Exploiting Big Data Analytics for Urban Planning and Smart City Performance Improvement

Abstract

The smart city notion facilitate interoperation among multiple disciplines to improve the Quality of Life (QoL) of urban citizens. Unceasingly growing urban networks has significantly increased the data processing complexity. In consequence, real-time data processing and analysis has become a major concern in modern smart city designing and implementation. Considering the challenges of existing smart cities, in this work we propose a smart city architecture embedded with Big Data Analytics (BDA). The utmost goal of the proposed scheme is to enhance the quality of real-time decision-making through efficient Big Data (BD) processing. The proposed architecture is in three folds to manage data collection, data processing, and data application. We evaluate the proposed BDA embedded smart city using authentic datasets on water consumption, traffic congestion, parking management, and air pollution measurements. The analysis offer useful insights for the community development, while ensuring the performance improvement of the proposed framework in terms of processing time and throughput.