Vehicular Communications最新文献

With the constant development of wireless communication technologies, the Vehicular Ad-hoc Networks (VANETs) will become an inevitable paradigm for intelligent transportation systems. Privacy-preserving and authentication security has been a core concern in VANETs, and pseudonym-based and group signature-based privacy-preserving authentication schemes have been widely proposed. However, most research schemes rely excessively on the Trusted Authority, and vehicle request and authentication information is almost transparent in front of TAs, while they cannot guarantee the security of vehicle request information after it is intercepted. To address these issues, this paper proposes a lightweight privacy-preserving authentication scheme that mitigates TA dependency, aiming to achieve mutual privacy protection and authentication between vehicles and TAs in VANETs by improving the oblivious transfer algorithm. The security analysis proves that this scheme has good privacy protection and attack resistance, and in addition, the performance simulation and evaluation show that this scheme spends lower computation and communication overheads while ensuring lower authentication delay and packet loss rate, which is a better overall performance compared with other schemes.

The recent advancement of smart UAVs (Unmanned Aerial Vehicles) allows its applicability to wide-range fields covering time-critical reports, emergent item delivery, virtual emotion services and so on. In particular, the surveillance can be supported by a fleet of smart UAVs in cooperation with various system components including sensors, mobile robots, autonomous vehicles and so on. Additionally, it is crucial to consider the reconfigurable capabilities of smart UAVs leveraging for rapid movement in disregarded airspace. In this paper, we introduce a reconfigurable UAV-enabled 3D sustainable surveillance in classified air-spaces to provide energy-efficient monitoring and security surveillance enforcement. We formally define a problem to maximize the reconfigurable UAV-aided sustainable surveillance efficiency with optimal height in 3D classified air-spaces avoiding excessive resource consumption and waste. To solve this issue, two different schemes of Lined-Up and Zig-Zag are proposed. Furthermore, we analyze their performances based on the gained numerical results through expanded simulations with detailed discussions.

The significant contribution of human errors, accounting for approximately 94% (with a margin of $\pm 2.2\text{\%}$), to road crashes leading to casualties, vehicle damages, and safety concerns necessitates the exploration of alternative approaches. Autonomous Vehicles (AVs) have emerged as a promising solution by replacing human drivers with advanced computer-aided decision-making systems. However, for AVs to effectively navigate the road, they must possess the capability to predict the future behaviour of nearby traffic participants, similar to the predictive driving abilities of human drivers. Building upon existing literature is crucial to advance the field and develop a comprehensive understanding of trajectory prediction methods in the context of automated driving. To address this need, we have undertaken a comprehensive review that focuses on trajectory prediction methods for AVs, with a particular emphasis on machine learning techniques including deep learning and reinforcement learning-based approaches. We have extensively examined over two hundred studies related to trajectory prediction in the context of AVs. The paper begins with an introduction to the general problem of predicting vehicle trajectories and provides an overview of the key concepts and terminology used throughout. After providing a brief overview of conventional methods, this review conducts a comprehensive evaluation of several deep learning-based techniques. Each method is summarized briefly, accompanied by a detailed analysis of its strengths and weaknesses. The discussion further extends to reinforcement learning-based methods. This article also examines the various datasets and evaluation metrics that are commonly used in trajectory prediction tasks. Encouraging an unbiased and objective discussion, we compare two major learning processes, considering specific functional features. By identifying challenges in the existing literature and outlining potential research directions, this review significantly contributes to the advancement of knowledge in the domain of AV trajectory prediction. Its primary objective is to streamline current research efforts and offer a futuristic perspective, ultimately benefiting future developments in the field.

Ambient backscatter communication (AmBC) has recently attracted a lot of attention as a solution for next-generation internet of things (IoT) applications due to its capability to interact with peer devices with extremely little power usage (in μW). Security being a major concern in IoT applications, we look at the AmBC system from a physical layer security standpoint. In this paper, we focus on a realistic scenario in which numerous tags, a multi-antenna reader, and a multi-antenna eavesdropper are present, and the nodes are in motion concerning each other. The influence of speed, estimation error, number of antennas at nodes, and number of tags on the AmBC system's secrecy performance in terms of secrecy outage probability and intercept probability is investigated. Finally, the theoretical findings are supported with Monte-Carlo simulations.

