Adverse weather conditions significantly challenge scene understanding of autonomous vehicles by degrading perception system performance. Despite advancements, the impacts of such conditions still require further investigation. In this paper, we propose a novel approach that leverages the advantages of Radar sensors against various weather types to improve robustness under foggy weather. Our framework refines Radar measurements through the Bayesian Filtering algorithm to enhance data quality and sparsity, generating informative representations as probabilistic Occupancy Grid Maps (OGM). We address sensor synchronization challenges to ensure accurate fusion across different modalities. We evaluate our framework and the effectiveness of the OGMs fused with LiDAR data under various fog densities. The experimental results demonstrated improvements in robustness and reduced detection errors when compared with LiDAR-only object detection.

The growing demands for low-latency and secure communication in vehicular networks necessitate reliable mobility management protocols. In this paper, we propose a proactive, hierarchical mobility management protocol designed to enhance the security of vehicular networks during handover processes. Our protocol emphasizes early registration and pre-authentication to mitigate risks associated with rapid vehicle mobility, such as impersonation, replay attacks, and data breaches. We evaluate the protocol’s effectiveness against common security threats in mobility management using real-world mobility traces.

Unmanned Aerial Vehicles (UAVs) are becoming essential visual monitoring technologies in various fields including urban planning, environmental monitoring, traffic surveillance, and disaster response. Nevertheless, there are some challenges in detecting objects in UAV imagery since the targets can be very small, densely packed, and acquired under unstable conditions such as shifting lighting, cluttered backgrounds, and flying motion. These factors make the already hard job of small object detection (SOD) even harder. SOD needs particular labelling strategies, model modifications, and deployment considerations.With a focus on small objects, this tutorial guides participants through the whole UAV imagery labelling and object identification pipeline. We start with annotation guidelines and dataset characteristics unique to aerial perspectives, followed by a general overview of the evolution of object detection architectures from two-stage detectors to real-time one-stage frameworks. The focus then moves to the latest YOLO-based techniques, emphasizing attention mechanisms, multiscale feature fusion, architectural modifications, and efficiency-focused designs specifically suited for UAV applications. We will also discuss enhancements to training, improvements in inference time, and accuracy vs. computational efficiency trade-offs for platforms with limited resources.By the end of the tutorial, participants will have a better understanding of the difficulties associated with UAV-based small objects detection, practical knowledge of labelling and model construction, and a taxonomy of efficient techniques for enhancing deployment in the real world.

Resource allocation is crucial in wireless systems, especially with the advent of mobile antennas (MAs), which enhance network performance, but introduce complexities in real-time optimization and computational overhead. Dominating Sets (DS) have emerged as a powerful tool in network design, sensor placement, and communication protocols due to their ability to ensure efficient coverage and connectivity. Leveraging DS for resource allocation enables the selection of representative nodes to manage resources effectively while maintaining robust network performance. In this paper, we conduct a comprehensive evaluation of DS algorithms in various graph models, analyzing key metrics such as the size, density, and coverage of the dominant set. Our results provide new insights into the comparative efficiency and applicability of DS-based strategies for resource allocation in various wireless networking scenarios.

For decades, Vehicular Networks have evolved to reach its ultimate goal, to provide modern, reliable, user-oriented, and real-time infotainment services and applications, while improving road safety and management. The recent advances and expansion in cellular networks communications and wireless access technologies have raised the bar to client demand and applications requirements. In the past few years, researchers and industries begun paving the way for the deployment of the fifth generation of cellular networks (5G) through enabling current network structures to accommodate the requirements of 5G systems. Henceforth, massive numbers of wireless technologies and devices are deployed and managed to deliver ultra-low latency and high-scalability. However, vehicles' mobility management and communication protocols are yet to be thoroughly investigated, adapted and reconfigured to address the current and future connectivity and resource allocation challenges [1-8].

Accurate traffic flow forecasting is crucial for smart-cities and traffic management in modern society. With the rapid increase in traffic information’s nonlinearity and complexity, Neural Networks-based models have been introduced to traffic flow forecasting for spatial and temporal dependencies extraction. In this paper, we present the feature engineering model of spatial, temporal, and spatial-temporal dependency in the traffic flow prediction problem. We consider the solution with Graph Convolutional Neural Network (GCN) for the spatial dependency modeling, Gated Recurrent Unit(GRU) for the temporal feature construction, and the sequential feature extraction for Spatial-temporal dependency. To explain the effectiveness of the proposed idea, we evaluate the models using real-world datasets. Experiments show that the models capture comprehensive Spatio-temporal correlations with sequential feature extraction outperforming the sole spatial and temporal models.

