SMART AND SUSTAINABLE CITY: SINGAPORE, SUCCESS CASE

Solidary Engineering

SMART AND SUSTAINABLE CITY: SINGAPORE, SUCCESS CASE

JULIÁN ANDRÉS CORREA GIRALDO.1, ANDRÉS ESCOBAR DIAZ.2, HAROLD.

VACCA GONZÁLEZ.3

1 Electronic Technologist, Francisco Jose de Caldas District University (UDFJ de C),

Colombia.

ORCID: https://orcid.org/0000-0002-6074-7283

e-mail: [email protected]

2 Electronic engineer, Francisco Jose de Caldas District University (UDFJ de C), Colombia.

Master's degree in engineering, Master's degree in administration, Los Andes University,

Colombia.

Professor at the Francisco Jose de Caldas District University (UDFJ de C), Colombia.

Order and Chaos Research Group Director: ORCA, attached to the Center for Research

and Scientific Development (CIDC) of the Francisco Jose de Caldas District University

(UDFJ de C), Colombia.

Technological Development Center Da Vinci


e-mail: [email protected].

Contact address: Tv. 70 b # 73 a 34 S. Technological faculty of Francisco Jose de Caldas

District University (UDFJ de C), Colombia.

3 Bachelor in Mathematics, Francisco José de Caldas District University (UDFJ de C),

Colombia.

Master in Applied Mathematics, EAFIT University, Colombia. Professor at the Francisco

José de Caldas District University (UDFJ de C), Colombia. Research group Director in

Basic Sciences: SciBas, attached to the Center for Research and Scientific Development

(CIDC) from the UDFJ de C.


e-mail: [email protected]

mailto:[email protected]


Resumen

Introducción: El artículo hace una descripción cualitativa de la ciudad-nación de Singapur

y su evaluación cuantitativa, las cuales hacen parte de una investigación de carácter

exploratorio para el grupo de investigación ORCA y SciBas de la Universidad Distrital

Francisco José de Caldas. Por lo anterior, durante el primer semestre de 2018 se realizó una

revisión conceptual sobre el tema, planteando una metodología cuyas fuentes se seleccionan

con la categoría de ciudad inteligente en el contexto de sostenibilidad. La búsqueda de

información se centró en las bases de datos: IEEE Xplore, ScienceDirect, Springer Link,

entre otras; y en la búsqueda del modelo de evaluación.

Problema: Ausencia de literatura y conceptualización para los temas relacionados con

ciudades inteligentes.

Objetivo: Comprender el tema de ciudades inteligentes como casos de éxito.

Metodología: Interpretación de información concerniente al tema de ciudades inteligentes.

Resultados: Establecer el desarrollo de la ciudad de Singapur en temas como movilidad

inteligente y gestión eficiente de energía, entre otros.

Discusión de resultados: Se presentan los aspectos positivos logrados por Singapur para

destacarse como una ciudad inteligente.

Conclusiones: Muestra los diversos proyectos implementados que ayudan a resolver los

problemas que se presentan en la ciudad.

Originalidad: Dentro de la literatura académica es escasa la referencia a ciudades

inteligentes utilizando metodologías para establecer categorías de análisis documental y

modelos para determinar el éxito de las mismas.

Limitaciones: Como las metodologías asumen una alta cantidad de variables en la categoría

de inteligencia urbana, es posible extender el estudio a indicadores como Capital Intelectual

y Calidad de Vida.


Abstract

Introduction: The paper focuses on the qualitative description of the nation city of Singapore

and its quantitative evaluation, which are part of an exploratory research for the ORCA and

SciBas research group of the Francisco José de Caldas District University. Therefore, during

the first semester of 2018, a conceptual review was made on the subject, proposing an

methodology whose snuyources are selected with the category of smart city in the context of

sustainability.

The search for information was focused on the databases: IEEE Xplore, ScienceDirect,

Springer Link, among others; and in the search for the evaluation model.

Problem: Absence of literature and conceptualization for topics related to smart cities.

Objective: Comprehend the smart cities topics as success cases.

Methodology: Interpretation of information concerning to smart cities.

Results: Establish the development of the city of Singapore in topics such as smart mobility

and efficient energy management, among other.

Discussion: Presents the positive aspects that Singapore has achieved to innovate and stand

out as a smart city.

Conclusions: Shows different implemented projects that allow that help to solve the

problems that arise in the city.

Originality: Inside of academic literature there is little reference to smart cities using

methodologies to establish categories of models and documentary analysis to determine the

success of them.

Limitations: As the methodologies assume a high quantity of variables in the category of

urban intelligence, in perspective the study can be extended to other indicators such as

Intellectual Capital and Life Quality.

Keywords: Smart City, Automatic learning, Cloud computing, Machine to machine

communication, E-Government, Internet of things.


1. Introduction

A smart city is an urban environment aimed to improve the life quality of the citizens that

inhabit it, using technology to achieve social and economic development, and where multiple

sectors systematically cooperate to achieve sustainable results [1],[2]. This article is focused

on the qualitative description of the nation-city of Singapore and is part of an investigation

to conceptualize and understand the smart cities topic, as well as the evaluation of it.

In accordance with the above, Singapore in 2014 established a plan aimed to transform

the existing nation-city in a highly developed place, taking into account the citizen as the

main axis of all changes to be made. The smart nation plan, see Figure 1, was focused on the

management and proper handling of urban growth and energy sustainability. The focus of

the vision of Singapore was developed in areas such as transport, security, energy,

construction, education, health, among others [3], [4].

This is why, thanks to the fact that this city has found the means to take advantage of

the challenges and turn them into economic opportunities due to the intelligent use of

information and communication technologies (ICT), the experience has been successfully

replicated by implementing some of these solutions in other cities, allowing Singapore to

progressively become a worldwide level smart city [5].

Consequently, the article focus is aimed at showing the nation-city of Singapore as a

success case because of its efforts in the implementation of ideas and projects that have

allowed it to stand out as one of the smart cities established in the world. The categories that

Singapore has focused on in its smart nation plan are the following:

Smart Mobility

Systematically understanding the city, coordinating efficiently to facilitate the live of

the people and their daily commuting

Energy efficiency

Correct and properly use of energy as one of the fundamental aspects for measuring

sustainability in the city.

Government and research policies


Facilitating the city administration by simplifying the procedures of citizens and the

proper management of government entities, allowing faster solutions to everyday and

recurrent problems that occur in urban environments.

ICT (Information and Communication Technologies)

Controlling the data flow for further processing and analysis, managing the

information to ensure the knowing everything that happens in the city reality at any

time of the day. Here are related aspects such as the Internet of Things (IoT) and big

data (Big Data), necessary tools that offer connectivity to all the systems that are

managed in Singapore.

