| Title | A Smart City IoT Crowdsensing System Based on Data Streaming Architecture |
|---|---|
| ID_Doc | 37801 |
| Authors | Labus, A; Radenkovic, M; Neskovic, S; Popovic, S; Mitrovic, S |
| Title | A Smart City IoT Crowdsensing System Based on Data Streaming Architecture |
| Year | 2022 |
| Published | |
| Abstract | The subject of this paper is data streaming in IoT crowdsensing systems. The goal of this paper is to present a way of designing a scalable IoT crowdsensing system that enables design of various business models for smart city projects. The system designed in such a way is capable of handling an increasing number of users while maintaining acceptable performance. Performance of the system can be measured in response latency, which allows for real-time tracking of crowdsensing parameters. The first part of the paper deals with data streaming concepts and software solutions, with a particular focus on Apache Kafka. The second part presents the designed system for crowdsensing in smart city environments. The designed system allows for use of mobile and Arduino devices as input data for the Kafka cloud cluster in order to provide crowdsourcing insights in real-time. The primary way that users can utilize these insights is through a web or mobile application, where various data visualizations can be presented. The development of a system based on the proposed model can allow for easy access to recent crowdsourced data, and real-time smart city indicators such as air pollution. |
Similar Articles
| ID | Score | Article |
|---|---|---|
67352
|
Capponi, A; Fiandrino, C; Franck, C; Sorger, U; Kliazovich, D; Bouvry, P Assessing Performance of Internet of Things-based Mobile Crowdsensing Systems for Sensing as a Service Applications in Smart Cities(2016) | |
| 43682
|
Alvear, O; Calafate, CT; Cano, JC; Manzoni, P Crowdsensing in Smart Cities: Overview, Platforms, and Environment Sensing Issues(2018)Sensors, 18, 2 | |
| 44492
|
Raptis, TP; Cicconetti, C; Falelakis, M; Kalogiannis, G; Kanellos, T; Lobo, TP Engineering Resource-Efficient Data Management for Smart Cities with Apache Kafka(2023)Future Internet, 15, 2 | |
| 42401
|
Mathew, SS; El Barachi, M; Kuhail, MA CrowdPower: A Novel Crowdsensing-as-a-Service Platform for Real-Time Incident Reporting(2022)Applied Sciences-Basel, 12, 21 | |
| 38213
|
Habibzadeh, H; Qin, Z; Soyata, T; Kantarci, B Large-Scale Distributed Dedicated- and Non-Dedicated Smart City Sensing Systems(2017)Ieee Sensors Journal, 17, 23 | |
| 45513
|
Alkhelaiwi, A; Grigoras, D Scheduling Crowdsensing Data to Smart City Applications in the Cloud(2016) | |
| 39602
|
Nasiri, H; Nasehi, S; Goudarzi, M Evaluation of distributed stream processing frameworks for IoT applications in Smart Cities(2019)Journal Of Big Data, 6, 1 | |
| 45385
|
Shahrour, I; Xie, XY Role of Internet of Things (IoT) and Crowdsourcing in Smart City Projects(2021)Smart Cities, 4, 4 |