The next generation of sustainable vehicular networks are expected to have a wide range of access technologies, multi-homing capabilities, and traffic demand and pattern heterogeneity. In order to deliver massive data loads from various services and applications to end-users, network softwarization is a key contributor that mitigates challenges in heterogeneous networks by providing a shared interface platform. SDN-enabled vehicular network provides global snapshot of the network's status and connectivity. However, using a centralized control unit, brings many difficulties, including bottleneck problem and densification issues. A distributed control plane is an alternative, but it raises questions about where to deploy the control units and how many controllers are needed in a given network structure. In this article, we propose an energy-efficient adaptive controller management strategy for distributed software-defined vehicular networks using vehicles' mobility densities and communication latencies between switch-enabled access points. To reduce the number of controllers, the proposed method employs a split-and-merge clustering technique. The performance of the clustering solution was evaluated using realistic mobility traces and compared to several benchmark clustering solutions and variations. The results indicate the efficiency of the proposed scheme in terms of energy consumption, latency, and load on network entities.

Software-defined vehicular networks (SDVNs) have been a vital addition to the design of intelligent vehicular networks. SDVNs elevate the constraints of static hardware network devices to programmable units, provide a global view of the network status, and standardize the interface between different wireless access technologies. However, the static deployment and assignment of switches to control units do not consider vehicles rapid mobility and diverse densities. In this paper, we propose a mobility-based switch migration scheme for software-defined vehicular networks. The proposed scheme utilizes the vehicles’ mobility between switch-enabled roadside units to efficiently migrate selected switches to different controllers. The proposed scheme has been evaluated using the network simulator and reported its performance under realistic mobility traces and environment.

With the increasing demands for various wireless communication technologies and standards, new challenges arise in seamless connectivity among different techniques. Mobility management protocols face a new difficulty in vehicular network heterogeneity, from deployment issues to optimal handover management process. However, due to the dynamic environment in vehicular networks, providing the best quality of service is a critical issue. Additionally, conventional mobility management solutions do not consider user's preferences when selecting the next points of access. In this paper, we present a load balancing and QoS-aware handover scheme in Heterogeneous Vehicular Networks (Het-VeNET) in order to choose the least loaded network, while maintaining the high level of required quality of service. We define the vehicle's mobility and QoS measurements and demonstrate the stated handover process in a vehicular environment. The proposed scheme performance showed a higher rate of successful handoff and load balance on different cells and scenarios when compared to benchmark schemes.

Software-Defined Vehicular Networks has been a vital component for heterogeneous radio access technologies to support massive data load through various safety and infotainment applications. Elevating the constraint of static hardware network devices into a programmable unit and providing a global view of the network status and standard interface between heterogeneous radio access technologies. However, having a logically centralized control unit brings several challenges, including bottleneck problem and densification issues. A distributed control plane comes as a possible solution to the centralized control plane yet with several questions of where to deploy the control units and how many SDN controllers are needed in a given network structure. In this paper, we present an adaptive Flow-based controller deployment and assignment strategy for distributed Software-Defined Vehicular Networks through the utilization of the communication latencies between switch-enabled access points and their corresponding vehicles' flow over a time window. We evaluate the proposed method's performance in terms of end-to-end delay and load on the resulted controller's points and their cluster's set. The clustering method is compared to several types of static placement strategies, in which the proposed method showed a reduction in controllers' average delays while distributing the load among them over time.

Recently, the Vehicular Network (VN) has received a lot of attention from researchers around the world. By allowing wireless communication, VNs enable information exchange among vehicles, which in turn has allowed drivers to become more aware of their surrounding road conditions. Accordingly, road safety is improved. However, due to the fast speed and frequent changes of direction of vehicles, the network topology of VNs is transient in nature. Hence, achieving efficient data dissemination/content delivery is a critical issue in the VNs-environment. In this article, we will introduce a novel passive roadside unit (RSU) detection-based proactive (PRDP) handover scenario. Consequently, the overhead of the handover process can be reduced, and the probability of successfully established connections can be improved. More precisely, by taking advantage of the Doppler effects of the received beacon signal, the passive RSU detection (PRD) scheme is derived by the maximum likelihood estimation function. Then, in combination with the extended Kalman filter (EKF), the PRDP handover protocol is designed to improve the energy efficiency of the handover procedure in the VNs-environment. We conduct intensive simulations to verify the proposed RSU detection scheme, and the experimental results further evaluate the performance of the proposed energy-efficient proactive handover protocol.