2. Methodology

The smart city concept has been strongly established in the last decade of this century

because it has radically changed the way people live in modern cities. That is why, in this

period of time, through a comprehensive bibliographic review, the article aims to illustrate,

SMART NATION VISION

Urban Mobility Environment District

Management Healthcare

Logistics Manufacturing Energy &

Sustainability Retail &

Advertising

Smart Nation Platform

Smart Nation Operating System (SN-OS)

Communications & Sensor Network

Supporting Ecosystem

Build Industry

Develop IP

Build Manpower

Figure 1. Singapore's smart nation vision, [3].


understand and conceptualize why Singapore is considered a success case and a real example

of implementation of this type of city.

Given the current status of the subject, and at the same time poor academic literature, the

methodology is covered in a documentary research modality [6] from which a contextualized

conceptual review (heuristic) on smart cities is obtained from the establishment of five

categories with their respective subcategories: Smart Mobility (Mobility integration,

Sustainable public transport service, Electric cars, Autonomous vehicles, Transport and

citizens); Energy efficiency (Renewable Energy, Natural Resources Management and

measurement of environmental parameters); Government and Innovation Policies (E-

Government, Directive Intervention, Facilitating Intervention); Smart Health (Telehealth

and solutions provided by ICT); and the use of ICT for the generation, transmission and

information processing (Machine learning, Internet of Things (IoT), sensor networks, Big

Data) that occurs daily in Singapore. Afterwards, the stage of interpretation (hermeneutics)

of the sources on the Instrumentation for Smart Cities is carried out. Figure 2 shows the

methodological model followed, validated by the Orca and SciBas research groups.

Finally, after consulting information in different databases such as: IEEE Xplore,

ScienceDirect, Springer Link -among others-, they are classified and interpreted to evaluate

the city of Singapore as a success case using the GMSDIV model (Governance, Mobility,

Sustainability, Economic Development, Intellectual Capital, Life Quality) given in [7]. As a

result of the research, in addition, an Integrated and Complementary Systems Network (ICSN)

is obtained, showing the existing relationships between the different systems and elements

that Singapore has incorporated to become a smart city. From this point of view, it is the

integration and complement of systems what determines success as a Smart City case.


Figure 2. Scheme that describes the documentary research methodology followed for

contextualized conceptual revision on smart cities.

Source: Authors

3. Results

Singapore is a State City in which the government has sought to improve the city through

the implementation of strategic plans; in 2014, for example, they adopted a nation plan that

included services in different areas such as mobility, health and safety. In a first step, Figure

3 shows the level of increase in the population of Singapore from 1960 to 2017. This high

rate of population increase meant taking relevant actions to allow citizens to access an

adequate life quality, making Singapore a city that adapted to both environmental, social,

economic and political changes.

H

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•Preparatory: Problem that determines social analysis ex-ante study (Relevance, Feasibility, Coherence, Feasibility): delimitation in time and space language: steps: stages

•Descriptive: documentation: keywords: search descriptors: scenarios (IEEE Xplore, ScienceDirect, Springer Link, Internet, books, thesis, ...)

•Interpretative: hypothesis: horizon extension of the study

•Constructive: trends: achievements: gaps: difficulties

Categorization:

1. Internal: MATRIX documentation under the

focus of thematic, methodology, findings, theories, prospective or

retrospective studies.

2. External: list of topics to establish a socio-cultural contribution of research

Construction:

1. Selection

2. Collection

3. Interpretation

4. Theoretical Construction

5. Drafting

Contextualization:

1. Problem statement:

2. Limits

3. Documentary material

4. Contextualization Criteria

Clasification:

1. Parameters for the systematization of

information

2. Class of documents studied: objectives,

chronology, disciplines that frame the work, the lines of

research, the level of conclusions and the scope


Similarly, the rate per capita, as shown in Figure 4, showed a significant increase from

1985 to 1997 due to innovation policies that the city incorporated; measures that have made

it possible to show over the years how the proper planning of projects executed with public-

private partnerships allowed improving the economy of the city.

3.1. Smart Mobility

The city is characterized by an intelligent transport system (ITS), developed since 2006,

which establishes a massive rapid transit system (MRT) to provide national interconnectivity

[9]. They have a monitoring system (one monitoring) to access transit information that is

collected by a network of cameras installed in the streets and by taxis with GPS. Likewise,

with the application developed by the LTA1 they monitor incidents on the roads and with an

1 Land transport authority by its acronym in English (LTA)

Figure 3. Increase in the population of Singapore from 1960 to 2017 [8]

1960 2010 2000 1990 1980 1970

5,5

5,0

4,5

4,0

3,5

1,5

2,0

2,5

3,0


smart electronic device (Your speed sign) alert drivers of the speed limit [3],[10],[11].

Electronic payment to access the transport system generates benefits that are evident in

reducing the time to access the different transport means, ensuring comfort to users, [12].

- Mobility integration

One option to integrate different transport means is through shared service that allows

different users to use the same vehicle to move to different parts of the city. Mobility concepts

such as MaaS2 (Mobility as a service) help to buy mobility services which are packages based

on the needs of consumers instead of buying them on transport means. Another function

offered by this service is the ticket and payment integration through the use of a smart card

that can access different transport means, making charge from the use of each service used.

The EZ-link3 card is used in Singapore with high acceptance by users because it eliminated

payment barriers between operators in the transit network [14],[15],[16].

2 MaaS: Mobility as a service 3 Singapore transportation pass

1965 1970 1975 1980 1985 1990

Figure 4. Singapore city per capita index from 1960 to 2017 [13].

1995 2000 2005 2010

5 2015

0

10

20

30

40

50

60

Mil


- Sustainable public transport service

The construction of a city must take into account the social and environmental

perspective focused on sustainability; looking for this, public transport must provide the

tools to achieve a dynamic and transformative development for urban regions [17].

Finding new innovative forms of transport is part and differentiates an smart city from a

traditional city [18],[19]. It is important to emphasize that socio-emotional factors have

a significant influence on the people decisions to access or use some transport means and,

taking these factors into account, it is necessary to understand and consider the different

transport means that facilitate the users flow [20],[21].

- Electric vehicles (EV)

A study in Singapore indicates that the widespread use of this transport means reduces

the air pollution levels, compared to traditional combustion vehicles; in turn, EVs offer

greater energy efficiency and lower energy costs [22],[23].

Electric car charging stations are controlled and monitored by an electric network

management system to establish how much is available for charging the electric car,

avoiding congestion at the stations and facilitating the collection to the vehicle owner

[24],[25],[26].

