Big data analytics and smart cities: applications, challenges, and opportunities

A. Salih S. F. Kak Wafaa M. Abdullah + 4 lainnya

29 April 2021

Many policymakers envisage using a community model and Big Data technology to achieve the sustainability demanded by intelligent city components and raise living standards. Smart cities use different technology to make their residents more successful in their health, housing, electricity, learning, and water supplies. This involves reducing prices and the utilization of resources and communicating more effectively and creatively for our employees. Extensive data analysis is a comparatively modern technology that is capable of expanding intelligent urban facilities. Digital extraction has resulted in the processing of large volumes of data that can be used in several valuable areas since digitalization is an essential part of daily life. In many businesses and utility domains, including the intelligent urban domain, successful exploitation and multiple data use is critical. This paper examines how big data can be used for more innovative societies. It explores the possibilities, challenges, and benefits of applying big data systems in intelligent cities and compares and contrasts different intelligent cities and big data ideas. It also seeks to define criteria for the creation of big data applications for innovative city services.

Anestis Kousis Christos Tjortjis

2021

Smart cities connect people and places using innovative technologies such as Data Mining (DM), Machine Learning (ML), big data, and the Internet of Things (IoT). This paper presents a bibliometric analysis to provide a comprehensive overview of studies associated with DM technologies used in smart cities applications. The study aims to identify the main DM techniques used in the context of smart cities and how the research field of DM for smart cities evolves over time. We adopted both qualitative and quantitative methods to explore the topic. We used the Scopus database to find relative articles published in scientific journals. This study covers 197 articles published over the period from 2013 to 2021. For the bibliometric analysis, we used the Biliometrix library, developed in R. Our findings show that there is a wide range of DM technologies used in every layer of a smart city project. Several ML algorithms, supervised or unsupervised, are adopted for operating the instrumentation, middleware, and application layer. The bibliometric analysis shows that DM for smart cities is a fast-growing scientific field. Scientists from all over the world show a great interest in researching and collaborating on this interdisciplinary scientific field.

S. Hashim Mohsen Marjani A. Sali + 3 lainnya

2021

The notion of smart cities has remained under evolution as its global implementations are challenged by numerous technological, economic, and governmental obstacles. Moreover, the synergy of the Internet of Things (IoT) and big data technologies could result in promising horizons in terms of smart city development which has not been explored yet. Thus, the current research aims to address the essence of smart cities. To this end, first, the concept of smart cities is briefly overviewed; then, their properties and specifications as well as generic architecture, compositions, and real-world implementations are addressed. Furthermore, possible challenges and opportunities in the field of smart cities are described. Numerous issues and challenges such as analytics and using big data in smart cities introduced in this study offers an enhancement in developing applications of the above-mentioned technologies. Hence, this study paves the way for future research on the issues and challenges of big data applications in smart cities.

C. Leung Adam G. M. Pazdor Nikola N. Kokilev + 4 lainnya

1 Desember 2022

In the current era of big data, very large amounts of data are generating at a rapid rate from a wide variety of rich data sources. Embedded in these big data are valuable information and knowledge that can be discovered by big data science techniques. Transportation data and meteorological data are examples of big data. In this paper, we present a big data science solution for transportation analytics with meteorological data. In particular, we analyze the meteorological data to examine impact of different meteorological conditions (e.g., fog, rain, snow) on the on-time performance of public transit. Evaluation on real-life data collected from the Canadian city of Winnipeg demonstrates the practicality of our big data science solution for transportation analytics on bus delay caused by various meteorological conditions.

Laura-Diana Radu

13 September 2020

This paper aims to explore the most important disruptive technologies in the development of the smart city. Every smart city is a dynamic and complex system that attracts an increasing number of people in search of the benefits of urbanisation. According to the United Nations, 68% of the world population will be living in cities by 2050. This creates challenges related to limited resources and infrastructure (energy, water, transportation system, etc.). To solve these problems, new and emerging technologies are created. Internet of Things, big data, blockchain, artificial intelligence, data analytics, and machine and cognitive learning are just a few examples. They generate changes in key sectors such as health, energy, transportation, education, public safety, etc. Based on a comprehensive literature review, we identified the main disruptive technologies in smart cities. Applications that integrate these technologies help cities to be smarter and offer better living conditions and easier access to products and services for residents. Disruptive technologies are generally considered key drivers in smart city progress. This paper presents these disruptive technologies, their applications in smart cities, the most important challenges and critics.