Federated learning (FL) enables vehicles to train machine learning (ML) models in a distributed manner for intelligent transportation system (ITS) services without sharing raw data, thereby preserving privacy and reducing communication overhead on roadside units (RSUs). However, due to their mobility and limited communication resources, vehicles participating in FL may be unable to transmit their updated models after local training if they move out of the RSU’s communication range. This limitation not only increases the convergence time of the global model but also wastes the computing resources of the participating vehicles used for training, as their trained models cannot be used to update the global model. To address these issues, this paper proposes a vehicle-to-vehicle communication-assisted FL (V2V-FL) mechanism for vehicular networks, in which vehicles moving out of the RSU’s coverage transmit their updated models to the RSU via a V2V link with limited communication resources, ensuring that more participating vehicles contribute their updated models to FL. An optimization problem for updated model forwarding with resource allocation is formulated to maximize the completion probability of participating vehicles while minimizing the total delay incurred during the training process. Since the formulated problem is a mixed-integer non-convex problem, it is decomposed into two subproblems, which are solved using the Karush-Kuhn-Tucker (KKT) conditions and a heuristic algorithm. Simulation results show that the proposed scheme outperforms other benchmark methods in terms of the total delay and convergence performance.

This paper proposes a UAV-assisted cooperative perception framework for vehicular networks, ensuring the timely transmission of image feature extraction results from vehicles to the RSU, thereby enabling cooperative perception such as HD map construction and object-level tracking in smart cities. We analyze the success probability that vehicles can complete image feature extraction and transmit the result before leaving the RSU’s communication range with the assistance of UAVs. The simulation results demonstrate that the proposed algorithm outperforms benchmark schemes in terms of success probability.

This paper proposes a delay and energy-aware clustering-based client selection scheme for federated learning in vehicular networks. We propose an algorithm that selects the appropriate number of vehicles for local training, minimizing delay and energy consumption while ensuring model performance. The simulation results demonstrate that the proposed algorithm achieves lower delay and energy consumption compared to benchmark methods, for both IID (independent and identically distributed) and non-IID datasets.

This paper proposes a proactive aerial base station (ABS) deployment framework for hotspots that optimizes the placement of ABSs based on mobility and data traffic demand prediction using a LSTM model. We design a greedy-based ABS deployment algorithm to solve the data rate maximization problem, which is known to be NP-hard. Simulation results demonstrate that the proposed algorithm achieves a higher data rate with fewer ABSs compared to benchmark methods.

Owing to remarkable improvements in deep neural networks (DNNs), various computation-intensive and delay-sensitive DNN services have been developed for smart IoT devices. However, employing these services on the devices is challenging due to their limited battery capacity and computational constraints. Although edge computing is proposed as a solution, edge devices cannot meet the performance requirements of DNN services because the majority of IoT applications require simultaneous inference services, and DNN models grow larger. To address this problem, we propose a framework that enables parallel execution of partitioned and offloaded DNN inference services over multiple distributed edge devices. Noteworthy, edge devices are reluctant to process tasks due to their energy consumption. Thus, to provide an incentive mechanism for edge devices, we model the interaction between the edge devices and DNN inference service users as a two-level Stackelberg game. Based on this model, we design the proposed framework to determine the optimal scheduling with a partitioning strategy, aiming to maximize user satisfaction while incentivizing the participation of edge devices. We further derive the Nash equilibrium points in the two levels. The simulation results show that the proposed scheme outperforms other benchmark methods in terms of user satisfaction and profits of edge devices.

Edge caching has been emerged to avoid redundant data transmission to the cloud by caching the popular contents on edge networks. While existing solutions for edge caching have focused on optimizing the hit ratio at the edge, they did not address the burden on edge servers under a massive content requests circumstance. The massive amount of content requests can cause the waiting delay at the edge server, leading to an unexpected long content delivery delay. Therefore, in this paper, we propose the distributed edge caching scheme that can reduce the content delivery delay by considering the waiting delay at the edge server.

Since Internet of Vehicles (IoV) generate and consume huge amount of data, caching becomes indispensable technique to provide better Internet services in IoV. However, deployment and operation of infrastructure to cache the massive vehicular data is very costly. To tackle this problem, we propose a vehicular caching scheme that reduces data delivery delay and cost using parked vehicles.