- Autonomous Vehicles

Autonomous vehicles will be part of the cities in the near future, so knowing the

benefits they offer and the way they can adapt to the existing mobility ecosystem is an

obligatory study to obtain the best results in its implementation. In Singapore, through

the SimMobility application (integrated model of supply and demand based on agents)

the shared autonomous vehicle could anticipate the demand in the mobility service to

reduce waiting times and ensure a balance between the vehicles required and vehicles

available in each area [27].

- Transport and citizens

Adapting the transport means to the needs of all citizens is a task that must be carried

out by government entities and the sectors involved, if are wanted the best services to be


provided to the city population. In [28] is presented, for example, the study of the activity

patterns performed by people over 50 (trend age) to know in detail the use of the time in

order to optimize the performance of activities and thus offer the transportation methods

that this age level of society requires.

3.2. Energy efficiency

On energy efficiency subject, Singapore implemented an power grid with smart meters

to know the electricity consumption and the electricity contribution to buildings by vehicles

(V2I)4 [24], to subsequently monitor and achieve cost reduction. The electricity levels of

consumption can be affected if this electric flow is not adequately controlled and that is why

customers are indispensable to make this supply control effective in Singapore buildings.

[29],[30], [31]. Defining the appropriate norms or laws that regulate these micro-grids is

important to standardize and facilitate their implementation, [32].

Figure 5 shows an smart grid system where all the necessary infrastructure is established to

obtain improvements in energy efficiency subject where buildings, vehicles and renewable

energy systems are integrated to know the state of energy consumption supported mainly by

smart meters equipped with communication capabilities that provide enough information for

city officials to make decisions regarding continuous work with citizens to reduce the energy

waste that promotes a sustainable urban environment.

It was possible to improve, then, the energetic efficiency in controlled enviroments in

which, looking for comfort for the building residents, smart systems like the one of

illumination with LED bulbs were designed and implemented. This type of system was

remotely controlled to set lighting parameters with respect to natural light or the number of

occupants of these buildings [33],[34].

4 V2I: Vehicle to infrastructure. One of the applications of this technology is to supply electricity in periods of high demand to buildings by electric vehicles through a smart grid.


On the other hand, smart homes were another option to reduce energy consumption

levels, a measure that helped regulate and control global warming; however, it was important

for the citizen to adopt measures to reduce these energy consumption rates, aided by smart

systems implemented in homes. For example, with the use of autonomous smart systems and

sensors (Motion detection sensors, temperature, humidity, light) it was possible to control

the appliances that will be used or the light sources of each of the spaces in the house

[35],[36]. However, it was necessary to take into account the security of this kind of homes

due to the amount of data handled, which can be easily accessed through the Internet if they

do not contemplate and anticipate the necessary privacy challenges [37],[38].

- Renewable energy

The commissioning of solar panels as a source of renewable energy were managed

through an smart grid [39],[40]. Figure 6 presents the general scheme of a micro-grid, where

the means of alternative energy production are linked (solar panels, wind turbines, energy

storage facilities, among others); the power lines for the energy transmission, together with

Intelligent electric vehicles

can be recharged from green

sources or when energy is

cheapest; supporting

cost-effective transport, with

zero-tail-pipe emissions

Intelligent energy storage

store electricity generated at

off-peak for later use.

Intelligent Communications use Wireless technologies and powerlines for “real-time” interactions

Smart meters relay energy usage

and pricing informations between

consumers and electricity providers Intelligent homes empower households with home

Automation systems centrally or remotely to control

energy consumption. Web portals and in-home displays

allow consumers to proactively monitor their enrgy

consumption

Demand response

management system in homes,

office buildings and industries

enable users to monitor and

optimize their energy use.

Intelligent offices

adjust cooling and

lighting depending

on real-time costs

and needs.

Intelligent generation can

Reduce overall demand on the

power grid by making use of

intermittent energy sources, e.g,

solar panels, to support the local

grid with green energy

Electricity

Data

Industry

Solar Panels

Figure 5. Smart energy system (IES) [3]


a controller that distributes the necessary electricity to the residences, balances the electric

consumption allowing an interconnectivity without interruptions to increase the efficiency

and the reliability of the network. Therefore, obtaining the highest efficiency in photovoltaic

systems allowed to reduce the demand of energy from the conventional electricity grid, which

contributed to reduce the pollution indexes. The solar panel orientation and the appropriate

inclination angle, on the other hand, guaranteed its greater efficiency and, consequently, the

greater use of this energy to meet the purposes of the smart city [41],[42].

In search of reducing the abundance of solid waste and establishing an green energy

impulse, was found as an isolated experience, an idea that started up and gave good results

on a poultry farm in Singapore, where a anaerobic biodigester system was implemented with

the purpose of producing biogas from the manure of its birds which allowed it to generate

electrical energy, guaranteeing the self-sufficiency and sustainability of the farm. The excess

of electrical energy that is not consumed, is sold to the city's electricity grid [43],[44].

- Natural resources management and measurement of environmental parameters

Water management is important to meet the daily demand of this vital liquid [45].

Therefore, this city inaugurated the largest seawater desalination plant in Asia to supply 25%

of the population with the implementation of innovative technologies based on ultrafiltration

membranes. The water monitoring quality is verified by a network of sensors to ensure that

the liquid is in the best conditions to be consumed [46]. On the other hand, the city has

proposed to reduce the concentration indexes of fine particles in the air, the level of sulfur

dioxide and the measurement of parameters such as noise, humidity and temperature [47].

Figure 6 shows different activities that can be controlled through the proper data management

produced in the city.

3.3. Government and Innovation Policies

The government plays a decisive role in the decision making of a city and that is why it

is one of the factors that can greatly boost innovation activities to help promote the production

of knowledge that establishes the bases for the construction of new projects that benefit the

community from the social, economic, cultural and political aspects [48]. The key to create

innovation policies in Singapore was based on establishing the adequate means so this


innovation could produce economic benefits and thus could support the academic and

scientific scope, contributing to gradually improve the city, see Figure 8.

Figure 6. Typical scheme of a micro-grid [40].

Communications Line

Microgrid

Controller

Divisional

Switch Power-Receiving

Equipment

PV

Power

Storage

Facility

Power Storage Facility Wind Biomass Fuel Cell

Collective

Housing

Residences Public /

Commercial

Facility

Power Line Hot Water Pipe

Figure 7. Singapore in real time [12].

Isochronic Taxi Movements Temperature

Events Conversation Airports


Policies have been presented thanks to:

1. In 2006, a research, innovation and company council was launched to advice on I+D

strategies and policies in Singapore.

