Search Results (722)

The advent of the Fourth Industrial Revolution has positioned the Internet of Things as a pivotal force in intelligent vehicles. With the source of vehicle-to-everything (V2X), Internet of Things (IoT) networks, and inter-vehicle communication, intelligent connected vehicles are at the forefront of this transformation, leading to complex vehicular networks that are crucial yet susceptible to cyber threats. The complexity and openness of these networks expose them to a plethora of cyber-attacks, from passive eavesdropping to active disruptions like Denial of Service and Sybil attacks. These not only compromise the safety and efficiency of vehicular networks but also pose a significant risk to the stability and resilience of the Internet of Vehicles. Addressing these vulnerabilities, this paper proposes a Dynamic Forest-Structured Ensemble Network (DFSENet) specifically tailored for the Internet of Vehicles (IoV). By leveraging data-balancing techniques and dimensionality reduction, the DFSENet model is designed to detect a wide range of cyber threats effectively. The proposed model demonstrates high efficacy, with an accuracy of 99.2% on the CICIDS dataset and 98% on the car-hacking dataset. The precision, recall, and f-measure metrics stand at 95.6%, 98.8%, and 96.9%, respectively, establishing the DFSENet model as a robust solution for securing the IoV against cyber-attacks. Full article

The rapid expansion of the Industrial Internet-of-Things (IIoT) has spurred significant research interest due to the growth of security-aware, vehicular, and time-sensitive applications. Unmanned aerial vehicles (UAVs) are widely deployed within wireless communication systems to establish rapid and reliable links between users and devices, attributed to their high flexibility and maneuverability. Leveraging UAVs provides a promising solution to enhance communication system performance and effectiveness while overcoming the unprecedented challenges of stringent spectrum limitations and demanding data traffic. However, due to the dramatic increase in the number of vehicles and devices in the industrial wireless networks and limitations on UAVs’ battery storage and computing resources, the adoption of energy-efficient techniques is essential to ensure sustainable system implementation and to prolong the lifetime of the network. This paper provides a comprehensive review of various disruptive methodologies for addressing energy-efficient issues in UAV-assisted industrial wireless networks. We begin by introducing the background of recent research areas from different aspects, including security-enhanced industrial networks, industrial vehicular networks, machine learning for industrial communications, and time-sensitive networks. Our review identifies key challenges from an energy efficiency perspective and evaluates relevant techniques, including resource allocation, UAV trajectory design and wireless power transfer (WPT), across various applications and scenarios. This paper thoroughly discusses the features, strengths, weaknesses, and potential of existing works. Finally, we highlight open research issues and gaps and present promising potential directions for future investigation. Full article

The research problem of this systematic review was whether green 6G networks can integrate energy-efficient Industrial Internet of Things (IIoT) in terms of distributed artificial intelligence, green 6G pervasive edge computing communication networks and big-data-based intelligent decision algorithms. We show that sensor data fusion can be carried out in energy-efficient IoT smart industrial urban environments by cooperative perception and inference tasks. Our analyses debate on 6G wireless communication, vehicular IoT intelligent and autonomous networks, and energy-efficient algorithm and green computing technologies in smart industrial equipment and manufacturing environments. Mobile edge and cloud computing task processing capabilities of decentralized network control and power grid system monitoring were thereby analyzed. Our results and contributions clarify that sustainable energy efficiency and green power generation together with IoT decision support and smart environmental systems operate efficiently in distributed artificial intelligence 6G pervasive edge computing communication networks. PRISMA was used, and with its web-based Shiny app flow design, the search outcomes and screening procedures were integrated. A quantitative literature review was performed in July 2024 on original and review research published between 2019 and 2024. Study screening, evidence map visualization, and data extraction and reporting tools, machine learning classifiers, and reference management software were harnessed for qualitative and quantitative data, collection, management, and analysis in research synthesis. Dimensions and VOSviewer were deployed for data visualization and analysis. Full article

Vehicle-to-everything (V2X) communication is pivotal in enhancing cooperative awareness in vehicular networks. Typically, awareness is viewed as a vehicle’s ability to perceive and share real-time kinematic information. We present a novel definition of awareness in V2X communications, conceptualizing it as a multi-faceted concept involving vehicle detection, tracking, and maintaining their safety distances. To enhance this awareness, we propose a deep reinforcement learning framework for the joint control of beacon rate and transmit power (DRL-JCBRTP). Our DRL−JCBRTP framework integrates LSTM-based actor networks and MLP-based critic networks within the Soft Actor-Critic (SAC) algorithm to effectively learn optimal policies. Leveraging local state information, the DRL-JCBRTP scheme uses an innovative reward function to increase the minimum awareness failure distance. Our SLMLab-Gym-VEINS simulations show that the DRL-JCBRTP scheme outperforms existing beaconing schemes in minimizing awareness failure probability and maximizing awareness distance, ultimately improving driving safety. Full article

