Applications of Big Data and Green IoT-Enabling Technologies for Smart Cities

2022 ◽  
pp. 1090-1109
Author(s):  
Onur Dogan ◽  
Omer Faruk Gurcan
Keyword(s):  
Big Data ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Smart Cities ◽  
Digital Data ◽  
Rapid Urbanization ◽  
Data Resource ◽  
Parallel Data ◽  
Information And Communication

In recent years, enormous amounts of digital data have been generated. In parallel, data collection, storage, and analysis technologies have developed. Recently, there has been an increasing trend of people moving towards urban areas. By 2030 more than 60% of the world's population will live in an urban environment. Urban areas are big data resource because they include millions of citizens, technological devices, and vehicles which generate data continuously. Besides, rapid urbanization brings many challenges, such as environmental pollution, traffic congestion, health problems, energy management, etc. Some policies for countries are required to cope with urbanization problems. One of these policies is to build smart cities. Smart cities integrate information and communication technology and various physical devices connected to the network (the internet of things or IoT) to both improve the quality of government services and citizen welfare. This chapter presents a literature review of big data, smart cities, IoT, green-IoT concepts, using technology and methods, and applications worldwide.

Applications of Big Data and Green IoT-Enabling Technologies for Smart Cities

2019 ◽  
pp. 22-41
Author(s):  
Onur Dogan ◽  
Omer Faruk Gurcan
Keyword(s):  
Big Data ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Smart Cities ◽  
Digital Data ◽  
Rapid Urbanization ◽  
Data Resource ◽  
Parallel Data ◽  
Information And Communication

In recent years, enormous amounts of digital data have been generated. In parallel, data collection, storage, and analysis technologies have developed. Recently, there has been an increasing trend of people moving towards urban areas. By 2030 more than 60% of the world's population will live in an urban environment. Urban areas are big data resource because they include millions of citizens, technological devices, and vehicles which generate data continuously. Besides, rapid urbanization brings many challenges, such as environmental pollution, traffic congestion, health problems, energy management, etc. Some policies for countries are required to cope with urbanization problems. One of these policies is to build smart cities. Smart cities integrate information and communication technology and various physical devices connected to the network (the internet of things or IoT) to both improve the quality of government services and citizen welfare. This chapter presents a literature review of big data, smart cities, IoT, green-IoT concepts, using technology and methods, and applications worldwide.

Big Data IoT Analytics for Smart Cities With Cloud Computing Technique

2021 ◽  
pp. 104-126
Author(s):  
Chellaswamy C. ◽  
Sathiyamoorthi V.
Keyword(s):  
Big Data ◽  
Smart Cities ◽  
Computing Technique ◽  
Strong Connectivity ◽  
The Public ◽  
Environmental Footprints ◽  
5G Network ◽  
Information And Communication ◽  
Data Analytic

Currently, cities are being reconstructed to smart cities that use an information and communication technology (ICT) framework alongside the internet of things (IoT) technology to increase efficiency and also share information with the public, helping to improve the quality of government services citizens' welfare. This large, diverse set of information called big data is obtained by ICT and IoT technologies from smart cities. This information does not have any meaning of its own but a high potential to make use of smart city services. Therefore, the information collected is mined and processed through use of big data analytic techniques. The environmental footprints in smart cities can be monitored and controlled with the help of ICT. Big data analytic techniques help enhance the functionalities of smart cities and the 4G and 5G network provides strong connectivity for professional devices.

