Search Results (171)

The rapid growth of Internet of Things (IoT) deployment has led to an unprecedented volume of interconnected, resource-constrained devices. Securing their communication is essential, especially in vehicular environments, where sensitive data exchange requires robust authentication, integrity, and confidentiality guarantees. In this paper, we present an empirical evaluation of TLS (Transport Layer Security)-enhanced MQTT (Message Queuing Telemetry Transport) on low-cost, quad-core Cortex-A72 ARMv8 boards, specifically the Raspberry Pi 4B, commonly used as prototyping platforms for On-Board Units (OBUs) and Road-Side Units (RSUs). Three MQTT entities, namely, the broker, the publisher, and the subscriber, are deployed, utilizing Elliptic Curve Cryptography (ECC) for key exchange and authentication and employing the AES_256_GCM and ChaCha20_Poly1305 ciphers for confidentiality via appropriately selected libraries. We quantify resource consumption in terms of CPU utilization, execution time, energy usage, memory footprint, and goodput across TLS phases, cipher suites, message packaging strategies, and both Ethernet and WiFi interfaces. Our results show that (i) TLS 1.3-enhanced MQTT is feasible on Raspberry Pi 4B devices, though it introduces non-negligible resource overheads; (ii) batching messages into fewer, larger packets reduces transmission cost and latency; and (iii) ChaCha20_Poly1305 outperforms AES_256_GCM, particularly in wireless scenarios, making it the preferred choice for resource- and latency-sensitive V2X applications. These findings provide actionable recommendations for deploying secure MQTT communication on an IoT platform. Full article

The Internet of Vehicles (IoV) operates in an environment characterized by asymmetric security threats, where centralized vulnerabilities create a critical imbalance that can be disproportionately exploited by attackers. This study addresses this imbalance by proposing a symmetrical security framework that integrates Blockchain and Federated Learning (FL) to restore equilibrium in the Vehicle–Road–Cloud ecosystem. The evolution toward sixth-generation (6G) technologies amplifies both the potential of vehicle-to-everything (V2X) communications and its inherent security risks. The proposed framework achieves a delicate balance between robust security and operational efficiency. By leveraging blockchain’s symmetrical and decentralized distribution of trust, the framework ensures data and model integrity. Concurrently, the privacy-preserving approach of FL balances the need for collaborative intelligence with the imperative of safeguarding sensitive vehicle data. A novel Cloud Proxy Re-Encryption Offloading (CPRE-IoV) algorithm is introduced to facilitate efficient model updates. The architecture employs a partitioned blockchain and a smart contract-driven FL pipeline to symmetrically neutralize threats from malicious nodes. Finally, extensive simulations validate the framework’s effectiveness in establishing a resilient and symmetrically secure foundation for next-generation IoV networks. Full article

Pompeii is widely known for its tragic past. In 79 A.D., a massive eruption of Mount Vesuvius buried the city and its inhabitants under volcanic ash. Lost for centuries, it was rediscovered in 1748 when the Bourbon monarchs initiated excavations, marking the beginning of systematic digs. Since then, Pompeii has gained worldwide recognition for its archeological wonders. Despite centuries of looting and damage, it remains a breathtaking site. With millions of visitors annually, the Pompeii Archeological Park is the one most visited site in Italy. Managing such a vast and complex heritage site requires significant effort to ensure both visitor safety and the preservation of its fragile structures. Accessibility is also crucial, particularly for individuals with disabilities and staff responsible for site management. To address these challenges, integrated systems and advanced technologies like the Internet of Things/Everything (IoT/IoE) can provide innovative solutions. These technologies connect people, smart devices (such as mobile terminals, sensors, and wearables), and data to optimize security, safety, and site management. This paper presents a security/safety IoT/IoE-based system for security, safety, management, and visitor services at the Pompeii Archeological Park. Full article

