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.

A Personalized Healthcare Monitoring System for Diabetic Patients by Utilizing BLE-Based Sensors and Real-Time Data Processing

“…A recent study utilized Apache Kafka for stream processing. Fernandez-Rodriguez et al proposed real-time data streaming for vehicles in a smart city [ 69 ]. Their results revealed that Apache Kafka achieved a greater scalability, faster response, and reduced cost compared to traditional systems.…”

“…Integration of Apache Kafka and MongoDB can be utilized for real-time data processing for handling healthcare sensor data. Previous studies have shown that both technologies can be utilized for real-time data processing so that an incoming large volume of streaming data can be quickly handled, stored, and presented in real-time [ 69 , 70 ]. Thus, in our study, Apache Kafka and NoSQL MongoDB were used for real-time data processing to monitor the vital signs data from diabetic patients and presented in real time.…”

Hadoop Oriented Smart Cities Architecture

“…As most of the mentioned platforms do not provide real-time data streaming analysis, an exciting contribution is the infrastructure that is described in [37]. The architecture was developed for real-time processing of data provided by vehicles in traffic, and for sending recommendations to drivers, using a simulator to test the scalability and response speed of the solution.…”

An Open Source-Based Real-Time Data Processing Architecture Framework for Manufacturing Sustainability

“…The result showed that Kafka achieves much higher throughput than conventional messaging systems (such as ActiveMQ and RabbitMQ) [28]. Fernandez-Rodriguez et al (2017) proposed real-time vehicle data streaming models for a smart city [29]. The proposed system gathers information from drivers in a big city, analyzing that information and sending real-time recommendations to improve driving efficiency and safety on roads.…”