Nowadays, to deal with driving safety-related issues and improve travel comfort, the VehiculAr NETwork (VANET) has gained tremendous attention from researchers in both academia and industry around the world. By taking advantage of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, the VANET can significantly enhance road safety and travel comfort by improving drivers' awareness of their surrounding road environment and providing entertainment-related data service for passengers, respectively. However, due to the highly dynamic nature of the network topology in VANET, how to achieve reliable data transmission and content delivery is a critical task for implementing VANETs. Accordingly, in this article, we provide a novel data-highway-assisted content delivery protocol for addressing the content delivery problem in VANETs, in which, we explore the advantages of the predicted vehicular traffic volume driven by a newly designed fast traffic flow prediction scheme. We evaluate the performance of the proposed traffic flow prediction scheme by using three different data sets with different vehicles traffic flow patterns are chosen from the England Highways data set. Moreover, extensive simulations have been implemented to evaluate the proposed content delivery protocol.

Wireless networks development to support the highly dynamic vehicular environment pose several significant challenges for vehicular network services and applications, in efforts to guarantee seamless communication. Intelligent Vehicular Networks goal is to provide high-quality services that can learn and forecast clients' needs and intentions. Machine Learning (ML) is one type of artificial intelligence that can be effective in utilizing the vehicular network's data to predict users movements and allocate resources ahead of time. In this paper, we propose a novel online ML-based Roadside Unit (RSU) prediction scheme for mobility management in Vehicular Networks, to provide seamless mobile connectivity to vehicles and enhance the performance of the prediction model. An Online Probabilistic Neural Network (O-PNN) prediction model is designed and adjusted for VANETs mobile IP protocol. Extensive simulation experiments were performed on the Network Simulator NS-2, and the performance of the prediction model is studied with different traffic and mobility scenarios. Our results showed a high accuracy rate in comparison to several other machine learning models.

Vehicular Networks will play a leading role in the next generation of Autonomous Driving (AD), as recent advances in vehicular networks are a promising solution for traffic management and congestion issues, as well as lane optimization. Wireless mobile communication in VANETs is essential for the content delivery of local and global information for intelligent operation decisions in autonomous driving control applications. However, the vehicles' high mobility and topology changes affect the performance of traditional mobility management protocols over VANETs. Therefore, an efficient mobility management solution that mitigates the challenges of vehicles' mobility is needed to support autonomous driving. In this paper, we present an efficient probabilistic neural network-based Road Side Unit (RSU) prediction scheme for autonomous driving control using vehicular networks. We evaluate the performance of the predictor against different machine learning models. Our results showed a high accuracy rate in comparison to several neural network models in various mobility environments.

In recent years, with the development of the Internet of Things (IoT) and the Vehicular Networks (VNets), a large number of computers and sensors equipped on different vehicles (e.g., onboard CPU, camera, GPS, etc.) can not only help the vehicle to collect its own surrounding environment information, but also share those information with other participants in the transportation system through Vehicle-to-everything (V2X) technique. This ability to share information further makes VNets a precious resource for information and resources, which can support the vehicular edge computing (VEC) environment. However, due to the high moving speed of vehicles and the relative motion between vehicles, the topology of vehicle networking is highly dynamic. How to estimate the number of vehicles and the time period that they can form a vehicular cloudlet in a road segment is a challenging task for enabling VEC. Hence, in the paper, we present a queueing model-assisted traffic density estimation scheme to derive and analyze some essential parameters for implementing VEC, i.e., the vehicular cloudlet existence probability and the corresponding lifetime. We further demonstrate the results derived by the proposed scheme.

Currently, traffic congestion has become a part of daily life of people in the cities around the world, and impacts people's lives adversely, e.g., the extra time spent on commuting, the extra exhaust emissions, etc. In order to reduce the effects of congestion on our lives, intensive research efforts have been proposed on this issue. Intelligent Transportation System (ITS) is one of potential solutions to enable various applications to improve road safety and travel comfort, and has gained a lot of attention from researchers around the world. In order to efficiently manage the transportation system and reduce traffic congestion, one of the paramount problems needed to be solved in ITS is the accurate traffic prediction. In this article, we firstly combine Fourier analysis with wavelet denoising technique to cope with the traffic flow forecasting problem. A two-layer fast Fourier transform (FFT)-based traffic prediction scenario is proposed, in which the discrete wavelet transform (DWT) with two different threshold values are adopted to decompose the high-frequent-noise and identify low-frequent traffic flow changing trend from the original data. Three different data sets with different traffic flow patterns are chosen from the England Highways data set to test our proposed work. Intensive simulations are implemented to verify the proposed work.

