Search Results (765)

The Internet of Things (IoT) represents a rapidly growing field, where billions of intelligent devices are interconnected through the Internet, enabling the seamless sharing of data and resources. These smart devices are typically employed to sense various environmental characteristics, including temperature, motion of objects, and occupancy, and transfer their values to the nearest access points for further analysis. The exponential growth in sensor availability and deployment, powered by recent advances in sensor fabrication, has greatly increased the complexity of IoT network architecture. As the market for these sensors grows, so does the problem of ensuring that IoT networks meet high requirements for network availability, dependability, flexibility, and scalability. Unlike traditional networks, IoT systems must be able to handle massive amounts of data generated by various and frequently-used resource-constrained devices, while ensuring efficient and dependable communication. This puts high constraints on the design of IoT, mainly in terms of the required network availability, reliability, flexibility, and scalability. To this end, this work considers deploying a recent technology of distributed edge computing to enable IoT applications over dense networks with the announced requirements. The proposed network depends on distributed edge computing at two levels: multiple access edge computing and fog computing. The proposed structure increases network scalability, availability, reliability, and scalability. The network model and the energy model of the distributed nodes are introduced. An energy-offloading method is considered to manage IoT data over the network energy, efficiently. The developed network was evaluated using a developed IoT testbed. Heterogeneous evaluation scenarios and metrics were considered. The proposed model achieved a higher energy efficiency by 19%, resource utilization by 54%, latency efficiency by 86%, and reduced network congestion by 92% compared to traditional IoT networks. Full article

In the context of data security, this work aims to present a novel solution that, rather than addressing the topic of endpoint security—which has already garnered significant attention within the international scientific community—offers a different perspective on the subject. In other words, the focus is not on device security but rather on the protection and security of the information contained within those devices. As we will see, the result is a next-generation decentralized infrastructure that simultaneously integrates two cognitive areas: data storage and its protection and security. In this context, an innovative Multiscale Relativistic Quantum (MuReQua) chain is considered to realize a novel decentralized and security solution for storing data. This engine is based on the principles of Quantum Mechanics, stochastic processes, and a new approach of decentralization for data storage focused on information security. The solution is broken down into four main components, considered four levels of security against attackers: (i) defocusing, (ii) fogging, (iii) puzzling, and (iv) crypto agility. The defocusing is realized thanks to a fragmentation of the contents and their distributions on different allocations, while the fogging is a component consisting of a solution of hybrid cyphering. Then, the puzzling is a unit of Information Fusion and Inverse Information Fusion, while the crypto agility component is a frontier component based on Quantum Computing, which gives a stochastic dynamic to the information and, in particular, to its data fragments. The data analytics show a very effective and robust solution, with executions time comparable with cloud technologies, but with a level of security that is a post quantum one. In the end, thanks to a specific application example, going beyond purely technical and technological aspects, this work introduces a new cognitive perspective regarding (i) the distinction between data and information, and (ii) the differentiation between the owner and the custodian of data. Full article

When using a fiber optic gyroscope as the core measurement element in an inertial navigation system, its work stability and reliability directly affect the accuracy of the navigation system. The modeling and fault diagnosis of the gyroscope is of great significance in ensuring the high accuracy and long endurance of the inertial system. Traditional diagnostic models often encounter challenges in terms of reliability and accuracy, for example, difficulties in feature extraction, high computational cost, and long training time. To address these challenges, this paper proposes a new fault diagnostic model that performs a fault diagnosis of gyroscopes using the enhanced capsule neural network (iCaps NN) optimized by the improved gray wolf algorithm (NIGWO). The wavelet packet transform (WPT) is used to construct a two-dimensional feature vector matrix, and the deep feature extraction module (DFE) is added to extract deep-level information to maximize the fault features. Then, an improved gray wolf algorithm combined with the adaptive algorithm (Adam) is proposed to determine the optimal values of the model parameters, which improves the optimization performance. The dynamic routing mechanism is utilized to greatly reduce the model training time. In this paper, effectiveness experiments were carried out on the simulation dataset and real dataset, respectively; the diagnostic accuracy of the fault diagnosis method in this paper reached 99.41% on the simulation dataset; the loss value in the real dataset converged to 0.005 with the increase in the number of iterations; and the average diagnostic accuracy converged to 95.42%. The results show that the diagnostic accuracy of the NIGWO-iCaps NN model proposed in this paper is improved by 13.51% compared with the traditional diagnostic methods. It effectively confirms that the method in this paper is capable of efficient and accurate fault diagnosis of FOG and has strong generalization ability. Full article