As with the broad field of deep learning, autonomy is a research topic that has experienced a heavy explosion in attention from both the scientific and commercial industries due to its potential for the advancement of humanity in many cross-cutting disciplines. Recent advancements in computer vision-based autonomy has highlighted the potential for the realization of increasingly sophisticated autonomous ground vehicles for both commercial and non-traditional applications, such as grocery delivery. Part of the success of these technologies has been a boon in the abundance of training data that is available for training the autonomous behaviors associated with their autonomy software. These data abundance advantage is quickly diminished when an application moves from structured environments, i.e., well-defined city road networks, highways, street signage, etc., into unstructured environments, i.e., cross-country, off-road, non-traditional terrains. Herein, we aim to present insights, from a dataset perspective, into how the scientific community can begin to expand autonomy into unstructured environments, while highlighting some of the key challenges that are presented with such a dynamic and ever-changing environment. Finally, a foundation is laid for the creation of a robust off-road dataset being developed by the Engineer Research and Development Center and Mississippi State University’s Center for Advanced Vehicular Systems. Full article

This paper addresses the growing importance of efficient street lighting management, driven by rising electricity costs and the need for municipalities to implement cost-effective solutions. Central to this study is the UNI 11248 Italian regulation, which extends the European EN 13201-1 standard introduced in 2016. These standards provide guidelines for designing, installing, operating, and maintaining lighting systems in pedestrian and vehicular traffic areas. Specifically, the UNI 11248 standard introduces the possibility to dynamically adjust light intensity through two alternative operating modes: (a) Traffic Adaptive Installation (TAI), which dims the light based solely on real-time traffic flow measurements; and (b) Full Adaptive Installation (FAI), which, in addition to traffic measurements, also requires evaluating road surface luminance and meteorological conditions. In this paper, we first present the general architecture and operation of an FAI-enabled lighting infrastructure, which relies on environmental sensors and a heterogeneous wireless communication network to connect intelligent, remotely controlled streetlights. Subsequently, we examine large-scale, in-field FAI infrastructures deployed in Vietnam and Italy as case studies, providing substantial measurement data. The paper offers insights into the measured energy consumption of these infrastructures, comparing them to that of conventional light-control strategies used in traditional installations. The measurements demonstrate the superiority of FAI as the most efficient solution. Full article

In the era of ubiquitous connectivity facilitated by the Internet of Things (IoT), ensuring robust security mechanisms for communication channels among resource-constrained devices has become imperative. Elliptic curve Diffie–Hellman (ECDH) key exchange offers strong security assurances and computational efficiency. This paper investigates the challenges and opportunities of deploying ECDH key exchange protocols on resource-constrained IoT devices. We review the fundamentals of ECDH and explore optimization techniques tailored to the limitations of embedded systems, including memory constraints, processing power, and energy efficiency. We optimize the implementation of five elliptic curves and compare them using experimental results. Our experiments focus on electronic control units and sensors in vehicular networks. The findings provide valuable insights for IoT developers, researchers, and industry stakeholders striving to enhance the security posture of embedded IoT systems while maintaining efficiency. Full article

Security is an important consideration when delivering information-aware messages to vehicles that are far away from the current location of the information-sending vehicle. This information helps the receiver to save fuel and time by making wise decisions to avoid damaged or blocked roads. To ensure the safety and security of this type of information using blockchain technology, we propose a new cognitive vehicular communication scheme to transfer messages from source to destination. Due to spectrum scarcity in vehicular networks, there needs to be a wireless medium available for every communication link since vehicles require it to communicate. The primary user (PU) makes a public announcement about a free channel to all secondary users nearby and only gives it to authentic vehicles. The authenticity of vehicles is guaranteed by a roadside unit (RSU) that offers secure keys to any vehicle that joins this blockchain network. Those who participate in this network must pay a certain amount and receive rewards for their honesty that exceed the amount spent. To test the performance of various parameters, the proposed scheme utilizes the Ethereum smart contract and compares them to blockchain and non-blockchain methods. Our results show a minimum delivery time of 0.16 s and a minimum overhead of 350 bytes in such a dynamic vehicle environment. Full article

