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.

To meet the rising demand of mobile data, IEEE 802.11ac was recently developed to offer better network performance than previous IEEE 802.11 standards, representing the 5G Wi-Fi technology. One of the new technologies added to IEEE 802.11ac is a dynamic bandwidth channel access scheme that supports channel bonding (CB) with an up to 160 MHz bandwidth. However, the use of CB increases the number of stations sharing the same medium, resulting in an increased collision rate and severe interference caused by overlapping sub-channels. This problem is further aggravated in densely deployed wireless networks where the service ranges of access points (APs) are likely to be highly overlapping. To resolve these issues, we present an analytical throughput model that considers the effect of both collisions and interference. With this analytical model, we formulate the CB problem to optimize the throughput along with the traffic demand and propose a genetic algorithm for solving the problem. Further, a dynamic channel bonding algorithm (named DCB) is proposed to solve the problem with lower complexity. Extensive simulation results demonstrate that our proposed algorithm provides a higher performance than other recent dynamic CB algorithms with respect to the throughput achieved for the traffic demand.

Skiing is one of the most popular winter sports in the world. Even though the equipment has been improved for preventing injuries during skiing, injury risks of the lower extremity are still high. It is necessary to investigate injury risk by motion analysis during skiing. The wearable motion capture system consisting of inertial sensors and insole pressure sensors can be utilized due to restrictions of conventional optical motion capture system. In this study, the motions for short-and middle-turns during skiing were analyzed using a wearable motion capture system and the multi-scale computer simulation technology. Seven male certified ski coaches participated in this study and their full body motion and foot pressure data were simultaneously recorded by the wearable motion capture system. Joint kinematics and kinetics in the hip, knee and ankle of the right lower extremity were analyzed during short- and middle-turns. Even though a slight difference in the joint kinematics between two turns was predicted, the joint forces and moments for middle-turn were higher than those for short-turn. Because these higher joint forces and moments can result in osteoarthritis or ligament injury at the joint, injury risk of the joint for middle-turn may be higher than that for short-turn. This study confirms that the wearable motion capture system is useful for measuring the motion and plantar pressure data in outdoor sports such as skiing.

Analyzing human motion has involved the use of the Kalman filter for calculating the orientation of the inertial measurement unit (IMU), based on many current motion analysis systems. However, its pitfalls, including high computation costs and difficult implementation renders itself sub-optimal in the clinical environment. Meanwhile, a filter developed by Mahony et al., which was developed for vehicular studies, possessed features that overcame the former's shortcomings. In this study, we compare the efficacies of these two filters to determine the applicability of the Mahony filter in the clinic. Approximations from both systems, on the upper and lower body, prove the Mahony filter to perform as consistently and accurately as Kalman filter. Thus, the cheaper alternative presented by Mahony filter may be applicable in sports and medicine.