To mitigate the prevalence of highway accidents in Southwest China during adverse weather conditions, this study introduces a novel method for identifying accident-prone sections in complex meteorological circumstances. The technique, anchored in data mining’s support index, pioneers the concept of meteorological responsiveness, which includes the elucidation of its mechanisms and the development of computational methodologies. Historical meteorological data and accident records from mountainous highways were meticulously analyzed to quantify the spectrum of adverse weather impacts on driving risks. By integrating road geometry, weather data, and accident site information, meteorological events were identified, categorized, and assigned a meteorological responsiveness score. Outlier sections were processed for preliminary screening, enabling the identification of high-risk segments. The Meteorological Response Ratio Index was instrumental in highlighting and quantifying the influence of adverse weather on traffic safety, facilitating the prioritization of critical sections. The case study of the SC2 highway in Southwest China validated the method’s feasibility, successfully pinpointing eight high-risk sections significantly affected by adverse weather, which constituted approximately 19.05% of the total highway length. Detailed analysis of these sections, especially those impacted by rain, fog, and snow, revealed specific zones prone to accidents. The meteorological responsiveness method’s efficacy was further substantiated by correlating accident mechanisms under adverse weather with the road geometry of key sections. This approach stands to significantly enhance the safety management of operational highways. Full article

Foggy weather poses significant challenges to outdoor computer vision tasks, such as object detection, by degrading image quality and reducing algorithm reliability. In this paper, we present a novel model for estimating fog density in outdoor scenes, aiming to enhance object detection performance under varying foggy conditions. Using a support vector machine (SVM) classification framework, the proposed model categorizes unknown images into distinct fog density levels based on both global and local fog-relevant features. Key features such as entropy, contrast, and dark channel information are extracted to quantify the effects of fog on image clarity and object visibility. Moreover, we introduce an innovative region selection method tailored to images without detectable objects, ensuring robust feature extraction. Evaluation on synthetic datasets with varying fog densities demonstrates a classification accuracy of 85.8%, surpassing existing methods in terms of correlation coefficients and robustness. Beyond accurate fog density estimation, this approach provides valuable insights into the impact of fog on object detection, contributing to safer navigation in foggy environments. Full article

Edge, fog, and cloud computing provide complementary capabilities to enable distributed processing of IoT data. This requires offloading mechanisms, decision-making mechanisms, support for the dynamic availability of resources, and the cooperation of available nodes. This paper proposes a novel 3-tier architecture that integrates edge, fog, and cloud computing to harness their collective strengths, facilitating optimised data processing across these tiers. Our approach optimises performance, reducing energy consumption, and lowers costs. We evaluate our architecture through a series of experiments conducted on a purpose-built testbed. The results demonstrate significant improvements, with speedups of up to 7.5 times and energy savings reaching 80%, underlining the effectiveness and practical benefits of our cooperative edge-fog-cloud model in supporting the dynamic computational needs of IoT ecosystems. We argue that a multi-tier (e.g., edge-fog-cloud) dynamic task offloading and management of heterogeneous devices will be key to flexible edge computing, and that the advantage of task relocation and offloading is not straightforward but depends on the configuration of devices and relative device capabilities. Full article

The distributed nature of IoT systems and new trends focusing on fog computing enforce the need for reliable communication that ensures the required quality of service for various scenarios. Due to the direct interaction with the real world, failure to deliver the required QoS level can introduce system failures and lead to further negative consequences for users. This paper introduces a prediction-based resource allocation method for Multi-Access Edge Computing-capable networks, aimed at assurance of the required QoS and optimization of resource utilization for various types of IoT use cases featuring adaptability to changes in users’ requests. The method considers the current resource load and predicted changes in resource utilization based on historical request data, which are then utilized to adjust the resource allocation optimization criteria for upcoming requests. The proposed method was developed for scenarios utilizing edge computing, e.g., autonomous vehicle data exchange, which can be susceptible to periodic resource demand fluctuations related to typical rush hours, predictable with the proposed approach. The results indicate that the proposed approach can increase the reliability of processes conducted in IoT systems. Full article