2. In 2008, the National Research Foundation (NRF), founded to encourage greater

innovation, launched the national research, innovation and business framework for

technology commercialization.

3. The 2010 science and technology plan aimed to strengthen the I+D base with a budget

of US $ 9 billion; and this commitment in I+ D was reinforced by another five year

plan RIE 2015 with US $ 12.4 billion.

4. In Singapore, 61% of I+D was carried out by the manufacturing industry, followed

by universities, research institutes and government institutions (29%).

5. These aspects of the innovation policy can be consulted in [49] and complemented

with [50].

- E-Government

From this perspective, Singapore has contemplated in its smart nation plan the integration

of different government entities to create an environment of mutual cooperation where the

city can be managed efficiently. The established government has seen the need to develop an

integrated data exchange platform to take advantage of the existing smart sensor network and

thus provide accurate data on the city operation where the information collected is processed

in the best way to enable the efficiency in the city administration with respect to the systems

that it has to control. In 2013, SCDF (governmental agency) responded to more than 4,000

fires, which represented a decrease of 7.8% of the cases in 2012. The year 2013 registered

the lowest annual figure in 20 years. Similarly, the benefits of surveillance cameras can be

measured by the number of cases of crimes that has helped solve and, ultimately, to reduce

the rate of urban crime. Several newspaper articles from Singapore revealed that the cameras

installed in the blocks of Housing and Development Boards since 2012 have helped solve

more than 430 cases and provided crucial information for 890 cases of criminal investigation

[3].


- Directive intervention

Aimed to achieve predetermined results by making changes in investment and production

patterns in selected industries. A clear example was that by promoting the high-tech

economy, the government provided funds for research and development, established

public research facilities and helped transfer the result to private sectors, [49].

- Facilitative intervention

The objective was to create positive environments for private companies by providing

public goods such as infrastructure and education. The facilitative government tried to

promote innovation by building institutions in order to promote a healthy culture and

targeting policies to overcome private investment obstacles instead of directly

influencing innovative behavior through highly interventionist measures, [49].

3.4. Smart health

The population birth rates in the city were reduced over the years, and it is for this reason

(a great challenge) that in order to guarantee timely attention to the public, mobile

Figure 8. Singapore and Hong Kong innovation initiatives, [49].

Singapore

Science

Park

National

Science and

Technology

Board

National

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$1.2 Billion

National

S&T Plan $2.8 Billion

S&T Plan

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applications have been developed to prevent chronic diseases, planning telehealth5 tools and

ICT solutions (Corresponds to major political initiatives at both national and autonomous

levels, linking different government entities with the health sector [51]) to improve the

logistics of patient care in hospitals [5].

3.5. ICT

Computer and mobile applications have revolutionized the teaching methodology in the

classroom, allowing to build a collaborative knowledge about contextual mobile learning. It

has been possible to create applications that link the student and help him establish complex

relationships with his environment to facilitate the resolution of initial conflicts and failures,

as well as the way in which he must develop in his environment, allowing him to learn in a

new form, the way in which he must take decisions to achieve the proposed goals [52].

- Machine learning

The data produced in a smart city can be used for different actions and approaches, the

high level of processing that can be achieved with this data can provide key information that

helps to provide welfare to citizens. A data application is presented in a study to find the

thermal comfort that a person feels at their work place or home, which is reflected in a daily

use / energy consumption to maintain this comfort [53],[54]. The data based prediction is

collected from 4 public city residential buildings to find an optimal balance between energy

use and thermal comfort, helping to reduce energy consumption in buildings due to the high

energy consumption presented by the frequent use of ventilation systems [55],[56],[57],[58].

The other approach that can be given to the data is related to the study of faults in the city

railway system to determine the system reliability and to decide the maintenance cycle based

on the average estimation time until the system failure [59].

- Big Data

The problems in a city can be solved in different ways; among these is the

implementation of information technologies that capture and transfer information

5 It describes a wide range of diagnosis and management, education and other related fields of medical care that is provided through technology. Includes: Dentistry, counseling, physical and occupational therapy, home health, among others.


between different places [60],[61]. Performing fast information processing acquired from

all kinds of sensors implemented in the city has required, in some cases, data simulation

to know the trend towards a specific result type and then make the most accurate decision

according to the needs [62]. Based on this, Big Data has been a tool that processes sensor

data located in strategic locations [63],[64]; and the use of this data has been oriented to

the solution and facilitation in the waste collection and treatment to improve the quality

of the environment [65]; through the creation of databases that store the places where this

waste is generated, and with the ecological grids implementation, viable commercial

industrial symbioses have been established to obtain a benefit from the waste collected

[66],[67].

On the other hand, Big Data has contributed with the transport system analysis,

modeling and planning [68],[69]. From this perspective, it is necessary to start from a

reality that focuses on being able to use the data to guarantee adequate transport routes

planning, understanding that there are inherent limitations to obtain the best results in

terms of improving mobility and it is only with the formulation of policies and the taking

of right decisions that can guarantee a better information understanding to be processed

in order to achieve optimal results that adjust to the citizen needs to mobilize in urban

areas [70].

In another sense, it is crucial to bear in mind that an adequate data protection

generated in a smart city must be considered in order to protect vital information about

citizens according to the procedures that are carried out through the Internet. [71],[72].

Protecting this data guarantees the formation or strengthening of the relationship between

citizens and the local government and allows attracting investment from private

companies, foreign investors and greatly improving the provision of basic and

complementary services required by citizens.

- Sensor networks

The sensors used in the city transmit information based on high-speed wireless networks

where cameras, GPS devices and other sensors are implemented in different places, as well

as in taxis, to help manage traffic and improve the prediction of future congestions helping

to the calculation of the most optimal routes to achieve an efficient vehicular flow [73]. The


citizen’s use of technological devices (smartphones, tablets) means that they become the best

sensors in the city (Participatory Detection Platform or PS) and, thanks to the fast processing

of these devices, it is possible to offer information in real time about certain events that occur

on a day to day basis. One of the applications of this platform is the development of a

Transport Travel Measurement System (TTMS) that provides data anywhere and large scale

coverage to demonstrate the paradigm of PS in the evaluation of the travel quality [74],[75].

- Internet of things

The IoT, which consists of a network of sensors located at strategic points in the city, has

allowed an adequate information flow that let know in real time the state of a particular area

[76],[77]. The sensor interconnection and the data transfer they produce to the cloud is a

challenge presented by smart cities [78],[79],[80]. The SENSg6 sensor nodes were used in

Singapore in 2015 to conduct large-scale experiments on environmental factors and to allow

the collection of daily activity data of up to 50,000 students. The project objective was to

find a solution to the large scale of environmental sensors urban deployment that, helped with

the design of the integrated algorithm, reduced the data amount that must be moved and

processed in the central servers. The implemented solution solved the problems in the

environmental sensors urban deployment and with the integrated algorithms design, they

demonstrated an important advance in the capacity to implement machine learning code [81].