The data dissemination and content delivery is a challenging research subject in the Internet of Things (IoT). Especially, in the Vehicular Networks environment, the network topology has the transient nature, due to the fast-moving velocity of the vehicles. One potential solution to this task is to improve the probability of the successful communication and content delivery. Hence, in this paper, we propose a novel and simple passive road side unit (RSU) localization scheme to estimate the location of the RSU, by which the vehicle can pre-determine the desired RSU to communicate based on its own position and routing information. By exploring the Doppler effects of the received signal, the RSU location estimator is derived by the maximum likelihood estimation method. Experimental results verify the correctness of the proposed estimation scheme.

Intelligent Transportation Systems have recently received great deal of attention and Vehicular networks and its applications represent a major part of ITS. Many vehicular network applications require accurate location information to improve their performance. Over the past years, many researchers worked on state prediction/estimation techniques in tracking, navigation applications for mobile ad hoc networks and wireless sensor networks. Yet, few were into the field of Vehicular networks. In this paper, We study five different movement prediction models and their efficiency and effectiveness for VANETs. We compare them using both real vehicle mobility traces of taxi cabs and generated mobility traces from SUMO.

Vehicular traffic accidents are a crucial problem in big urban centers. Nearly one million people die in road crashes each year. In this paper, we propose a predictive vehicular collision detection protocol for VANETs. The proposed protocol takes advantage of the vehicle-to-vehicle communication in VANETs to predict potential collisions with vehicles in the proximity. Simulation results show that our proposed protocol achieves 92% accuracy in detecting collisions using real case Ottawa urban scenarios and several accidents scenarios. Also, applying a prediction model to estimate future trajectories of nearby vehicles, has significantly reduced the overhead of transmitted packets.

Summary Simulations have been used extensively for evaluating scenarios, which are very difficult, costly or impractical to implement in real systems. Testing in a synthetic, realistic environment provides a means to determine the viability of solutions. Simulations have proved to be very useful in the verification of algorithms and protocols, offering tools for testing them in different situations. The simulation of vehicular area networks pose additional challenges as realistic mobility models are crucial and must be incorporated in scenario elements while applications and communication protocols are tested. Several simulators and simulation frameworks have been designed that aim to synthetically reproduce communication and mobility of vehicles as realistically as possible. The majority of such simulators merge pre‐existing networking and mobility simulators, which add issues regarding compatibility and realism. Such simulators present limited run‐time 3D visualization tools, essential for providing immersive environments. Therefore, in this paper, we propose real‐time simulation and 3D visualization for vehicular networks of realistic scenarios. This proposed simulation system generates output in real time, making use of 3D‐modelled real‐world maps and effectively generating visualization as elements are updated in the simulation. Experiments have been conducted with simulation and visualization components to evaluate delays and performance of the proposed simulator. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract In the recent years, we are witnessing a growing interest into the design of smart vehicles and smart roads for Intelligent transportation systems. Vehicles as part of the Internet of Things should provide to the driver and passenger with a variety of services using efficient gateway discovery mechanism while maintaining a certain level of security and authentication to avoid potential malicious attacks. In this paper, we propose a secure hybrid adaptive gateway discovery and communication protocol for smart vehicular networks, which we refer to as SEGAL. Our proposed SEGAL protocol is based upon building a secure clustered vehicular network, and permits the exchange of gateway discovery messages through authenticated clusterheads and cluster members. We shall present the design of our protocol, and describe how it can overcome the possible malicious attacks that might harm the network. Then, we report its efficiency and scalability using an extensive set of simulation experiments using Ns‐2 simulator. Our results indicate that the proposed SEGAL protocol is scalable while achieving high success rate, low response time and dropping rate. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract A great interest in vehicular ad‐hoc networks has been noticed by the research community. General goals of vehicular networks are to enhance safety on the road and to ensure the convenience of passengers by continuously providing them, in real time, with information and entertainment options such as routes to destinations, traffic conditions, facilities' information, and multimedia/Internet access. Indeed, time efficient systems that have high connectivity and low bandwidth usage are most needed to cope with realistic traffic mobility conditions. One foundation of such a system is the design of an efficient gateway discovery protocol that guarantees robust connectivity between vehicles, while assuring Internet access. Little work has been performed on how to concurrently integrate load balancing, quality of service (QoS), and fault tolerant mechanisms into these protocols. In this paper, we propose a reliable QoS‐aware and location aided gateway discovery protocol for vehicular networks by the name of fault tolerant location‐based gateway advertisement and discovery. One of the features of this protocol is its ability to tolerate gateway routers and/or road vehicle failure. Moreover, this protocol takes into consideration the aspects of the QoS requirements specified by the gateway requesters; furthermore, the protocol insures load balancing on the gateways as well as on the routes between gateways and gateway clients. We discuss its implementation and report on its performance in contrast with similar protocols through extensive simulation experiments using the ns‐2 simulator. Copyright © 2013 John Wiley & Sons, Ltd.