Towards Emulating Internet-of-Vehicles on a Single Machine

Techniques for Human-Vehicle Interaction can be easily bounded by the limited amount of computational resources within the vehicles. Therefore, outsourcing the computations from vehicles into a powerful, centralized server becomes the prime method in practice. Such formalization between edge vehicles and centralized servers is denoted as the Internet-of-Vehicles, which forms multiple vehicles as a distributed system. However, there are no available supports to examine the feasibility and suitability of Human-Vehicle Interaction techniques, in the context of Internet-of-Vehicles. Such examinations are essential for newly proposed techniques, by providing experimental characterizations, suggesting detailed implementation schemes and understanding its real-world effects under Internet-of-Vehicle settings.

In this work, we report our progress in terms of a general-purpose and portable emulation platform, for examining the effects of novel Human-Vehicle Interaction techniques under the Internet-of-Vehicles setting. The key idea of our work is two-folded: (1) we provide an automatic extractor to retrieve the key patterns from Human-Vehicle Interaction workloads, so that our platform can facilitate with the needs of different scenarios/techniques; and (2) we leverage the configurable networking connections to provide abstractions regarding the interactions between edge vehicles and centralized servers, so that we can enable various types of emulations (e.g. geo-distributed applications). Our current progress is the finalization of the first prototype, and we leverage this prototype to characterize the impacts of a Deep-Neural-Network-driven in-vehicle application. Our results reveal the impacts of different implementations under the Internet-of-Vehicles setting, and our future works would focus on enhancing the characteristics of our prototype and scaling our study into a broader range of applications in Human-Vehicle Interactions.