Understanding the data-processing challenges in Intelligent Vehicular Systems

Vehicular sensors able to perceive and measure the environment, ranging from in-vehicle sensors to speed cameras, are revolutionizing how technology can interact with our daily lives, enabling Intelligent Vehicular Systems (IVSs). These sensors generate large volumes of data which can reveal useful information for enhancing the sustainable development (through improved utilization of resources), as well as the safety and functionality of the system. In this context, a key challenge is to reduce the large data streams into manageable sets of valuable information in a real-time, reliable and cost-affordable fashion. Due to the data volume size and velocity, relying exclusively on traditional data processing systems, such as databases and batch processing, is no longer a suitable option, since it is not feasible to store the data to later process it. Moreover, careful decisions should be made to leverage the existing computing capacity, from embedded devices found in the IVSs to cloud infrastructures. In this paper we study trade-offs of possible options for data-stream processing models and computing infrastructures. Through building an experimental platform that emulates realistic components of a future deployable IVS and validating two different data-stream processing systems with a wellknown benchmark for IVSs, we study options and trade-offs in real-time data stream processing in IVS infrastructures. Our evaluation shows that existing data-stream processing models can be leveraged in different ways, based on the processing requirements.