Therefore, the IoT has a large field of action in the activities that are carried out in a city;

among these is to help reduce the level of energy consumption in industrial areas or factories

[82],[83]. In the study carried out by [84] a software application for energy efficiency real

time monitoring in production plants is presented, finding valuable benefits such as the

abnormal occurrences real time monitoring and the help it provides to the energy managers

to integrate best practices into day to day operations eliminating possible waste of energy in

operations.

In another sense, the hydroponic vegetables cultivation in Singapore is a measure used to

supply the food demand in the city, which through the use of sensors, actuators and

microprocessors is used to control water quality and luminosity of these crops, proving to be

6 Custom sensors with a hybrid cloud infrastructure.


a self sustaining and low cost source that, if implemented, can significantly reduce labor and

operational costs, while increasing livestock production and profitability, contributing to the

sustainability and habitability of the city [85].

4. Discussion

The future vision with the approach of strategies to seek and achieve the objectives

proposed in a city, establishes the bases to develop initiatives that promote innovation and

citizen participation for the projects implementation that, financed and supported by

government and private entities, achieve continuous consolidation for the construction of an

efficient and optimal city for the lives of citizens; This has been the case in Singapore. Here

began to implement projects since the 70s of the last century to advance urban infrastructure

works which has allowed it to be listed today as one of the best cities to live.

Likewise, citizen participation was fundamental to achieve the relevant changes in the

city, because were and are its inhabitants who interact continuously with smart systems that

are implemented and it is precisely because of this interaction that an information flow is

created allowing its timely management, supervising, controlling and acting at all times to

achieve a single objective to identify the improvement of the people life quality, among many

other factors.

The urban spaces that are taken as reference of smart cities have strengthened their

competitive advantages supported by factors such as social innovation and a strong research

capacity to find solutions that allow them to overcome the obstacles of the city, finding a

sustainability approach for the city development of a social, economic and political

environment.

In an smart city it is essential to use efficient communication technologies with a large

scale network architecture deployed for the internet of things, accompanied by adequate

transmission techniques and means to avoid connectivity problems, guaranteeing a good

information reception that, with the use of machine learning, improve the management and

the amount of information that is processed in the central servers.

The mobility system is one of the main indicators of development in a city. The services

for this integration go from tickets, payments and the use of ICT to facilitate the use of the


service. Encouraging and developing this kind of public transport solutions brings an

important benefit, such as the increase in the traveler’s level using the system, which

significantly reduces the use of private vehicles as an alternative to transportation. Realize a

total or partial integration of this system is a challenge that must be faced in order to advance

in terms of social and economic development.

As an evaluation proposal, or determination of success case, Singapore was considered in

this document as an smart city because it has managed different city systems to continuously

improve infrastructure, environment, social and economic aspects in order to offer good life

quality to its inhabitants. However, it is necessary to establish a concrete evaluation model

for Singapore, given in [7], which indicates a quantitative assessment in terms of GMSDIL

(Governance, Mobility, Sustainability, Economic Development, Intellectual Capital, Life

Quality) that is used to classify cities as smart. The classification is presented as follows:

Table 1 describes the axis and corresponding factors that are taken into account to

evaluate a city as a smart city. Each factor is evaluated on a scale of 0 to 4 and the factors

of each axis are evaluated as follows:

G = (g1+g2+g3+g4) / 4 (1)

M = (m1+m2+m3+m4) / 4 (2)

S = (s1+s2+s3) / 3 (3)

D = (d1+d2) / 2 (4)

I = (i1+i2) / 2 (5)

L = (l1+l2+l3) /3 (6)

SMART CITY AXIS FACTOR

G e- Government and e- Gobernance

Electronic Headquarters (g1)

Transparency (g2)

Interactive Street (g3)

Citizen Communication (g4)

M Mobility

Sustainable Urban Mobility Plans (m1)

Public Transport Multimodal Integration (m2)

Deployment of Alternative Means (Bicycle) (m3)

ICT in Traffic Control (m4)


S Environmental sustainability

Energy Efficiency (s1)

Water Consumption Efficiency (s2)

Emissions (s3)

D Economic Development Open Data (d1)

Innovation Ecosystem (d2)

I Intellectual Capital Wifi as a municipal service (i1)

Training (i2)

L Life Quality

Health and Sanitation (l1)

Universal Accessibility (l2)

Deployment of various ICT measures for the enjoyment of the city

(l3)

Table 1. The axes of the smart city and the evaluation factors used [7]

Finally, (7) is applied to obtain the corresponding value to determine the classification of

the city as follows:

5. Cities rated as Smart City: Rated above 2

6. Conventional cities: Rated with 2

7. Cities below the average: Rated below 2

SC = (G + M +S + D + I + L) / 6 (7)

Figure 9 indicates the values of each factor for each axis, where is applied (7) and a

score of 2.46 is obtained, which clearly indicates that Singapore is valued as an smart city.

8. Conclusions

G M S D I V

g1 3 m1 4 s1 4 d1 2 i1 1 v1 3

g2 3 m2 4 s2 4 d2 4 i2 2 v2 1

g3 1 m3 1 s3 2 v3 1

g4 2 m4 3

2,25 3 3,33 3 1,5 1,666

SC 2,458333

Figure 9. Values applied to (7) indicating the qualification of Singapore as smart city


Mobility

The reliability of the rail system in Singapore is important so that citizens can move around

the city, which is why deciding intelligent maintenance strategies especially in the

communications part, is crucial to comply with the regular operation, avoiding delays in the

service.

The travel demand data for a large scale model in Singapore's rapid transit train system allows

to understand the congestion dynamics in this transport means and enables transport

operators to accurately estimate passengers comfort and satisfaction to build efficient

transportation strategies.

The mobility trends are driven by several factors, among these are the social and emotional.

The choice of one or another transport mean depends to a large extent on how it affects the

mentioned factors. Taking this fact into account, it is possible to understand and comprehend

mobility patterns that make it possible to understand how to reduce the traffic levels

congestion in the city in order to offer better and more efficient public and private transport

services.

Energy efficiency

The data use generated by the citizens turns out to be an option to optimize the energy

consumption that, with the help of specialized algorithms, obtain prediction accuracies from

73.14% to 81.2% in Singapore building thermal comfort. These data, used in modeling and

specific studies, establish the bases to carry out efficient energy operations, which, together

with the use and development of smart electricity grids, make it possible to take advantage

of and manage energy to meet the citizen’s needs for electricity consumption.

