Road safety has become increasingly efficient by incorporating technologies such as Vehicle-to-Everything (V2X) communication and Machine Learning (ML) algorithms. These solutions enable real-time data exchange between vehicles and infrastructure, helping predict and prevent accidents. The latency in transmitting this information is a critical factor that impacts the system’s effectiveness. In this context, this work investigates vehicle collision prevention and uses simulations with different collision scenarios to train predictive ML models. As a result, trained models were capable of predicting collisions up to five seconds in advance and accurately classifying different risk scenarios. This demonstrates the potential of these technologies to enhance safety and efficiency in traffic.

Federated Learning is a distributed approach in which multiple devices collaborate to train a shared global model. During its training, client devices must communicate their gradients to update the global model. This incurs significant communication costs (bandwidth utilization and number of messages exchanged), leading to many challenges (communication bottlenecks and scalability issues). Furthermore, the heterogeneous nature of clients’ datasets poses an extra training challenge. In this sense, we introduce FedCCSKD, a Federated Clustered Client Selection and Knowledge Distillation training algorithm, to decrease the overall communication costs. FedCCSKD is an innovative combination of: (i) client selection, and (ii) knowledge distillation approaches with three main objectives: (i) reducing the number of devices training at every round; (ii) increasing convergence speed; and (iii) mitigating the effect of clients’ heterogeneous data on the global model effectiveness. Our experimental evaluations on MNIST and MotionSense datasets demonstrate that FedCCSKD is highly efficient in training the global model until convergence. FedCCSKD reaches a higher accuracy score and faster convergence than state-of-the-art baseline models. Our results also show higher performance when analyzing the accuracy scores on the clients’ datasets.

The growing number of vehicles has led to increased emissions of polluting gases, necessitating accurate forecasting for effective mitigation strategies and sustainable urban development. Leveraging computational resources in vehicles, this study presents a framework, called EcoPredict, for predicting CO2emissions in collaborative vehicular network environments. The framework implements three forms of learning methods—centralized, federated, and split—using urban sensor networks for data collection. Experiments carried out in realistic vehicular mobility scenarios demonstrate the framework’s robustness and efficiency in providing real-time emission predictions. Each learning architecture has its own advantages and limitations regarding performance, training time, latency, communication overhead, and data privacy. Therefore, this work aims to assess their performance to analyze their effectiveness in urban environments.

Vehicular Edge Computing (VEC) has emerged to offer cloud computing services closer to vehicular users by combining vehicles and edge computing nodes into Vehicular Clouds (VCs). In this scenario, an intelligent task scheduler must decide which VC will run which tasks, considering contextual aspects like vehicular mobility and tasks’ requirements. This is important to minimize both processing time and monetary costs. However, such direct optimization can lead to unfairness in resource usage, easily leading to (as we will show) decreased performance. Towards this end, in this work, we propose FARID, a task scheduling mechanism that considers contextual aspects of its decision process and applies a probabilistic selection function on VCs to balance the processing load and increase the fairness in the use of vehicular resources. Compared to state-of-the-art solutions, FARID has a higher level of fairness and can schedule more tasks while minimizing monetary costs and system latency.

Vehicular Edge Computing (VEC) is a promising paradigm that provides cloud computing services closer to vehicular users. In VEC, vehicles and communication infrastructures can form pools with computational resources to meet vehicular services with low-latency constraints. These resource pools are known as Vehicular Cloud (VC). The usage of VC resources requires a task scheduling process. In this case, depending on its complexity, a vehicular service can be divided into different tasks. An efficient task scheduling needs to orchestrate where and for how long such tasks will run, considering the available pools, the mobility of nodes, and the tasks deadline constraints. Thus, this article proposes an efficient VC task scheduler based on an approximation heuristic and resources prediction to select the best VC for each task, called MARINA. MARINA aims to analyze the behavior of vehicles that share their computational resources with the VC and make scheduling decisions based on the mobility (VC availability) of these vehicles. Simulation results under a realistic scenario demonstrate the efficiency of MARINA compared to existing state-of-the-art mechanisms in terms of the number of tasks scheduled, monetary cost, system latency, and Central Processing Unit (CPU) utilization.

Vehicular Edge Computing is a promising paradigm that provides cloud computing services closer to vehicular users. Vehicles and communication infrastructure can cooperatively provide vehicular services with low latency constraints through vehicular cloud formation and using these computational resources via task scheduling. An efficient task scheduler must decide which cloud will run the tasks, considering vehicular mobility and task requirements. This is important to minimize processing time and, consequently, monetary cost. However, the literature solutions do not consider these contextual aspects together, degrading the overall system efficiency. This work presents EFESTO, a task scheduling mechanism that considers contextual aspects in its decision process. The results show that, compared to state-of-the-art solutions, EFESTO can schedule more tasks while minimizing monetary cost and system latency.

Due to the increased computational capacity of Connected and Autonomous Vehicles (CAVs) and worries about transferring private information, it is becoming more and more appealing to store data locally and move network computing to the edge. This trend also extends to Machine Learning (ML) where Federated learning (FL) has emerged as an attractive solution for preserving privacy. Today, to evaluate the implemented vehicular FL mechanisms for ML training, researchers often disregard the impact of CAV mobility, network topology dynamics, or communication patterns, all of which have a large impact on the final system performance. To address this, this work presents FLEXE, an Open Source extension to Veins that offers researchers a simulation environment to run FL experiments in realistic scenarios. FLEXE combines the popular Veins framework with the OpenCV library. Using the example of traffic sign recognition, we demonstrate how FLEXE can support investigations of FL techniques in a vehicular environment.

