based on Zadoff–Chu (ZC) sequences, as used in 4G LTE and 5G NR systems, are unsuitable for direct deployment in 6G STICSs. This paper first analyzes the challenges in adapting terrestrial designs to STICSs. It then proposes a CFO-resistant preamble design specifically tailored for STICSs and details its detection procedure. Furthermore, a dedicated root set selection algorithm for the proposed preambles is presented, generating an expanded pool of random access signals to meet the demands of increasing IoT device access. The developed analytical framework provides a foundation for performance analysis of random access signals in 6G STICSs. Full article

The Extensible Authentication Protocol–Transport Layer Security (EAP-TLS) is a critical authentication protocol for wireless networks and secure IoT communications. However, it faces significant challenges from man-in-the-middle attacks, including message tampering, replay, and certificate forgery. Although model checking techniques have been applied to verify its security properties, the complexity of the EAP-TLS handshake often prevents accurate formal modeling; existing studies rarely assess the communication overhead of protocol enhancements. Moreover, traditional Logic of Events Theory (LoET) struggles to handle transport-layer protocols like EAP-TLS due to their intricate interaction processes. This study proposes a novel formal analysis approach, extending LoET by expanding five event classes, formulating corresponding rules, and introducing new axioms. Formal verification reveals attack paths involving plaintext theft, message tampering, and entity impersonation. The research proposes an enhanced strategy to mitigate these vulnerabilities through hash merging, encryption, and signature methods, alongside analyzing their communication costs to ensure feasibility. Using the extended LoET, the improved protocol is rigorously proven to satisfy strong authentication, thereby enhancing practical security. The proposed method achieves a time complexity of O(n²) and demonstrates superior performance in resisting state explosion compared with related approaches, thus establishing a more efficient and robust framework for EAP-TLS security analysis. Full article

This systematic review examines the methodologies and applications of integrated risk indicators in positron emission tomography (PET) radiopharmaceutical production, focusing on occupational, technological, and environmental risks. Conducted in accordance with PRISMA 2020 guidelines and utilizing the Ryyan software 2023 for article screening, the review synthesizes findings from 70 studies published between 2020 and 2025 in English and Spanish, including articles, conference papers, and reviews. The review was registered on PROSPERO (CRD420251078221). Key disciplines contributing to risk assessment frameworks include environmental science, occupational health and safety, civil engineering, mining engineering, maritime safety, financial/economic risk, and systems engineering. Predominant risk assessment methods identified are probabilistic modeling (e.g., Monte Carlo simulations), machine learning (e.g., neural networks), multi-criteria decision-making (e.g., AHP and TOPSIS), and failure mode and effects analysis (FMEA), each offering strengths, such as uncertainty quantification and systematic hazard identification, alongside limitations like data dependency and subjectivity. The review explores how frameworks from other industries can be adapted to address PET-specific risks, such as radiation exposure to workers, equipment failure, and waste management, and how studies integrate these factors into unified risk indicators using weighted scoring, probabilistic methods, and fuzzy logic. Gaps in the literature include limited stakeholder engagement, lack of standardized frameworks, insufficient real-time monitoring, and under-represented environmental risks. Future research directions propose developing PET-specific tools, integrating AI and IoT for real-time data, establishing standardized frameworks, and expanding environmental assessments to enhance risk management in PET radiopharmaceutical production. This review highlights the interdisciplinary nature of risk assessment and the critical need for comprehensive, tailored approaches to ensure safety and sustainability in this field. Full article

The widespread adoption of Internet of Things (IoT) technology has significantly expanded the scale at which devices are connected, posing new challenges to maintaining symmetry in network management. Traditional centralized identification architectures adopt a symmetric processing paradigm in which all device data are uniformly transmitted to the cloud for processing. However, this rigid symmetric structure fails to accommodate the asymmetric distribution typical of IoT edge devices. To address these challenges, this paper proposes an asymmetric identification framework based on cloud–edge collaboration, exploring a high-performance, resource-efficient, and privacy-preserving solution for IoT device identification. The proposed region-specific personalized algorithm (FedRP) introduces a region-specific, personalized identification approach grounded in federated learning principles. Firstly, FedRP leverages a decentralized processing framework to enhance data security by processing data locally. Secondly, it employs a personalized federated learning framework to optimize local models, thus improving identification accuracy and effectiveness. Finally, FedRP strategically separates the personalized parameters of transformer-based blocks from shared parameters and selectively transmits them, reducing the burden on network resources. Comprehensive comparative experiments demonstrate the efficacy of the proposed approach for large-scale IoT environments, which are characterized by numerous devices and complex network conditions. Full article