The green energy generation for lighting purposes can be improved with more complete

and sophisticated systems such as solar plants, which consist of mobile solar panels that, by

means of sensors, follow the sun's illumination, allowing a higher rate of electricity to be

stored by the monitoring carried out on this light source. The results obtained showed that a

solar plant can produce 3.2% more electricity than a conventional solar panel.


The energy saving in homes makes it possible for electric saving levels to increase, but it

is important to consider people's behaviors and perceptions to make it more efficient. By

designing smart systems in the home with modules that involve artificial intelligence, it is

possible to obtain greater control of household appliances. In Singapore to achieve a

successful smart home solution it is necessary to integrate services and public service sectors

such as smart grids and health sectors.

Technological infrastructure

The use of SENSg sensor nodes helps to extend a large number of city environmental sensors

with a large scale IoT deployment, in addition to this, the design of integrated algorithms

allows to reduce the amount of data to be processed demonstrating the ability to use automatic

learning code to optimize the use of central servers, which translates into the rapid display of

the analysis and / or statistics generated by the data obtained from the sensors in the face of

changes in city environmental parameters.

The Big Data applications are varied, one of them is finding the exact points from the

location, type and quantity of materials where can be performed industrial symbiosis

(capture, processing and waste reuse to convert them into resources) in a city industrialized.

Although more research is required to extend the system, the results demonstrated the

feasibility of the approach to develop a proof of concept program using Singapore as a

geographic region.

From all the above, have been built relationships between the different systems and elements

that Singapore has incorporated to become a smart city. Figure 10 shows the integration and

complement of systems that gave the strength of a smart city. The mentioned systems are

highlighted in blue and correspond to smart mobility, energy efficiency, government and

innovation policies, smart health and ICT. It is evident how, through ICT, it is possible to

provide these systems with new solutions and continuous implementations to make them

more and more efficient. The mental map that represents this figure, is established from the

areas in which the city focuses, how through the use of technology it is possible to address

the problems of citizenship to provide effective solutions. The IoT and Big Data with data

management, make possible the innovation and adaptation to change that allow to better


manage the city. The relationships between the systems are established according to the

solutions provided by the city to make this environment a more sustainable medium.

The relationship between smart mobility, energy efficiency and ICT is presented as follows:

the smart mobility system with the use of the electric vehicle increases energy efficiency

(reduction of greenhouse gases) and smart communications make possible acquire data for

transfer through the IoT; in this phase you can make a data processing by means of Big Data

and from this point can be taken the statistics of these data to an E-Government management

for the appropriation of this information by the established Government or, on the other hand,

with these processed data generate innovation in the transport system to start again from the

smart mobility system, presenting a global city system feedback (it is the objective of an

smart city) that increases its efficiency for the achievement in the improvement of the citizen

life quality.

Figure 10. Integrated and complementary system network and elements that make

Singapore a success case as smart city. Source: Authors, based on the information present

in the article.

SINGAPORE

SMART CITY

SMART

HEALTH ICT

ELECTRONIC

GOVERNMENT

SMART

MOBILITY

ENERGY

EFFICIENCY

SMART

METERS

IoT

INTELLIGENT

COMMUNICATIONS

SOLAR

PANELS

RENOWABLE ENERGY INCREASE

POLLUTION

LEVELS

REDUCTION

ELECTRIC

VEHICLE

INNOVATION

SOCIIO-EMOTIONALS

FACTORS

ONE

MONITORING SMART

CARD

CAMERA

NETWORK

TAXIS WITH

GPS IoT

RAIL

SYSTEM

SMART

HOMES

THERMAL

COMFORT

COLLECTION AND

TREATMENT OF RESIDUES

SENSg

NODES BIG

DATA IoT

HYDROPONIC

CULTIVATION

MOBILE

APPS

STUDY AND

CONTROL

ICT SOLUTIONS

DISEASE

PREVENTION

TELE-HEALTH

STUDY

MONITORING


Bibliographic references

[1] T. Nam and T. A. Pardo, “Conceptualizing smart city with dimensions of

technology, people, and institutions,” Article, pp. 282–291, 2011, https://inta-

aivn.org/images/cc/Urbanism/background%20documents/dgo_2011_smartcity.pdf,

DOI: 10.1145/2037556.2037602., 2011

[2] H. Chourabi et al., “Understanding smart cities: An integrative framework,” Proc.

Annu. Hawaii Int. Conf. Syst. Sci., pp. 2289–2297, 2012,

http://observgo.uquebec.ca/observgo/fichiers/78979_B.pdf, DOI:

10.1109/HICSS.2012.615,

[3] J. Gutiérrez Bayo, “International Case Studies of Smart Cities: Santander, Spain,”

Washington, D.C., Jun. 2016,

https://publications.iadb.org/en/publication/17137/international-case-studies-smart-

cities-santander-spain, DOI: http://dx.doi.org/10.18235/0000409,

[4] L. Vidiasova, P. Kachurina, and F. Cronemberger, “Smart Cities Prospects from the

Results of the World Practice Expert Benchmarking,” Procedia Comput. Sci., vol.

119, no. 2017, pp. 269–277, 2017,

https://www.sciencedirect.com/science/article/pii/S1877050917323955, DOI:

10.1016/j.procs.2017.11.185,

[5] Agentshap NL, “Business opportunities in Singapore as a Smart City,” Article, pp.

1–20, 2013, https://www.rvo.nl/sites/default/files/Smart%20Cities%20Singapore.pdf

[6] L. P. O. Lucia, M. G. L. Facundo, and C. V. L. Catalina, Guía Para Construir

Estados Del Arte. Bogotá, 2014, http://iconk.org/docs/guiaea.pdf.

[7] C. Moreno, “Desarrollo De Un Modelo De Evaluación De Ciudades Basado En El

Concepto De Ciudad Inteligente (Smart City),” p. 411, 2015,

http://oa.upm.es/39079/, DOI: 10.20868/UPM.thesis.39079.

[8] B. Mundial, “Indice población - Singapur,” 2018. [Online]. Available:

https://datos.bancomundial.org/indicador/SP.POP.TOTL?locations=SG. [Accessed:

12-Aug-2018].


