Big Data-driven Architecture for Crowdsensing Systems in Smart Cities

Abstract

The subject of this paper is the development of a crowdsensing system with a big data architecture that aims to efficiently collect, process, and analyze data from various sensors deployed in smart cities. The primary goal of this research is to propose an architecture that enables real-time data collection, processing, and analysis for noise, vibrations, healthcare, and pollution monitoring. The proposed architecture is presented in detail, highlighting its components and their interactions. By leveraging asynchronous event-based communication and integrating Apache Kafka and Apache Spark, the proposed system offers improved decision-making capabilities and resource management for urban sustainability. This research contributes to the field of smart cities and crowdsensing by proposing a big data architecture that enables effective collection of data, processing, and analysis for noise, vibrations, healthcare, and pollution monitoring.