With the advancement of network communication technology and Internet of Everything (IoE) technology, which connects all edge devices to the internet, the network traffic generated in various platform environments is rapidly increasing. The increase in network traffic makes it more difficult for the detection system to analyze and detect malicious network traffic generated by malware or intruders. Additionally, processing high-dimensional network traffic data requires substantial computational resources, limiting real-time detection capabilities in practical deployments. Artificial intelligence (AI) algorithms have been widely used to detect malicious traffic, but most previous work focused on improving accuracy with various AI algorithms. Many existing methods, in pursuit of high accuracy, directly utilize the extensive raw features inherent in network traffic. This often leads to increased computational overhead and heightened complexity in detection models, potentially degrading overall system performance and efficiency. Furthermore, high-dimensional data often suffers from the curse of dimensionality, where the sparsity of data in high-dimensional space leads to overfitting, poor generalization, and increased computational complexity. This paper focused on feature engineering instead of AI algorithm selections, presenting an approach that uniquely balances detection accuracy with computational efficiency through strategic dimensionality reduction. For feature engineering, two jobs were performed: feature representations and feature analysis and selection. With effective feature engineering, we can reduce system resource consumption in the training period while maintaining high detection accuracy. We implemented a malicious network traffic detection framework based on Convolutional Neural Network (CNN) with our feature engineering techniques. Unlike previous approaches that use one-hot encoding, which increases dimensionality, our method employs label encoding and information gain to preserve critical information while reducing feature dimensions. The performance of the implemented framework was evaluated using the NSL-KDD dataset, which is the most widely used for intrusion detection system (IDS) performance evaluation. As a result of the evaluation, our framework maintained high classification accuracy while improving model training speed by approximately 17.47% and testing speed by approximately 19.44%. This demonstrates our approach’s ability to achieve a balanced performance, enhancing computational efficiency without sacrificing detection accuracy—a critical challenge in intrusion detection systems. With the reduced features, we achieved classification results of a precision of 0.9875, a recall of 0.9930, an F1-score of 0.9902, and an accuracy of 99.06%, with a false positive rate of 0.65%. Full article

Our modern lives are increasingly shaped by the Internet of Things (IoT), as IoT devices monitor and manage everything from our homes to our workplaces, becoming an essential part of health systems and daily infrastructure. However, this rapid growth in IoT has introduced significant security challenges, leading to increased vulnerability to cyber attacks. To address these challenges, machine learning-based intrusion detection systems (IDSs)—traditionally considered a primary line of defense—have been deployed to monitor and detect malicious activities in IoT networks. Despite this, these IDS solutions often struggle with the inherent resource constraints of IoT devices, including limited computational power and memory. To overcome these limitations, we propose an approach to enhance intrusion detection efficiency. First, we introduce a recursive clustering method for data condensation, integrating compactness and entropy-driven sampling to select a highly representative subset from the larger dataset. Second, we adopt a Monte Carlo Cross-Entropy approach combined with a stability metric of features to consistently select the most stable and relevant features, resulting in a lightweight, efficient, and high-accuracy IoT-based IDS. Evaluation of our proposed approach on three IoT datasets from real devices (N-BaIoT, Edge-IIoTset, CICIoT2023) demonstrates comparable classification accuracy while significantly reducing training and testing times by 45× and 15×, respectively, and lowering memory usage by 18×, compared to competitor approaches. Full article

Recently, the rapid increase in the adoption of electric vehicles (EVs) has been driven by considerable technological advancements and a growing focus on environmental sustainability. As consumers and governments increasingly recognize EVs as a viable alternative to traditional internal combustion engine vehicles, the demand for a reliable and accessible charging infrastructure has surged. However, establishing a robust network of charging stations is no longer crucial only to fulfill the demands of EV proprietors but also to relieve range anxiety and improve user convenience, thereby facilitating wider EV adoption. This paper provides a comprehensive global analysis of charging station infrastructure, exploring international standards and regulations, various charging modes, the key parameters of leading electric vehicles, and the importance of RE deployment and ES solutions. Full article

The traditional non-cooperative and cooperative game methods have limitations in solving the traffic problems of autonomous or assisted driving vehicles using vehicle-to-everything communication. In this paper, the biform game method is introduced to optimize the lane-changing behavior of autonomous or assisted driving vehicles in highway on-ramp areas based on vehicle-to-everything. Considering the lane-changing and speed adjustment needs of autonomous vehicles in high-speed scenarios, a forced lane-changing framework was constructed, and the speed gain allocation was determined based on the target vehicle lane-changing time, and a speed increase was regarded as a benefit. Through the constructed biform game model, research was carried out on conflicting and cooperative vehicles. A strategy combination is first constructed in the non-cooperative situation, and then the cooperative game competition stage begins. The Shapley value is used to deduce the distribution value of each participant in the cooperative game stage, which is the profit value in the non-cooperative stage, and then the pure-strategy Nash equilibrium solution is calculated. The interaction with other vehicles in the lane-change process is based on maximizing the benefit to all the vehicles participating in the lane change, and the optimal speed solution of the biform game model when changing lanes is obtained. Numerical examples were used to verify the validity and feasibility of the model and broaden the application range of the biform game method. In future research, this method will be applied to more complex traffic models, such as driving models in emergency situations and research from the perspective of road infrastructure designers, providing new ideas and directions for optimization strategies for autonomous vehicle lane changes in the Internet of Vehicles. Full article

