Mobility-aware edge-assisted 5G communication framework analysis for driver emotion recognition

Modern vehicles are equipped with sophisticated systems that continuously monitor both their mechanical condition and the well-being of passengers. In the vehicular network scenario, this availability of a vast amount of data has encouraged ever more the development of ML-based systems to enable highly reliable functionalities for supporting autonomous driving. However, a major challenge is to enable vehicles to process ML-based complex models quickly and efficiently. This problem can be solved by utilizing edge-based computing solutions where computing and storage resources available in network infrastructures are located near the vehicles. By offloading some of the processing tasks to these local resources, vehicles can achieve faster response times and enhanced efficiency. In this paper, we analyze the computing and communication performance of a federated multimodal distillation approach for driver emotion detection for a vehicular communication semi-urban scenario, which will be modeled by implementing stochastic geometry models. The aim of our analysis is to evaluate the impact of a complex ML-based approach on the communication infrastructure, where locally distilled models suitable for constrained devices are federated into a global model. We also investigate the impact on the network of the load due to the learning procedure when IID data and non-IID are considered. The numerical results highlight the strengths and weaknesses of the communication infrastructure when heterogeneous wireless technologies such as 5G and WiFi are involved in handling this type of approach for vehicles with limited computational resources.