Vehicle Ad hoc Networks (VANETs) play an essential role in intelligent transportation systems (ITSs) by improving road safety and traffic management through robust decentralized communication between vehicles and infrastructure. Yet, decentralization introduces security vulnerabilities, including spoofing, tampering, and denial-of-service attacks, which can compromise the reliability and safety of vehicular communications. Traditional centralized security mechanisms are often inadequate in providing the real-time response and scalability required by such dispersed networks. This research promotes a shift toward distributed and real-time technologies, including blockchain and secure multi-party computation, to enhance communication integrity and privacy, ultimately strengthening system resilience by eliminating single points of failure. A core aspect of this study is the novel D-CASBR framework, which integrates three essential components. First, it employs hybrid machine learning methods, such as ElasticNet and Gradient Boosting, to facilitate real-time anomaly detection, identifying unusual activities as they occur. Second, it utilizes a consortium blockchain to provide secure and transparent information exchange among authorized participants. Third, it implements a fog-enabled reputation system that uses distributed fog computing to effectively manage trust within the network. This comprehensive approach addresses latency issues found in conventional systems while significantly improving the reliability and efficacy of threat detection, achieving 95 percent anomaly detection accuracy with minimal false positives. The result is a substantial advancement in securing vehicular networks. Full article

Recently, task offloading in the Internet of Drones (IoD) is considered one of the most important challenges because of the high transmission delay due to the high mobility and limited capacity of drones. This particularity makes it difficult to apply the conventional task offloading technologies, such as cloud computing and edge computing, in IoD environments. To address these limits, and to ensure a low task offloading delay, in this paper we propose PSO BS-Fog, a task offloading optimization that combines a particle swarm optimization (PSO) heuristic with fog computing technology for the IoD. The proposed solution applies the PSO for task offloading from unmanned aerial vehicles (UAVs) to fog base stations (FBSs) in order to optimize the offloading delay (transmission delay and fog computing delay) and to guarantee higher storage and processing capacity. The performance of PSO BS-Fog was evaluated through simulations conducted in the MATLAB environment and compared against PSO UAV-Fog and PSO UAV-Edge IoD technologies. Experimental results demonstrate that PSO BS-Fog reduces task offloading delay by up to 88% compared to PSO UAV-Fog and by up to 97% compared to PSO UAV-Edge. Full article

Due to developments in vehicle engineering and communication technologies, vehicular networks have become an attractive and feasible solution for the future of electric, autonomous, and connected vehicles. Electric autonomous vehicles will require more data, computing resources, and communication capabilities to support them. The combination of vehicles, the Internet, and cloud computing together to form vehicular cloud computing (VCC), vehicular edge computing (VEC), and vehicular fog computing (VFC) can facilitate the development of electric autonomous vehicles. However, more connected and engaged nodes also increase the system’s vulnerability to cybersecurity and privacy breaches. Various security and privacy challenges in vehicular cloud computing and its variants (VEC, VFC) can be efficiently tackled using machine learning (ML). In this paper, we adopt a semi-systematic literature review to select 85 articles related to the application of ML for cybersecurity and privacy protection based on VCC. They were categorized into four research themes: intrusion detection system, anomaly vehicle detection, task offloading security and privacy, and privacy protection. A list of suitable ML algorithms and their strengths and weaknesses is summarized according to the characteristics of each research topic. The performance of different ML algorithms in the literature is also collated and compared. Finally, the paper discusses the challenges and future research directions of ML algorithms when applied to vehicular cloud computing. Full article

WSNs are significant components of modern IoT systems, which typically operate in resource-constrained environments integrated with fog and cloud computing to achieve scalability and real-time performance. Integrating these systems brings challenges such as security threats, scalability bottlenecks, and energy constraints. In this work, we propose a bioinspired blockchain framework aimed at addressing those challenges through the emulation of biological immune adaptation mechanisms, such as the self-recovery of swarm intelligence. It integrates lightweight blockchain technology with bioinspired algorithms, including an AIS for anomaly detection and a Proof of Adaptive Immunity Consensus mechanism for secure resource-efficient blockchain validation. Experimental evaluations give proof of the superior performance reached within this framework: up to 95.2% of anomaly detection accuracy, average energy efficiency of 91.2% when the traffic flow is normal, and latency as low as 15.2 ms during typical IoT scenarios. Moreover, the framework has very good scalability since it can handle up to 500 nodes with only a latency of about 6.0 ms. Full article