Modelling and simulation techniques for transport networks within urban areas applied to logistics and resilience

2021 ◽  
Author(s):  
◽  
Pablo Álvarez
Keyword(s):  
Big Data ◽  
Real Time ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Smart Cities ◽  
Modelling And Simulation ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Simulation Techniques ◽  
New Methodologies

This thesis investigates the use of modelling and simulation techniques in urban areas of smart cities, also exploring how big data can be used to feed these models. These modelling techniques have been applied to two different fields that have been gaining prominence during the last years but where research is still limited: urban logistics and urban resilience. Through this thesis, the author has expanded the research knowledge in these fields by exploring different methods such as meta-heuristics, transport modelling, and agent-based simulation in order to define new methodologies to be applied to urban areas. Regarding logistics, the author has shown through the use of meta-heuristics that when traffic congestion is considered as a dynamic attribute to optimize delivery routes in urban areas, time can be reduced by 11%, which is crucial for logistics companies in a market that is fiercer every day. This is true not only for urban areas, but this research has also demonstrated that optimizing routes with dynamic congestion attributes is also beneficial at a strategic level for routes between cities. To consider congestion costs in real time, a new approach has been developed in which data from Google is downloaded to feed these meta-heuristic models, although other sources of big data could be also used. In this thesis, a methodology is also presented that has been used to model logistics routes in urban areas considering real-time data and with the flexibility to add different network attributes (gradient, traffic bans, CO2, etc.) to simulate different scenarios. This can be useful for logistics companies to optimize their deliveries (choosing between van or tricycles, selecting the time of the day to deliver, etc.) but also for public authorities to get guidance on different transport and urban policies (pedestrianization of some streets, traffic bans, etc.).As for city resilience, the thesis focuses on evacuation planning. A new methodology has been created in which agent-based simulation is used through interconnected sub-models to model a large-scenario evacuation scenario (flooding event as a consequence of a dam collapse). This research defines the data needed to create these models that can be of great help to improve city resilience, and also analyzes how traffic congestion can affect the evacuation procedures. Through the different research articles that compose this thesis, the author brings light to these fields by developing new methodologies and using real case-studies that can help urban planners, companies, and policy makers to create more efficient, sustainable, and resilient smart cities.

scholarly journals Characterization and Efficient Management of Big Data in IoT-Driven Smart City Development

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2430 ◽  
Author(s):  
Alaa Alsaig ◽  
Vangalur Alagar ◽  
Zaki Chammaa ◽  
Nematollaah Shiri
Keyword(s):  
Big Data ◽  
Information And Communication Technologies ◽  
Smart City ◽  
Smart Cities ◽  
Communication Technologies ◽  
City Development ◽  
Efficient Management ◽  
Information And Communication ◽  
Service Oriented

Smart city is an emerging initiative for integrating Information and Communication Technologies (ICT) in effective ways to support development of smart cities with enhanced quality of life for its citizens through safe and secure context-aware services. Major technical challenges to realize smart cities include resource use optimization, service delivery without interruption at all times in all aspects, minimization of costs, and reduction of resource consumption. To address these challenges, new techniques and technologies are required for modeling and processing the big data generated and used through the underlying Internet of Things (IoT). To this end, we propose a data-centric approach to IoT in conceptualizing the “things” from a service-oriented perspective and investigate efficient ways to identify, integrate, and manage big data. The data-centric approach is expected to better support efficient management of data with complexities inherent in IoT-generated big data. Furthermore, it supports efficient and scalable query processing and reasoning techniques required in development of smart city applications. This article redresses the literature and contributes to the foundations of smart cities applications.

Big Data Analytics and Internet of Things for Urban Transportation

2019 ◽  
pp. 244-277
Author(s):  
Jyoti Chandiramani ◽  
Sushma Nayak
Keyword(s):  
Big Data ◽  
Public Transportation ◽  
Traffic Congestion ◽  
Smart City ◽  
Traffic Management ◽  
Smart Cities ◽  
Big Data Analytics ◽  
Considerable Impact ◽  
The Government

The idea of smart city has assumed popularity in numerous countries across the globe. In 2015, the Government of India embarked on a mission of creating 100 smart cities to sustain the burgeoning urban population. While a wide-ranging set of fundamentals has a key role in enhancing the quality of life of citizens, the chapter revolves around transportation issues and traffic management concerns in one of India's smart cities, Pune. Transport is one of the few areas where Pune lags behind compared to its urban counterparts in the country. Public transportation in the city has been ineffectual, and auto rickshaws have been unyielding and pricey, thus making it imperative to possess personal vehicles or resort to app-based cab services. A palpable outcome of this has been traffic congestion that leads to slower travelling speeds, extended trip times, and amplified vehicular queuing. Big data and IoT can make a considerable impact in realizing the smart city objectives for efficient transportation in Pune by serving as complementary measures to supply-side policies.