Cloud of Things (CoT) emerges as a pivotal paradigm, connecting Internet of Things (IoT) devices to the Cloud Computing space, facilitating the efficient management of smart cities. In navigating the intricate landscape of smart city environments, this paper confronts two paramount challenges— heterogeneity and privacy preservation. Heterogeneity, rooted in the diverse origins of CoT devices from various vendors, intro- duces compatibility gaps and data format variations, impeding seamless communication among devices. Simultaneously, privacy preservation concerns itself with averting the inadvertent disclo- sure of sensitive data generated by CoT devices. Existing solutions often exhibit limitations in effectively addressing both challenges concurrently. To bridge this gap, our proposed solution employs a novel ontology-based approach, commencing with the introduc- tion of a groundbreaking ”Ontology” using the Protege software. This foundational tool serves a dual purpose—standardizing and unifying general and privacy-related information among diverse CoT devices. The ontology addresses the heterogeneity challenge by fostering a shared understanding and vocabulary, promoting interoperability for smoother communication among disparate devices. Complementing the ontology, a privacy-preservation method, implemented with ”MININET-WIFI” and grounded in Modular Arithmetic, dynamically adjusts the privacy-preserving rules of each CoT device. This adaptive mechanism signifi- cantly enhances security, mitigating the risk of unintentional data disclosure—a critical aspect evaluated extensively within the context of a widely used CoT application, specifically, the Autonomous Vehicle (AV) environment. The computational cost is meticulously evaluated, showcasing that our solution introduces a modest overhead, notably below 1.8 s, compared to alternative models. Furthermore, the penetration rate analysis reveals the solution's resilience against honest but curious Remote Service Units (RSUs). Communication overhead is quantified for various privacy-preserving methods, providing a comprehensive view of the solution's performance. Through rigorous simula- tions, encompassing assessments of communication overhead, computational costs, and penetration rates, our solution exhibits not only affordability for a diverse array of CoT devices in smart cities but also heightened resilience against malicious activities and adversaries, surpassing current studies. This paper, therefore, not only presents a novel ontology-based solution but also delves into the nuanced intricacies of heterogeneity and privacy preservation within CoT-based smart cities. The proposed approach, characterized by its dual focus on standardization and dynamic privacy adaptation, signifies a significant stride towards fostering secure, interoperable, and privacy-aware CoT ecosystems amid the dynamic landscape of smart cities.

In this paper, we consider a Software Defined Networking (SDN)/Network Function Virtualized (NFV) networked computing system, which is composed of a serving Rotary Wing (RW) Unmanned Aerial Vehicle (UAV), a Ground Controller Station (GCS) and a number of resource-limited (possibly, heterogeneous) Ground Users (GUs) that randomly move in environments affected by fading-induced probabilistic path-loss. The focus of this paper is on the joint and adaptive optimization of the 3D trajectory parameters (i.e., altitude, radius, and speed) of the RW-UAV that circulates over the served hotspot area for providing communication and/or computing support to the GUs. The objective is the minimization of the RW-UAV propulsion energy under constraints on the maximum allowed average path-loss, maximum tolerated outage probability, and finite beam-width of the UAV antenna. Due to the acceleration-dependent terms present in the considered RW-UAV energy propulsion model, the formulated problem is non-convex, and up to now, its solution still appears not to be addressed in the literature. Hence, to tackle this challenging problem: 1) we develop a (seemingly new) convexification approach to turn the problem into a Geometric Programming (GP) one; 2) after characterizing the related feasibility conditions, we develop an adaptive solving approach that relies on primal-dual gradient-based iterations; and, then, 3) we perform a joint co-design of the main blocks of the SDN/NFV-based communication/computing architectures equipping the serving RW-UAV and controlling GCS, in order to provide support for the orchestration of the computing/communication microservices possibly required by the served GUs. The conducted numerical tests confirm that the performance gains of the proposed optimization framework against the ones of a number of baselines may reach 22%, while the corresponding performance gaps against the ultimate performance of a brute force search-based benchmark remain typically limited up to 3%-4%.

The UAV (Unmanned Aerial Vehicles) is an automatic aircraft, widely used several applications like emergency management, wildlife conservation, forestry, aerial photography, etc. The communication among the UAV is susceptible to security threats with several diverse attacks. The data sharing among the UAV and other vehicles is vulnerable to jamming and suspicious activities that disturbs the communication. To tackle the issue, IDS (Intrusion Detection System) is the significant system that monitors and identifies the suspicious activities in the communication network. To attain this, several conventional researchers attempted to accomplish better intrusion detection. However, classical models are limited by accuracy, noise and computation. To overcome the limitation, proposed method employs particular set of procedures for the intrusion detection in UAV with Intrusion UAV dataset. The dataset comprise of features like drone speed, height, width, velocity etc. Initially, in the respective approach, GG (Greedy based Genetic) algorithm for feature selection, which maintains the exact balance between the greediness and diversified population. Greedy approach enhances Genetic algorithm in combinatorial optimisation problems. Further, the study proposes Modified Deep CNN-BiLSTM (Deep Convolutional Neural Network and Bi-Long Short Term Memory) with attention mechanism for classification of intrusion in UAV. The deep CNN is utilized for the ability of handling larger datasets and accuracy. Conversely, it is limited by computation and speed. To tackle the problem, Bi-LSTM is used for the capability of enhancing the computation and speed. Moreover, attention mechanism is used for handle the complexity and to permit the presented system to focus on the significant and relevant data. Correspondingly, proposed approach performance is calculated using performance metrics such as accuracy, specificity, sensitivity, $R^2$ (R-Squared), execution time, RMSE and precision. Furthermore, comparative analysis of the proposed method and classical model exposes the efficacy of the respective system.

