Benchmarking real-time vehicle data streaming models for a smart city

Abstract

The information systems of smart cities offer project developers, institutions, industry and experts the possibility to handle massive incoming data from diverse information sources in order to produce new information services for citizens. Much of this information has to be processed as it arrives because a real-time response is often needed. Stream processing architectures solve this kind of problems, but sometimes it is not easy to benchmark the load capacity or the efficiency of a proposed architecture. This work presents a real case project in which an infrastructure was needed for gathering information from drivers in a big city, analyzing that information and sending real-time recommendations to improve driving efficiency and safety on roads. The challenge was to support the real-time recommendation ser- vice in a city with thousands of simultaneous drivers at the lowest possible cost. In addition, in order to estimate the ability of an infrastructure to handle load, a simulator that emulates the data produced by a given amount of simultaneous drivers was also developed. Experiments with the simulator show how recent stream processing platforms like Apache Kafka could replace custom-made streaming servers in a smart city to achieve a higher scalability and faster responses, together with cost reduction