Spatial Autocorrelation in the Study of Neighbourhoods towards Smart Cities: Empirical Evidence from Kerman, Iran

2012 ◽  
Vol 9 (2) ◽  
pp. 1
Author(s):  
Asra Hosseini
Keyword(s):  
Spatial Autocorrelation ◽  
Urban Areas ◽  
Smart Cities ◽  
Moran’S I ◽  
Moran's I ◽  
Urban Planner ◽  
Universal Feature ◽  
Autocorrelation Method ◽  
Selection Of

From earliest cities to the present, spatial division into residential zones and neighbourhoods is the universal feature of urban areas. This study explored issue of measuring neighbourhoods through spatial autocorrelation method based on Moran's I index in respect of achieving to best neighbourhoods' model for forming cities smarter. The research carried out by selection of 35 neighbourhoods only within central part of traditional city of Kerman in Iran. The results illustrate, 75% of neighbourhoods' area in the inner city of Kerman had clustered pattern, and it shows reduction in Moran's index is associated with disproportional distribution of density and increasing in Moran's I and Z-score have monotonic relation with more dense areas and clustered pattern. It may be more efficient for urban planner to focus on spatial autocorrelation to foster neighbourhood cohesion rather than emphasis on suburban area. It is recommended characteristics of historic neighbourhoods can be successfully linked to redevelopment plans toward making city smarter, and also people's quality of life can be related to the way that neighbourhoods' patterns are defined. 

scholarly journals iBikeSafe: A Multi-Parameter System for Monitoring, Evaluation and Visualization of Cycling Paths in Smart Cities Targeted at Cycling Adverse Conditions

Smart Cities ◽  
2021 ◽  
Vol 4 (3) ◽  
pp. 1058-1086
Author(s):  
Franklin Oliveira ◽  
Daniel G. Costa ◽  
Luciana Lima ◽  
Ivanovitch Silva
Keyword(s):  
Urban Areas ◽  
Smart Cities ◽  
Large City ◽  
Health And Safety ◽  
Parameter System ◽  
Urban Centers ◽  
Adverse Conditions ◽  
Last Mile ◽  
Real Scenario

The fast transformation of the urban centers, pushed by the impacts of climatic changes and the dramatic events of the COVID-19 Pandemic, will profoundly influence our daily mobility. This resulted scenario is expected to favor adopting cleaner and flexible modal solutions centered on bicycles and scooters, especially as last-mile options. However, as the use of bicycles has rapidly increased, cyclists have been subject to adverse conditions that may affect their health and safety when cycling in urban areas. Therefore, whereas cities should implement mechanisms to monitor and evaluate adverse conditions in cycling paths, cyclists should have some effective mechanism to visualize the indirect quality of cycling paths, eventually supporting choosing more appropriate routes. Therefore, this article proposes a comprehensive multi-parameter system based on multiple independent subsystems, covering all phases of data collecting, formatting, transmission, and processing related to the monitoring, evaluating, and visualizing the quality of cycling paths in the perspective of adverse conditions that affect cyclist. The formal interactions of all modules are carefully described, as well as implementation and deployment details. Additionally, a case study is considered for a large city in Brazil, demonstrating how the proposed system can be adopted in a real scenario.

scholarly journals Sustainable governance in smart cities and use of supervised learning based opinion mining