[9] C. Houghton, J., Reiners, J. & Lim, “Transporte inteligente,” Transp. Intel. Cómo

Mejor. la Movil. en las Ciudad., p. 24, 2009, https://www-

05.ibm.com/services/es/bcs/pdf/transporte-inteligente-como-mejorar-la-movilidad-

en-las-ciudades.pdf

[10] C. E. Seng, “Singapore’s smart nation program — Enablers and challenges,” 2016

11th Syst. Syst. Eng. Conf., pp. 1–5, 2016,

https://www.researchgate.net/publication/306113634_Singapore's_smart_nation_pro

gram_-_Enablers_and_challenges, DOI: 10.1109/SYSOSE.2016.7542892.

[11] C. Harrison, B. Eckman, R. Hamilton, and P. Hartswick, “Foundations for Smarter

Cities,” vol. 54, no. 4, pp. 1–16, 2010,

http://fumblog.um.ac.ir/gallery/902/Foundations%20for%20Smarter%20Cities.pdf,

DOI: 10.1147/JRD.2010.2048257.

[12] F. Telefónica, “Smart Cities: un primer paso hacia la internet de las cosas,” Editor.

Ariel, pp. 13–16, 2011,

https://www.fundaciontelefonica.com/arte_cultura/publicaciones-listado/pagina-

item-publicaciones/itempubli/101/

[13] B. Mundial, “Indice Per Cápita,” 2018. [Online]. Available:

https://datos.bancomundial.org/indicador/NY.GNP.PCAP.CD?locations=SG.

[Accessed: 12-Aug-2018].

[14] M. Kamargianni, W. Li, M. Matyas, and A. Schäfer, “A critical review of new

mobility services for urban transport,” Transp. Res. Procedia, vol. 14, no. 0, pp.

3294–3303, 2016,


10.1016/j.trpro.2016.05.277.

[15] G. Li, L. Yu, W. S. Ng, W. Wu, and S. T. Goh, “Predicting Home and Work

Locations Using Public Transport Smart Card Data by Spectral Analysis,” IEEE

Conference on Intelligent Transportation Systems, Proceedings, ITSC, vol. 2015–

Octob. pp. 2788–2793, 2015, https://ieeexplore.ieee.org/document/7313540, DOI:

10.1109/ITSC.2015.445.


[16] N. Bin Othman, E. F. Legara, V. Selvam, and C. Monterola, “Simulating Congestion

Dynamics of Train Rapid Transit using Smart Card Data,” Procedia - Procedia

Comput. Sci., vol. 29, pp. 1610–1620, 2014,


10.1016/j.procs.2014.05.146.

[17] S. P. Sebhatu and B. Enquist, “Sustainable Public Transit Service Value Network for

Building Living Cities in Emerging Economies: Multiple Case Studies from Public

Transit Services,” Procedia - Soc. Behav. Sci., vol. 224, no. August 2015, pp. 263–

268, 2016, https://ac.els-cdn.com/S1877042816305596/1-s2.0-S1877042816305596-

main.pdf?_tid=b92c4952-538e-4f8f-b5a7-

4f68ecfe0d39&acdnat=1548373299_36d5925f42818730e34b967295a5b82a, DOI:

10.1016/j.sbspro.2016.05.458.

[18] G. Di Pasquale, A. S. Dos Santos, A. G. Leal, and M. Tozzi, “Innovative Public

Transport in Europe, Asia and Latin America: A Survey of Recent

Implementations,” Transp. Res. Procedia, vol. 14, pp. 3284–3293, 2016,


10.1016/j.trpro.2016.05.276.

[19] G. regional metropolitano de Santiago, “Revisión y Actualización del Plan Maestro

de Ciclovías y Plan de Obras,” 2012,

https://es.scribd.com/document/192555924/Revision-y-Actualizacion-del-Plan-

Maestro-de-Ciclovias-y-Plan-de-Obras-Ciclovias-Stgo-2032

[20] M. Goletz, I. Feige, and D. Heinrichs, “What Drives Mobility Trends: Results from

Case Studies in Paris, Santiago de Chile, Singapore and Vienna,” Transp. Res.

Procedia, vol. 13, no. Pucher 2010, pp. 49–60, 2016,


10.1016/j.trpro.2016.05.006.

[21] Y. Xiao et al., “Transportation activity analysis using smartphones,” IEEE

Consumer Communications and Networking. pp. 60–61, 2012,

https://ieeexplore.ieee.org/document/6181051, DOI: 10.1109/CCNC.2012.6181051.


[22] S. Nazir and Y. S. Wong, “Energy and pollutant damage costs of operating electric,

hybrid, and conventional vehicles in Singapore,” Energy Procedia, vol. 14, pp.

1099–1104, 2012, https://ac.els-cdn.com/S187661021104481X/1-s2.0-

S187661021104481X-main.pdf?_tid=7d48d7db-5d23-407b-b6bb-

c56410e11848&acdnat=1548373558_3dc14838ad4428f710fb6dedb18e5781, DOI:

10.1016/j.egypro.2011.12.887.

[23] V. Nian, M. P. Hari, and J. Yuan, “The prospects of electric vehicles in cities without

policy support,” Energy Procedia, vol. 143, pp. 33–38, 2017,

https://reader.elsevier.com/reader/sd/pii/S1876610217364081?token=3C19243D8C5

EA4EFE26A2A5F73B64967292C692ED19A67FCFF85C492715C38EAE80416651

3490C8E7CA7A05044B433BB, DOI: 10.1016/j.egypro.2017.12.644.

[24] M. Y. W. Chia, S. Krishnan, and J. Zhou, “Challenges and opportunities in

infrastructure support for electric vehicles and smart grid in a dense urban

environment-Singapore,” 2012 IEEE Int. Electr. Veh. Conf. IEVC 2012, 2012,

https://www.researchgate.net/publication/237013151_Challenges_and_Opportunities

_in_Infrastructure_Support_for_Electric_Vehicles_and_Smart_Grid_in_a_dense_urb

an_environment-Singapore, DOI: 10.1109/IEVC.2012.6183252.

[25] B. T. Lokesh, J. Tay, and H. Min, “A Framework for Electric Vehicle (EV) Charging

in Singapore The 15th International Symposium on District Heating and Cooling

Assessing,” Energy Procedia, vol. 143, pp. 15–20, 2017,

https://reader.elsevier.com/reader/sd/pii/S1876610217364056?token=93C96457966

B35B305C32F6DA57674C6D717D88B4F14E220FED3B4A44F1335BCDCEB11C

06E7F7448EB2EF42874B02A84, DOI: 10.1016/j.egypro.2017.12.641.

[26] K. N. Kumar and K. J. Tseng, “Impact of demand response management on

chargeability of electric vehicles,” Energy, vol. 111, pp. 190–196, 2016,

https://ac.els-cdn.com/S0360544216307484/1-s2.0-S0360544216307484-

main.pdf?_tid=812e3e88-66ea-47d8-a855-

6ad695c10dcd&acdnat=1548374077_03b85e811b42794c6d3b05c4c50260b5, DOI:

http://dx.doi.org/10.1016/j.energy.2016.05.120.