In recent years, Flying Ad-hoc Networks (FANETs) have gained significant attention among researchers due to the widespread applications and increasing popularity of Unmanned Aerial Vehicles (UAVs). As technology advances and more research is undertaken, FANETs are expected to become a vital aspect of modern times, allowing for more effective and creative applications in different domains. However, FANETs also face several challenges, including high mobility, dynamic topology, energy constraints, and communication reliability. Addressing these challenges is essential to unlock the full potential of FANETs and to ensure reliable and timely delivery of data. In this paper, we propose HMGOC, a novel clustered routing model for FANETs, utilizing a hybrid approach that combines the Mountain Gazelle Optimizer (MGO) and Jaya Algorithms. The dynamic flying behavior of UAVs demands an adaptive and efficient clustering strategy to maintain network stability and ensure robust and reliable communication among UAVs. In this context, MGO, one of the most recent swarm-based optimization methods, is enhanced and employed for FANET clustering process. Also, we design a routing mechanism based on conditional Bayes' theorem which adapts to changing network conditions, reduces packet losses, and ensures timely data delivery. HMGOC offers several advantages over other competitive techniques, including improved load balancing, minimized energy consumption and latency, and enhanced network throughput and lifespan. The simulation results demonstrate that the HMGOC technique beats the existing methods in terms of enhanced cluster stability and lifetime, increased packet deliverability, energy efficiency, reduced latency, and minimized overhead.

With the rapid development of Internet of Vehicles (IoV), crowd-sensing based on IoV is widely used in various fields. Traditional crowd-sensing uses a third-party service platform for information interaction, which has problems of worker location privacy leakage and imbalanced participation task fairness. To solve these problems, this paper proposes a combination of blockchain and crowd-sensing for location privacy protection (BCS-LPP) method in IoV. First, blockchain is introduced into BCS-LPP to prevent the leakage of user information by third-party service platforms. Second, providing workers with personalized location privacy level options, combined with Geohash encoding and order-preserving encryption to safeguard the confidentiality of workers' location privacy information. Finally, the fairness of worker participation in tasks and the quality of sensing data are guaranteed by verifying the sensing locations submitted by workers. Using real datasets, BCS-LPP is compared with existing schemes through experimental simulation. BCS-LPP can better ensure the quality of sensing data, protect workers' location privacy information, and enhance the fairness of user participation in tasks.

To establish reliable connection between Internet of Underwater Things (IoUT) devices and terrestrial data centers, this work first proposes a reconfigurable intelligent surface (RIS) aided multihop underwater wireless optical communication (UWOC) convergent with radio frequency (RF) uplink system. Specifically, the RIS carried by an autonomous underwater vehicle (AUV) is introduced into UWOC link to relax the line-of-sight (LOS) requirement and the maximal-ratio combining (MRC) receiver is adopted at the terrestrial data center to mitigate the RF link fading. It is assumed that the underwater thermocline channel is subject to the newly proposed Weibull-generalized gamma (WGG) turbulence distribution and the RF link composite fading follows Fisher-Snedecor F distribution. Additionally, the optical link misalignment is characterized by the zero-boresight pointing errors model. With the decode-and-forward (DF) relaying scheme, the analytical closed-form expressions of the outage probability (OP) and average bit error rate (ABER) of this system are mathematically achieved, and then the impacts of air bubbles, thermohaline gradient, the number of RIS elements, pointing errors, system structure, and the number of receive antennas are further investigated. Meanwhile, the analytical results are verified by Monte Carlo (MC) simulations. Results reveal that this hybrid system performance would degrade with the increased air bubble levels and thermohaline gradients. Notably, RIS can effectively alleviate the impact of underwater turbulence and this effect would be more pronounced as the number of RIS elements increases. This work will benefit the design and research of hybrid UWOC-RF system.