The secret key is stored in an ideal tamper-proof device so that a vehicle can implement a secure authentication with the road-side units (RSUs) and other drivers. However, some adversaries can capture the secret key by physical attacks. To resist physical attacks, we propose a physical-preserving authentication based on a physical unclonable function for vehicular ad hoc networks. In the proposed scheme, a physical unclonable function is deployed on the vehicle and the RSU to provide a challenge–response mechanism. A secret key is only generated by the challenge–response mechanism when it is needed, which eliminates the need to store a long-term secret key. As a result, this prevents secret keys from being captured by adversaries, improving system security. In addition, route planning is introduced into the proposed scheme so that a vehicle can obtain the authentication key of RSUs on its route before vehicle-to-infrastructure authentication, which greatly speeds up the authentication when the vehicle enters the RSUs’ coverage. Furthermore, a detailed analysis demonstrates that the proposed scheme achieves security objectives in vehicular ad hoc networks. Ultimately, when contrasted with similar schemes, the performance assessment demonstrates that our proposed scheme surpasses others in terms of computational overhead, communication overhead and packet loss rate. Full article

The increasing reliance of Vehicular Ad-hoc Networks (VANETs) on the BeiDou Navigation Satellite System (BDS) for precise positioning and timing information has raised significant concerns regarding their vulnerability to spoofing attacks. This research proposes a novel approach to mitigate BeiDou spoofing attacks in VANETs by leveraging a hybrid machine learning model that combines XGBoost and Random Forest with a Kalman Filter for real-time anomaly detection in BeiDou signals. It also introduces a geospatial message authentication mechanism to enhance vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication security. The research investigates low-cost and accessible countermeasures against spoofing attacks using COTS receivers and open-source SDRs. Spoofing attack scenarios are implemented in both software and hardware domains using an open-source BeiDou signal simulator to examine the effects of different spoofing attacks on victim receivers and identify detection methods for each type, focusing on pre-correlation techniques with power-related metrics and signal quality monitoring using correlator values. The emulation results demonstrate the effectiveness of the proposed approach in detecting and mitigating BeiDou spoofing attacks in VANETs, ensuring the integrity and reliability of safety-critical information. This research contributes to the development of robust security mechanisms for VANETs and has practical implications for enhancing the resilience of transportation systems against spoofing threats. Future research will focus on extending the proposed approach to other GNSS constellations and exploring the integration of additional security measures to further strengthen VANET security. Full article

Polycyclic aromatic hydrocarbons (PAHs) are persistent organic pollutants that pose significant environmental and health risks. These compounds originate from both natural phenomena, such as volcanic activity and wildfires, and anthropogenic sources, including vehicular emissions, industrial processes, and fossil fuel combustion. Their classification as carcinogenic, mutagenic, and teratogenic substances link them to various cancers and health disorders. PAHs are categorized into low-molecular-weight (LMW) and high-molecular-weight (HMW) groups, with HMW PAHs exhibiting greater resistance to degradation and a tendency to accumulate in sediments and biological tissues. Soil serves as a primary reservoir for PAHs, particularly in areas of high emissions, creating substantial risks through ingestion, dermal contact, and inhalation. Coastal and aquatic ecosystems are especially vulnerable due to concentrated human activities, with PAH persistence disrupting microbial communities, inhibiting plant growth, and altering ecosystem functions, potentially leading to biodiversity loss. In plants, PAH contamination manifests as a form of abiotic stress, inducing oxidative stress, cellular damage, and growth inhibition. Plants respond by activating antioxidant defenses and stress-related pathways. A notable aspect of plant defense mechanisms involves plant-derived extracellular vesicles (PDEVs), which are membrane-bound nanoparticles released by plant cells. These PDEVs play a crucial role in enhancing plant resistance to PAHs by facilitating intercellular communication and coordinating defense responses. The interaction between PAHs and PDEVs, while not fully elucidated, suggests a complex interplay of cellular defense mechanisms. PDEVs may contribute to PAH detoxification through pollutant sequestration or by delivering enzymes capable of PAH degradation. Studying PDEVs provides valuable insights into plant stress resilience mechanisms and offers potential new strategies for mitigating PAH-induced stress in plants and ecosystems. Full article

The emerging and promising paradigm of the Internet of Vehicles (IoV) employ vehicle-to-everything communication for facilitating vehicles to not only communicate with one another but also with the supporting roadside infrastructure, vulnerable pedestrians, and the backbone network in a bid to primarily address a number of safety-critical vehicular applications. Nevertheless, owing to the inherent characteristics of IoV networks, in particular, of being (a) highly dynamic in nature and which results in a continual change in the network topology and (b) non-deterministic owing to the intricate nature of its entities and their interrelationships, they are susceptible to a number of malicious attacks. Such kinds of attacks, if and when materialized, jeopardizes the entire IoV network, thereby putting human lives at risk. Whilst the cryptographic-based mechanisms are capable of mitigating the external attacks, the internal attacks are extremely hard to tackle. Trust, therefore, is an indispensable tool since it facilitates in the timely identification and eradication of malicious entities responsible for launching internal attacks in an IoV network. To date, there is no dataset pertinent to trust management in the context of IoV networks and the same has proven to be a bottleneck for conducting an in-depth research in this domain. The manuscript-at-hand, accordingly, presents a first of its kind trust-based IoV dataset encompassing 96,707 interactions amongst 79 vehicles at different time instances. The dataset involves nine salient trust parameters, i.e., packet delivery ratio, similarity, external similarity, internal similarity, familiarity, external familiarity, internal familiarity, reward/punishment, and context, which play a considerable role in ascertaining the trust of a vehicle within an IoV network. Full article