Intelligent Transportation Systems (ITSs) will be part of our daily lives, where new services are bringing novel challenges for smart cities. The ITS services rely on vehicular clouds (VC) to aggregate tasks from other vehicles to provide cloud services closest to the vehicular users. However, the resource and task allocation processes in dynamic and mobile environments are still open issues. This paper proposes a task optimization mechanism based on the meta-heuristic algorithm of the Grey Wolf Optimizer, called TOVEC. It aims to improve the usage of the available resources in a VC and maximizing task allocation. Simulation results showed that the TOVEC increases the number of tasks served by up to 34.2%, maximizes the use of resources by up to 21.5%, and improves the allocation reward by up to 24.7% compared to Greedy and Dynamic Programming (DP) methods.

Efficient content distribution over vehicular ad hoc networks (VANETs) is a challenging task due to highly topology changes caused by vehicle mobility. In this context, Vehicle Named-Data Networks (VNDN) architecture improves the performance and reliability in delivering content by providing content-centric network communication and caching capabilities. However, the success of VNDN architecture depends on mitigating the broadcast storm problem during the cache discovery process, where the network performance impairment occurs due to the waste of resources generated. In this paper, we propose a receiver-based cache discovery protocol based on degree-centrality for VNDN, called CLYMENE. The protocol paves the way for efficient content distribution by minimizing the broadcast storm problem. Simulation results show that CLYMENE enhances the cache discovery by 80.59% while allowing a content delivery rate of 39.49% and reducing the number of transmissions in the cache discovery process at 70.65% compared to existing protocols.

The automotive industry has been continuously investing in the modernization of the vehicles by the addition of more sensors and computational power. With this evolution, Intelligent Transportation Systems (ITS) make up a services framework that seeks to mitigate problems in the road sector. Many ITS services are facilitated by creating vehicular clouds (VCs) by using the communication capabilities of other vehicles to provide cloud services closer to vehicular applications. However, often the computational resources present in the vehicles are underutilized. For this reason, we propose in this work a mechanism that efficiently allocates computational tasks to be performed in VCs. Simulation results on a realistic mobility trace show that, with our mechanism, tasks are more allocated, the reward from allocating these tasks was higher, resource waste was minimized, and less CPU is used in the allocation processing. Also, the proposed mechanism is statistically close to a globally optimal solution.

Virtual Machines (VMs) offer great flexibility, reusability, and portability to manage applications in physical resources of a Cloud data center or a mobile cloud. These VMs may suffer a migration from one datacenter to another or from one cloudlet to another, due to the characteristics of vehicular cloud, such as high mobility and the need to meet the demands of a particular service. In this way, establishing a VM migration policy in a vehicular cloud becomes a challeng e, since it has to deal with the unique challenges of vehicular networks, as well as to meet the demands of users' services without virtual machine migration, which impacts on the performance of such service. In this paper, we propose a decision policy based on multiple attributes to migrate VM in a vehicular cloud scenario. In this way, the proposed policy allows to decide more quickly if a VM migration should be carried out and to which cloudlet this machine should be migrated. Simulation results showed that the proposed policy reduced in 2% the amount of VM migration in the network, decreased in 3% the blockages of the migration requests, as well as a reduction in the inference time of approximately 5 ms.

With the growth of large urban centers, some problems arise and solutions must be sought to contain them. In this context, traffic congestion is one such problem, where road infrastructure does not follow the high growth in the number of vehicles. Traffic Management Systems (TMS) arise to mitigate traffic-related problems, with automatic detection of slow roadways and vehicle rerouting to avoid such routes. Such applications are supported by the Vehicular Ad hoc NETworks (VANETs), where traffic information are disseminated between the vehicles or central server, and thus the better decisions about traffic management can be made. However, the data dissemination in VANETs is a challenging task, due to the short-range communication and high node mobility. Thus, this paper introduces a protocol for Data Dissemination based on Metrics of Complex Networks, called CRONOS. It provides data dissemination of traffic data with low overhead and high coverage. Simulation results show that CRONOS reduced the number of transmissions by 95%, the congestion time by 48.95%, and travel time by 18.11% for a TMS application.

Vehicular Ad-hoc NETworks (VANETs) are composed of moving vehicles with the ability to process, store, and communicate via wireless medium. VANETs promise a wide scope of services, such as, safety and security, traffic efficiency, and others. For instance, a VANET application can detect, control and reduce traffic congestion based on data that describes traffic patterns. However, disseminating data in VANET is a challenging task, due to its particular characteristics, i.e., heterogeneous density, short-range communication, and node mobility. Since, existing protocols for data dissemination do not effectively address the high overhead, in this paper, we proposed a Data Dissemination protocol Based on Centrality (DDBC) for urban scenarios. The simulation results show that DDBC protocol offers good efficiency in terms of delays and overhead, while achieve network coverage around 90%.