2021 ◽  
Vol 11 (1) ◽  
pp. 498
Author(s):  
Hena Iqbal ◽  
Sujni Paul ◽  
Khaliquzzaman Khan
Keyword(s):  
Big Data ◽  
Policy Evaluation ◽  
Opinion Mining ◽  
Smart Cities ◽  
Life Quality ◽  
Future Prospects ◽  
Indian Government ◽  
Data Set ◽  
Governance Model

Evaluation is an analytical and organized process to figure out the present positive influences, favourable future prospects, existing shortcomings and ulterior complexities of any plan, program, practice or a policy. Evaluation of policy is an essential and vital process required to measure the performance or progression of the scheme. The main purpose of policy evaluation is to empower various stakeholders and enhance their socio-economic environment. A large number of policies or schemes in different areas are launched by government in view of citizen welfare. Although, the governmental policies intend to better shape up the life quality of people but may also impact their every day’s life. A latest governmental scheme Saubhagya launched by Indian government in 2017 has been selected for evaluation by applying opinion mining techniques. The data set of public opinion associated with this scheme has been captured by Twitter. The primary intent is to offer opinion mining as a smart city technology that harness the user-generated big data and analyse it to offer a sustainable governance model.

scholarly journals Analyzing the Issue of On-street Parking in Commercial Areas: A Case Study of the City of Hyderabad, Pakistan

2018 ◽  
Vol 01 (02) ◽  
pp. 01-09
Author(s):  
Baig Farrukh ◽  
Sahito Noman ◽  
Bano Arsla ◽  
Keyword(s):  
Traffic Congestion ◽  
Urban Areas ◽  
Survey Methodology ◽  
Greenhouse Gas Emission ◽  
Central Business District ◽  
Rapid Urbanization ◽  
Analysis Technique ◽  
Business District ◽  
Public Views ◽  
The City

In developing countries, rapid urbanization has created an enormous pressure on land use, infrastructure and transportation. The fast growing ratio of motorized vehicles in urban areas is the main cause of environmental degradation. Almost 80% of the greenhouse gas emission is from vehicles in cities. In the city centers, on-street parking is considered the major cause of traffic congestion. The aim of this study was to evaluate the problems of on-street parking and disorderly parking at Central Business District (CBD) of Hyderabad city. The field survey methodology was adopted to perceive the current traffic problems in the city center and traffic count survey was carried out in both peak and off hours. The data was analyzed using descriptive statistics frequency analysis technique with the help of Statistical Package for the Social Sciences (SPSS). The findings revealed that increasing number of vehicles, on-street parking, improper parking, encroachment, inadequate parking space and poor condition of roads are the main causes of traffic congestion. The study bridges up the research gap of determining public views about on-street parking challenges in the context of Hyderabad, Pakistan and provides statistical results which may equally be adapted by policy makers and transportation planners in order to improve the traffic situation.

On Internet of Things and Big Data in University Courses

2019 ◽  
pp. 1393-1406
Author(s):  
Dmitry Namiot ◽  
Manfred Sneps-Sneppe
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Information And Communication Technologies ◽  
Smart Cities ◽  
Communication Technologies ◽  
Machine To Machine ◽  
University Courses ◽  
Information And Communication ◽  
The University ◽  
Educational Course

In this paper, the authors discuss Internet of Things educational programs for universities. The authors' final goal is to provide a structure for a new educational course for Internet of Things and related areas such as Machine to Machine communications and Smart Cities. The Internet of Things skills are in high demands nowadays and, of course, Internet of Things models, as well as appropriate Big Data proceedings elements should have a place in the university courses. The purpose of the proposed educational course is to cover information and communication technologies used in Internet of Things systems and related areas, such as Smart Cities. The educational course proposed in this paper aims to introduce students to modern information and communication technologies and create the formation of competencies needed for such areas as Machine to Machine communications, Internet of Things, and Smart Cities. Also, the authors discuss Big Data issues for IoT course and explain the importance of data engineering.

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