This study presents a predictive maintenance system designed for industrial Internet of Things (IoT) environments, focusing on resource efficiency and adaptability. The system utilizes Nicla Sense ME sensors, a Raspberry Pi-based concentrator for real-time monitoring, and a Long Short-Term Memory (LSTM) machine-learning model for predictive analysis. Notably, the LSTM algorithm is an example of how the system’s sandbox environment can be used, allowing external users to easily integrate custom models without altering the core platform. In the laboratory, the system achieved a Root Mean Squared Error (RMSE) of 0.0156, with high accuracy across all sensors, detecting intentional anomalies with a 99.81% accuracy rate. In the real-world phase, the system maintained robust performance, with sensors recording a maximum Mean Absolute Error (MAE) of 0.1821, an R-squared value of 0.8898, and a Mean Absolute Percentage Error (MAPE) of 0.72%, demonstrating precision even in the presence of environmental interferences. Additionally, the architecture supports scalability, accommodating up to 64 sensor nodes without compromising performance. The sandbox environment enhances the platform’s versatility, enabling customization for diverse industrial applications. The results highlight the significant benefits of predictive maintenance in industrial contexts, including reduced downtime, optimized resource use, and improved operational efficiency. These findings underscore the potential of integrating Artificial Intelligence (AI) driven predictive maintenance into constrained environments, offering a reliable solution for dynamic, real-time industrial operations. Full article

The Internet of Things (IoT) contains many devices that can compute and communicate, creating large networks. Industrial Internet of Things (IIoT) represents a developed application of IoT, connecting with embedded technologies in production in industrial operational settings to offer sophisticated automation and real-time decisions. Still, IIoT compels significant cybersecurity threats beyond jamming and spoofing, which could ruin the critical infrastructure. Developing a robust Intrusion Detection System (IDS) addresses the challenges and vulnerabilities present in these systems. Traditional IDS methods have achieved high detection accuracy but need improved scalability and privacy issues from large datasets. This paper proposes a Fog-enabled Federated Learning-based Intrusion Detection System (FFL-IDS) utilizing Convolutional Neural Network (CNN) that mitigates these limitations. This framework allows multiple parties in IIoT networks to train deep learning models with data privacy preserved and low-latency detection ensured using fog computing. The proposed FFL-IDS is validated on two datasets, namely the Edge-IIoTset, explicitly tailored to environments with IIoT, and CIC-IDS2017, comprising various network scenarios. On the Edge-IIoTset dataset, it achieved 93.4% accuracy, 91.6% recall, 88% precision, 87% F1 score, and 87% specificity for jamming and spoofing attacks. The system showed better robustness on the CIC-IDS2017 dataset, achieving 95.8% accuracy, 94.9% precision, 94% recall, 93% F1 score, and 93% specificity. These results establish the proposed framework as a scalable, privacy-preserving, high-performance solution for securing IIoT networks against sophisticated cyber threats across diverse environments. Full article

Wildfires pose a severe threat to ecological systems, human life, and infrastructure, making early detection critical for timely intervention. Traditional fire detection systems rely heavily on single-sensor approaches and are often hindered by environmental conditions such as smoke, fog, or nighttime scenarios. This paper proposes Adaptive Multi-Sensor Oriented Object Detection with Space–Frequency Selective Convolution (AMSO-SFS), a novel deep learning-based model optimized for drone-based wildfire and smoke detection. AMSO-SFS combines optical, infrared, and Synthetic Aperture Radar (SAR) data to detect fire and smoke under varied visibility conditions. The model introduces a Space–Frequency Selective Convolution (SFS-Conv) module to enhance the discriminative capacity of features in both spatial and frequency domains. Furthermore, AMSO-SFS utilizes weakly supervised learning and adaptive scale and angle detection to identify fire and smoke regions with minimal labeled data. Extensive experiments show that the proposed model outperforms current state-of-the-art (SoTA) models, achieving robust detection performance while maintaining computational efficiency, making it suitable for real-time drone deployment. Full article

This review examines the integration of blockchain technology with the IoT in the Marine Internet of Things (MIoT) and Internet of Underwater Things (IoUT), with applications in areas such as oceanographic monitoring and naval defense. These environments present distinct challenges, including a limited communication bandwidth, energy constraints, and secure data handling needs. Enhancing BIoT systems requires a strategic selection of computing paradigms, such as edge and fog computing, and lightweight nodes to reduce latency and improve data processing in resource-limited settings. While a blockchain can improve data integrity and security, it can also introduce complexities, including interoperability issues, high energy consumption, standardization challenges, and costly transitions from legacy systems. The solutions reviewed here include lightweight consensus mechanisms to reduce computational demands. They also utilize established platforms, such as Ethereum and Hyperledger, or custom blockchains designed to meet marine-specific requirements. Additional approaches incorporate technologies such as fog and edge layers, software-defined networking (SDN), the InterPlanetary File System (IPFS) for decentralized storage, and AI-enhanced security measures, all adapted to each application’s needs. Future research will need to prioritize scalability, energy efficiency, and interoperability for effective BIoT deployment. Full article