The platooning of cars and trucks is a pertinent approach for autonomous driving due to the effective utilization of roadways. The decreased gas consumption levels are an added merit owing to sustainability. Conventional platooning depended on Dedicated Short-Range Communication (DSRC)-based vehicle-to-vehicle communications. The computations were executed by the platoon members with their constrained capabilities. The advent of 5G has favored Intelligent Transportation Systems (ITS) to adopt Multi-access Edge Computing (MEC) in platooning paradigms by offloading the computational tasks to the edge server. In this research, vital parameters in vehicular platooning systems, viz. latency-sensitive radio resource management schemes, and Age of Information (AoI) are investigated. In addition, the delivery rates of Cooperative Awareness Messages (CAM) that ensure expeditious reception of safety-critical messages at the roadside units (RSU) are also examined. However, for latency-sensitive applications like vehicular networks, it is essential to address multiple and correlated objectives. To solve such objectives effectively and simultaneously, the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework necessitates a better and more sophisticated model to enhance its ability. In this paper, a novel Cascaded MADDPG framework, CMADDPG, is proposed to train cascaded target critics, which aims at achieving expected rewards through the collaborative conduct of agents. The estimation bias phenomenon, which hinders a system’s overall performance, is vividly circumvented in this cascaded algorithm. Eventually, experimental analysis also demonstrates the potential of the proposed algorithm by evaluating the convergence factor, which stabilizes quickly with minimum distortions, and reliable CAM message dissemination with $99\%$ probability. The average AoI quantity is maintained within the 5–10 ms range, guaranteeing better QoS. This technique has proven its robustness in decentralized resource allocation against channel uncertainties caused by higher mobility in the environment. Most importantly, the performance of the proposed algorithm remains unaffected by increasing platoon size and leading channel uncertainties. Full article

Vehicular Ad-Hoc Networks (VANETs) are pivotal to the advancement of intelligent transportation systems (ITS), enhancing safety and efficiency on the road through secure communication networks. However, the integrity of these systems is severely threatened by Distributed Denial-of-Service (DDoS) attacks, which can disrupt the transmission of safety-critical messages and put lives at risk. This research paper focuses on developing robust detection methods and countermeasures to mitigate the impact of DDoS attacks in VANETs. Utilizing a combination of statistical analysis and machine learning techniques (i.e., Autoencoder with Long Short-Term Memory (LSTM), and Clustering with Classification), the study introduces innovative approaches for real-time anomaly detection and system resilience enhancement. Emulation results confirm the effectiveness of the proposed methods in identifying and countering DDoS threats, significantly improving (i.e., 94 percent anomaly detection rate) the security posture of a high mobility-aware ad hoc network. This research not only contributes to the ongoing efforts to secure VANETs against DDoS attacks but also lays the groundwork for more resilient intelligent transportation systems architectures. Full article

Visible light communication (VLC) is considered to be a promising technology for realizing intelligent transportation systems (ITSs) and solving traffic safety problems. Due to the complex and changing environment and the influence of weather and other aspects, there are many problems in channel modeling and performance analysis of vehicular VLC. Unlike existing studies, this study proposes a practical vehicle-to-infrastructure (V2I) VLC propagation model for a typical mountain road. The model consists of both line-of-sight (LOS) and non-line-of-sight (NLOS) links. In the proposed model, the effects of vehicle mobility and weather conditions are considered. To analyze the impact of the considered propagation characteristics on the system, closed-form expressions for several performance metrics were derived, including average path loss, received power, channel capacity, and outage probability. Furthermore, to verify the accuracy of the derived theoretical expressions, simulation results were presented and analyzed in detail. The results indicate that, considering the LOS link and when the vehicle is 50 m away from the infrastructure, the difference in channel gain between moderate fog and dense fog versus clear weather conditions is 1.8 dB and 3 dB, respectively. In addition, the maximum difference in total received optical power between dense fog conditions and clear weather conditions can reach 76.2%. Moreover, under clear weather conditions, the channel capacity when vehicles are 40 m away from infrastructure is about 98.9% lower than when they are 10 m away. Additionally, the outage probability shows a high correlation with the threshold data transmission rate. Therefore, the considered propagation characteristics have a significant impact on the performance of V2I–VLC. Full article