Vehicular ad hoc networks (VANETs) are a critical aspect of intelligent transportation systems, improving safety and comfort for drivers. These networks enhance the driving experience by offering timely information vital for safety and comfort. Yet, VANETs come with their own set of challenges concerning security, privacy, and design reliability. Traditionally, vehicle authentication occurs every time a vehicle enters the domain of the roadside unit (RSU). In our study, we suggest that authentication should take place only when a vehicle has not covered a set distance, increasing system efficiency. The rise of the Internet of Things (IoT) has seen an upsurge in the use of IoT devices across various fields, including smart cities, healthcare, and vehicular IoT. These devices, while gathering environmental data and networking, often face reliability issues without a trusted intermediary. Our study delves deep into implementing Teegraph in VANETs to enhance authentication. Given the integral role of VANETs in Intelligent Transportation Systems and their inherent challenges, we turn to Hashgraph—an alternative to blockchain. Hashgraph offers a decentralized, secure, and trustworthy database. We introduce an efficient authentication system, which triggers only when a vehicle has not traversed a set distance, optimizing system efficiency. Moreover, we shed light on the indispensable role Hashgraph can occupy in the rapidly expanding IoT landscape. Lastly, we present Teegraph, a novel Hashgraph-based technology, as a superior alternative to blockchain, ensuring a streamlined, scalable authentication solution. Our approach leverages the logical key hierarchy (LKH) and packet update keys to ensure data privacy and integrity in vehicular networks. Full article

As smart homes and Internet of Things (IoT) ecosystems continue to expand, the need for energy-efficient and low-latency communication has become increasingly critical. One of the key challenges in these systems is minimizing delay time while ensuring reliable and efficient communication between devices. This study focuses on optimizing delay time in ZigBee sensor networks used in smart-home systems. A Smart–Adaptive Communication Distribution Algorithm is proposed, which dynamically adjusts the communication between network nodes based on real-time network conditions. Experimental measurements were conducted under various scenarios to evaluate the performance of the proposed algorithm, with a particular focus on reducing delay and enhancing overall network efficiency. The results demonstrate that the proposed algorithm significantly reduces delay times compared to traditional methods, making it a promising solution for delay-sensitive IoT applications. Furthermore, the findings highlight the importance of adaptive communication strategies in improving the performance of ZigBee-based sensor networks. Full article

Novel networking paradigms such as the Internet of Things (IoT) have expanded their usage and deployment to various application domains. Consequently, unseen critical security vulnerabilities such as zero-day attacks have emerged in such deployments. The design of intrusion detection systems for IoT networks is often challenged by a lack of labeled data, which complicates the development of robust defenses against adversarial attacks. As deep learning-based network intrusion detection systems, network intrusion detection systems (NIDS) have been used to counteract emerging security vulnerabilities. However, the deep learning models used in such NIDS are vulnerable to adversarial examples. Adversarial examples are specifically engineered samples tailored to a specific deep learning model; they are developed by minimal perturbation of network packet features, and are intended to cause misclassification. Such examples can bypass NIDS or enable the rejection of regular network traffic. Research in the adversarial example detection domain has yielded several prominent methods; however, most of those methods involve computationally expensive retraining steps and require access to labeled data, which are often lacking in IoT network deployments. In this paper, we propose an unsupervised method for detecting adversarial examples that performs early detection based on the intrinsic characteristics of the deep learning model. Our proposed method requires neither computationally expensive retraining nor extra hardware overhead for implementation. For the work in this paper, we first perform adversarial example generation on a deep learning model using autoencoders. After successful adversarial example generation, we perform adversarial example detection using the intrinsic characteristics of the layers in the deep learning model. A robustness analysis of our approach reveals that an attacker can easily bypass the detection mechanism by using low-magnitude log-normal Gaussian noise. Furthermore, we also test the robustness of our detection method against further compromise by the attacker. We tested our approach on the Kitsune datasets, which are state-of-the-art datasets obtained from deployed IoT network scenarios. Our experimental results show an average adversarial example generation time of 0.337 s and an average detection rate of almost 100%. The robustness analysis of our detection method reveals a reduction of almost 100% in adversarial example detection after compromise by the attacker. Full article