Data Analytics for Smart cities: Challenges and Promises

TL;DR

This paper surveys data analytics in smart cities, focusing on smart mobility, highlighting challenges, potential solutions, and the role of AI and machine learning in enhancing decision-making and reducing environmental impact.

Contribution

It introduces a comprehensive framework for smart city decision-making, integrating data capturing, analysis, and AI-driven decision support specifically for smart mobility.

Findings

Identifies key challenges in smart city data analytics.

Proposes a universal decision-making framework for smart mobility.

Discusses the application of machine learning and deep learning algorithms.

Abstract

The explosion of advancements in artificial intelligence, sensor technologies, and wireless communication activates ubiquitous sensing through distributed sensors. These sensors are various domains of networks that lead us to smart systems in healthcare, transportation, environment, and other relevant branches/networks. Having collaborative interaction among the smart systems connects end-user devices to each other which enables achieving a new integrated entity called Smart Cities. The goal of this study is to provide a comprehensive survey of data analytics in smart cities. In this paper, we aim to focus on one of the smart cities important branches, namely Smart Mobility, and its positive ample impact on the smart cities decision-making process. Intelligent decision-making systems in smart mobility offer many advantages such as saving energy, relaying city traffic, and more importantly, reducing air pollution by offering real-time useful information and imperative knowledge. Making a decision in smart cities in time is challenging due to various and high dimensional factors and parameters, which are not frequently collected. In this paper, we first address current challenges in smart cities and provide an overview of potential solutions to these challenges. Then, we offer a framework of these solutions, called universal smart cities decision making, with three main sections of data capturing, data analysis, and decision making to optimize the smart mobility within smart cities. With this framework, we elaborate on fundamental concepts of big data, machine learning, and deep leaning algorithms that have been applied to smart cities and discuss the role of these algorithms in decision making for smart mobility in smart cities.