Smart Cities in the Development of Sustainable Infrastructure—Systematic Literature Review of Two Decades Research

2021 ◽  
pp. 55-64
Author(s):  
Tharun Dolla ◽  
Reena Bisht ◽  
Boeing Laishram
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Smart Cities ◽  
Sustainable Infrastructure

The governance of smart cities: A systematic literature review

Cities ◽  
2018 ◽  
Vol 81 ◽  
pp. 1-23 ◽  
Author(s):  
Robert Wilhelm Siegfried Ruhlandt
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Smart Cities

Intelligent Surveillance Systems for Smart Cities: A Systematic Literature Review

2021 ◽  
pp. 135-147
Author(s):  
Nour Ahmed Ghoniem ◽  
Samiha Hesham ◽  
Sandra Fares ◽  
Mariam Hesham ◽  
Lobna Shaheen ◽  
...  
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Smart Cities ◽  
Surveillance Systems ◽  
Intelligent Surveillance

Smart Mobility: A Systematic Literature Review of Mobility Assistants to Support Drivers in Smart Cities

2021 ◽  
pp. 256-266
Author(s):  
Nelson Pacheco Rocha ◽  
Ana Dias ◽  
Gonçalo Santinha ◽  
Mário Rodrigues ◽  
Carlos Rodrigues ◽  
...  
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Smart Cities ◽  
Smart Mobility

A systematic literature review and analysis towards developing PPP models for delivering smart infrastructure

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nimesha Sahani Jayasena ◽  
Daniel W.M. Chan ◽  
Mohan Kumaraswamy
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Smart Cities ◽  
Relevant Literature ◽  
Developed Countries ◽  
Sustainable Urban Development ◽  
Environmental Pressures ◽  
Content Type ◽  
Holistic Review ◽  
Smart Infrastructure

PurposeRising human aspirations leading to rapid urbanisation, amidst climate changes and other environmental pressures have aggravated the needs for better-focused sustainable urban development in general as well as for smart and sustainable cities in particular. Indeed, smart infrastructure (SI) development is a prerequisite for smart cities (SCs). However, inadequate funding and expertise for such SI development pose profound challenges that may be partially addressed by mobilising private finance and efficiencies through collaborative public–private partnership (PPP) models. This paper provides a holistic review and analysis of the relevant literature, as a basis for proposing such PPP models for developing SI.Design/methodology/approachA systematic literature review helped to fulfil the aim of this paper in the first phase of the underlying longer-term study. Authoritative search engines like Scopus and Web of Science indexed articles were reviewed and analysed, 85% of these being journal articles.FindingsSCs that necessarily include SI are important in overcoming current urban challenges in developing and developed countries. Given shortfalls in traditional procurement and funding models, special PPP models are required for SI development. After identifying the relevant needs, drivers, barriers and challenges in different countries, a general indicative framework is developed to illustrate how the various interacting force fields can be harnessed to develop the envisaged PPP models that can complement non-PPP procurement models.Originality/valuePPP for SI development is a relatively new, hence, under-researched topic. This desktop review and analysis provide a useful launching pad for the development of SI through overcoming the potential challenges in traditional procurement and financial models.

scholarly journals Open Data Based Machine Learning Applications in Smart Cities: A Systematic Literature Review

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2997
Author(s):  
Luminita Hurbean ◽  
Doina Danaiata ◽  
Florin Militaru ◽  
Andrei-Mihail Dodea ◽  
Ana-Maria Negovan
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Smart City ◽  
Smart Cities ◽  
Open Data ◽  
Quality Of Data ◽  
Data Movement ◽  
Depth Analysis ◽  
Machine Learning Applications

Machine learning (ML) has already gained the attention of the researchers involved in smart city (SC) initiatives, along with other advanced technologies such as IoT, big data, cloud computing, or analytics. In this context, researchers also realized that data can help in making the SC happen but also, the open data movement has encouraged more research works using machine learning. Based on this line of reasoning, the aim of this paper is to conduct a systematic literature review to investigate open data-based machine learning applications in the six different areas of smart cities. The results of this research reveal that: (a) machine learning applications using open data came out in all the SC areas and specific ML techniques are discovered for each area, with deep learning and supervised learning being the first choices. (b) Open data platforms represent the most frequently used source of data. (c) The challenges associated with open data utilization vary from quality of data, to frequency of data collection, to consistency of data, and data format. Overall, the data synopsis as well as the in-depth analysis may be a valuable support and inspiration for the future smart city projects.

scholarly journals Cyber Resilience and Incident Response in Smart Cities: A Systematic Literature Review

Smart Cities ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 894-927
Author(s):  
Gabriela Ahmadi-Assalemi ◽  
Haider Al-Khateeb ◽  
Gregory Epiphaniou ◽  
Carsten Maple
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Smart Cities ◽  
Scientific Evidence ◽  
Simulation Software ◽  
Incident Response ◽  
Natural Ecosystems ◽  
Future Directions ◽  
Smart Healthcare ◽  
The Internet Of Things

The world is experiencing a rapid growth of smart cities accelerated by Industry 4.0, including the Internet of Things (IoT), and enhanced by the application of emerging innovative technologies which in turn create highly fragile and complex cyber–physical–natural ecosystems. This paper systematically identifies peer-reviewed literature and explicitly investigates empirical primary studies that address cyber resilience and digital forensic incident response (DFIR) aspects of cyber–physical systems (CPSs) in smart cities. Our findings show that CPSs addressing cyber resilience and support for modern DFIR are a recent paradigm. Most of the primary studies are focused on a subset of the incident response process, the “detection and analysis” phase whilst attempts to address other parts of the DFIR process remain limited. Further analysis shows that research focused on smart healthcare and smart citizen were addressed only by a small number of primary studies. Additionally, our findings identify a lack of available real CPS-generated datasets limiting the experiments to mostly testbed type environments or in some cases authors relied on simulation software. Therefore, contributing this systematic literature review (SLR), we used a search protocol providing an evidence-based summary of the key themes and main focus domains investigating cyber resilience and DFIR addressed by CPS frameworks and systems. This SLR also provides scientific evidence of the gaps in the literature for possible future directions for research within the CPS cybersecurity realm. In total, 600 papers were surveyed from which 52 primary studies were included and analysed.

scholarly journals Machine Learning Approaches to Bike-Sharing Systems: A Systematic Literature Review

2021 ◽  
Vol 10 (2) ◽  
pp. 62
Author(s):  
Vitória Albuquerque ◽  
Miguel Sales Dias ◽  
Fernando Bacao
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Smart Cities ◽  
Research Question ◽  
Machine Learning Algorithms ◽  
Urban Transport ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Bike Sharing

Cities are moving towards new mobility strategies to tackle smart cities’ challenges such as carbon emission reduction, urban transport multimodality and mitigation of pandemic hazards, emphasising on the implementation of shared modes, such as bike-sharing systems. This paper poses a research question and introduces a corresponding systematic literature review, focusing on machine learning techniques’ contributions applied to bike-sharing systems to improve cities’ mobility. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) method was adopted to identify specific factors that influence bike-sharing systems, resulting in an analysis of 35 papers published between 2015 and 2019, creating an outline for future research. By means of systematic literature review and bibliometric analysis, machine learning algorithms were identified in two groups: classification and prediction.

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