NFC has emerged as a critical technology in IoET ecosystems, facilitating seamless data exchange in proximity-based systems. However, the security and privacy challenges associated with NFC-enabled IoT devices remain significant, exposing them to various threats such as eavesdropping, relay attacks, and spoofing. This paper introduces DC-NFC, a novel deep learning framework designed to enhance the security and privacy of NFC communications within IoT environments. The proposed framework integrates three innovative components: the CE for capturing intricate temporal and spatial patterns, the PML for enforcing end-to-end privacy constraints, and the ATF module for real-time threat detection and dynamic model adaptation. Comprehensive experiments were conducted on four benchmark datasets—UNSW-NB15, Bot-IoT, TON-IoT Telemetry, and Edge-IIoTset. The results of the proposed approach demonstrate significant improvements in security metrics across all datasets, with accuracy enhancements up to 95% on UNSW-NB15, and consistent F1-scores above 0.90, underscoring the framework’s robustness in enhancing NFC security and privacy in diverse IoT environments. The simulation results highlight the framework’s real-time processing capabilities, achieving low latency of 20.53 s for 1000 devices on the UNSW-NB15 dataset. Full article

In the Internet of Everything era of Web 3.0, smart grid (SG) technology is also developing towards intelligent interconnection of terminal devices. However, in the smart grid scenario, security issues are particularly prominent, especially the openness of wireless sensor networks. Sensor nodes are vulnerable to attacks and other security threats, which makes confirming the legitimacy of access identity and ensuring the secure transmission of data an urgent problem to be solved. At present, although a variety of authentication schemes for smart grid nodes have been proposed, most of them have problems. For example, some cannot achieve forward security. Therefore, this paper aims to solve this problem and proposes a lightweight anonymous authentication protocol based on physical unclonable functions (PUFs), which can implement mutual authentication and session key agreement between gateway nodes and sensor nodes. Compared to five state-of-the-art schemes in security and performance, the proposed scheme achieves all eight of the listed security requirements with lightweight calculation overhead, communication overhead, and storage overhead. Full article

In the intelligent transportation field, the Internet of Things (IoT) is commonly applied using 3D object detection as a crucial part of Vehicle-to-Everything (V2X) cooperative perception. However, challenges arise from discrepancies in sensor configurations between vehicles and infrastructure, leading to variations in the scale and heterogeneity of point clouds. To address the performance differences caused by the generalization problem of 3D object detection models with heterogeneous LiDAR point clouds, we propose the Dual-Channel Generalization Neural Network (DCGNN), which incorporates a novel data-level downsampling and calibration module along with a cross-perspective Squeeze-and-Excitation attention mechanism for improved feature fusion. Experimental results using the DAIR-V2X dataset indicate that DCGNN outperforms detectors trained on single datasets, demonstrating significant improvements over selected baseline models. Full article

Semiconducting single-walled carbon nanotubes (SWCNTs) are significantly attractive for thermoelectric generators (TEGs), which convert thermal energy into electricity via the Seebeck effect. This is because the characteristics of semiconducting SWCNTs are perfectly suited for TEGs as self-contained power sources for sensors on the Internet of Things (IoT). However, the thermoelectric performances of the SWCNTs should be further improved by using the power sources. The ideal SWCNTs have a high electrical conductivity and Seebeck coefficient while having a low thermal conductivity, but it is challenging to balance everything. In this study, to improve the thermoelectric performance, we combined two types of SWCNTs: one with a high electrical conductivity (Tuball 01RW03, OCSiAl) and the other with a high Seebeck coefficient and low thermal conductivity (ZEONANO SG101, ZEON). The SWCNT inks were prepared by mixing two types of SWCNTs using ultrasonic dispersion while varying the mixing ratios, and p-type SWCNT films were prepared using vacuum filtration. The highest dimensionless figure-of-merit of 1.1 × 10⁻³ was exhibited at approximately 300 K when the SWCNT film contained the SWCNT 75% of SWCNT (ZEONANO SG101) and 25% of SWCNT (Tuball 01RW03). This simple process will contribute to the prevalent use of SWCNT-TEG as a power source for IoT sensors. Full article

Future 7G/8G networks are expected to integrate both terrestrial Internet and space-based networks. Space networks, including inter-planetary Internet such as cislunar and deep-space networks, will become an integral part of future 7G/8G networks. Vehicle-to-everything (V2X) communication networks will also be a significant component of 7G/8G networks. Therefore, space networks will eventually integrate with V2X communication networks, with both space vehicles (or spacecrafts) and terrestrial vehicles involved. DTN is the only candidate networking technology for future heterogeneous space communication networks. In this work, we study possible concatenations of different DTN convergence layer protocol adapters (CLAs) over a cislunar relay communication architecture. We present a performance characterization of the concatenations of different CLAs and the associated data transport protocols in an experimental manner. The performance of different concatenations is compared over a typical primary and secondary cislunar relay architecture. The intent is to find out which network relay path and DTN protocol configuration has the best performance over the end-to-end cislunar path. Full article

With the development of Internet of Vehicles (IoV) technology, the need for real-time data processing and communication in vehicles is increasing. Traditional request-based methods face challenges in terms of latency and bandwidth limitations. Mode 4 in cellular vehicle-to-everything (C-V2X), also known as autonomous resource selection, aims to address latency and overhead issues by dynamically selecting communication resources based on real-time conditions. However, semi-persistent scheduling (SPS), which relies on distributed sensing, may lead to a high number of collisions due to the lack of centralized coordination in resource allocation. On the other hand, non-orthogonal multiple access (NOMA) can alleviate the problem of reduced packet reception probability due to collisions. Age of Information (AoI) includes the time a message spends in both local waiting and transmission processes and thus is a comprehensive metric for reliability and latency performance. To address these issues, in C-V2X, the waiting process can be extended to the queuing process, influenced by packet generation rate and resource reservation interval (RRI), while the transmission process is mainly affected by transmission delay and success rate. In fact, a smaller selection window (SW) limits the number of available resources for vehicles, resulting in higher collisions when the number of vehicles is increasing rapidly. SW is generally equal to RRI, which not only affects the AoI part in the queuing process but also the AoI part in the transmission process. Therefore, this paper proposes an AoI estimation method based on multi-priority data type queues and considers the influence of NOMA on the AoI generated in both processes in C-V2X system under different RRI conditions. Our experiments show that using multiple priority queues can reduce the AoI of urgent messages in the queue, thereby providing better service about the urgent message in the whole vehicular network. Additionally, applying NOMA can further reduce the AoI of the messages received by the vehicle. Full article

The majority of the global population now resides in cities, and this trend continues to grow. In this context, the Internet of Things (IoT) is crucial in transforming existing urban areas into Smart Cities. However, IoT architectures mainly focus on machine-to-machine interactions, leaving human involvement aside. The Internet of Everything (IoE) includes human-to-human and human–machine collaboration, but the specifics of these interactions are still under-explored. As urban populations grow and IoT integrates into city infrastructure, efficient, collaborative architectures become crucial. In this work, we use the Rapid Review methodology to analyze collaboration in four prevalent computing architectures in the IoE paradigm, namely Edge Computing, Cloud Computing, Blockchain/Web Services, and Fog Computing. To analyze the collaboration, we use the 3C collaboration model, comprising communication, cooperation, and coordination. Our findings highlight the importance of Edge and Cloud Computing for enhancing collaborative coordination, focusing on efficiency and network optimization. Edge Computing supports real-time, low-latency processing at data sources, while Cloud Computing offers scalable resources for diverse workloads, optimizing coordination and productivity. Effective resource allocation and network configuration in these architectures are essential for cohesive IoT ecosystems. Therefore, this work offers a comparative analysis of four computing architectures, clarifying their capabilities and limitations. Smart Cities are a major beneficiary of these insights. This knowledge can help researchers and practitioners choose the best architecture for IoT and IoE environments. Additionally, by applying the 3C collaboration model, the article provides a framework for improving collaboration in IoT and IoE systems. Full article

In this paper, a novel topology and method for designing a multi-band rectenna is proposed to improve its RF-DC efficiency. The rectifier achieves simultaneous rectification using both series and parallel configurations by connecting two branches to the respective terminals of the diode, directing the energy input from two ports to the anode and cathode of the diode. Six desired operating frequency bands are evenly distributed across these two branches, each of which is connected to antennas corresponding to their specific operating frequencies, serving as the receiving end of the system. To optimize the design process, a low-pass filter is incorporated into the rectifier design. This filter works in conjunction with a matching network that includes filtering capabilities to isolate the two ports of the rectifier. The addition of the filter ensures that each structure within the rectifier can be designed independently without adversely affecting the performance of the already completed structures. Based on the proposed design methodology, a dual-port rectenna operating at six frequency bands—1.85 GHz, 2.25 GHz, 2.6 GHz, 3.52 GHz, 5.01 GHz, and 5.89 GHz—was designed, covering the 4G, 5G, and Wi-Fi/WLAN frequency bands. The measured results indicate that high-power conversion efficiency was achieved at an input power of −10 dBm: 43.01% @ 1.85 GHz, 41.00% @ 2.25 GHz, 41.33% @ 2.6 GHz, 35.88% @ 3.52 GHz, 22.36% @ 5.01 GHz, and 19.27% @ 5.89 GHz. When the input power is −20 dBm, the conversion efficiency of the rectenna can be improved from 5.2% for single-tone input to 27.7% for six-tone input, representing a 22.5 percentage point improvement. The proposed rectenna demonstrates significant potential for applications in powering low-power sensors and other devices within the Internet of Everything context. Full article