[27] F. Leurent, “Modeling Transportation Systems involving Autonomous Vehicles: A

State of the Art,” Transp. Res. Procedia, vol. 27, pp. 215–221, 2017,

https://reader.elsevier.com/reader/sd/pii/S2352146517309742?token=6A245C6A19

AEB8839D696550368B5BA24FC9CAAE7E26917F0DCCADEBD8BF8F5E3E331

FB1B65177C11D1A827511E4296B, DOI: 10.1016/j.trpro.2017.12.077.

[28] C. Krishnasamy, C. Unsworth, and L. Howie, “The patterns of activity, and transport

to activities among older adults in singapore,” Hong Kong J. Occup. Ther., vol. 21,

no. 2, pp. 80–87, 2011, https://ac.els-cdn.com/S1569186111000337/1-s2.0-

S1569186111000337-main.pdf?_tid=8352f35a-2e8f-4666-8d49-

e08672f9ebc3&acdnat=1548374183_fff22dea9eeddf23cccb749cad7fda96, DOI:

10.1016/j.hkjot.2011.12.002.

[29] N. Khansari, A. Mostashari, and M. Mansouri, “Conceptual modeling of the impact

of smart cities on household energy consumption,” Procedia Comput. Sci., vol. 28,

no. Cser, pp. 81–86, 2014, https://ac.els-cdn.com/S187705091400074X/1-s2.0-

S187705091400074X-main.pdf?_tid=0a6e3d99-7955-4ddb-a25b-

ff6946633f74&acdnat=1548374222_d8f9d3af84dd35bf34ab4553ad83c9fa, DOI:

10.1016/j.procs.2014.03.011.

[30] P. Alvina, X. Bai, Y. Chang, D. Liang, and K. Lee, “Smart Community Based

Solution for Energy Management: An Experimental Setup for Encouraging

Residential and Commercial Consumers Participation in Demand Response

Program,” Energy Procedia, vol. 143, pp. 635–640, 2017,

https://reader.elsevier.com/reader/sd/pii/S1876610217365037?token=7A0949E8E65

320D5C6E52F2C36BB331757E8197C224ECAA56416A039849B44BCD8CDB2D

0428FCA50C9BFBC36A40C8B2D, DOI: 10.1016/j.egypro.2017.12.739.

[31] L. Chuan, A. Ukil, and S. Member, “Modeling and Validation of Electrical Load Pro

fi ling in Residential Buildings in Singapore,” IEEE Trans. Power Syst., vol. 30, no.

5, pp. 1–10, 2014, https://ieeexplore.ieee.org/document/6965659, DOI:

10.1109/TPWRS.2014.2367509.

[32] C. Wouters, “Towards a regulatory framework for microgrids — The Singapore


experience,” Sustain. Cities Soc., vol. 15, pp. 22–32, 2015, https://ac.els-

cdn.com/S2210670714001152/1-s2.0-S2210670714001152-

main.pdf?_tid=d080bc9c-3994-4bc3-a771-

97b8e81cf845&acdnat=1548374448_854ad823bbf7baae41ff828c1a225c91, DOI:

http://dx.doi.org/10.1016/j.scs.2014.10.007.

[33] A. Kumar, P. Kar, R. Warrier, A. Kajale, and S. K. Panda, “Implementation of Smart

LED Lighting and Efficient Data Management System for Buildings,” Energy

Procedia, vol. 143, pp. 173–178, 2017,

https://reader.elsevier.com/reader/sd/pii/S1876610217364317?token=5313B6E0A73

58598486436CC8AC80FD44E22FA50B27169305596D3829D21DE8199FCE43F4

29C510551509758E625B19E, DOI: 10.1016/j.egypro.2017.12.667.

[34] R. F. Fernandes, C. C. Fonseca, D. Brandão, and S. Carlos, “Flexible Wireless

Sensor Network for smart lighting applications.” 2014,

https://www.researchgate.net/publication/265421808_Flexible_Wireless_Sensor_Ne

twork_for_smart_lighting_applications, DOI: 10.1109/I2MTC.2014.6860782.

[35] H. Kazmi, F. Mehmood, and M. Amayri, “Smart Home Futures: Algorithmic

Challenges and Opportunities,” 2017 14th International Symposium on Pervasive

Systems, Algorithms and Networks & 2017 11th International Conference on

Frontier of Computer Science and Technology & 2017 Third International

Symposium of Creative Computing (ISPAN-FCST-ISCC). pp. 441–448, 2017,

https://www.researchgate.net/publication/321403107_Smart_Home_Futures_Algorit

hmic_Challenges_and_Opportunities, DOI: 10.1109/ISPAN-FCST-ISCC.2017.60.

[36] A. Bhati, M. Hansen, and C. M. Chan, “Energy conservation through smart homes in

a smart city: A lesson for Singapore households,” Energy Policy, vol. 104, no.

February, pp. 230–239, 2017,

https://reader.elsevier.com/reader/sd/pii/S0301421517300393?token=2354E1DBD7

34FF8EDD0708E12C3A29921CE3972B2ED519D714F4AB2008F21B97891387A

C44DACE09C3C4D5ED4E3CEB51, DOI:

http://dx.doi.org/10.1016/j.enpol.2017.01.032.


[37] J. Bugeja, A. Jacobsson, and P. Davidsson, “On privacy and security challenges in

smart connected homes,” Proceedings - 2016 European Intelligence and Security

Informatics Conference, EISIC 2016. pp. 172–175, 2017,

https://www.researchgate.net/publication/314266078_On_Privacy_and_Security_Ch

allenges_in_Smart_Connected_Homes, DOI: 10.1109/EISIC.2016.044.

[38] C. Wongvises, A. Khurat, D. Fall, and S. Kashihara, “Fault tree analysis-based risk

quantification of smart homes,” 2017 2nd International Conference on Information

Technology (INCIT). pp. 1–6, 2017, https://ieeexplore.ieee.org/document/8257865,

DOI: 10.1109/INCIT.2017.8257865.

[39] L. G. H. Brenda, “Grid Code Compliance for Grid-Connecting a PV System to an

Existing Facility in Singapore,” pp. 530–533, 2016,

https://www.researchgate.net/publication/313585339_Grid_code_compliance_for_gr

id-connecting_a_PV_system_to_an_existing_facility_in_Singapore, DOI:

10.1109/TENCON.2016.7848056.

[40] B. T. Jiang et al., “A Microgrid Test Bed in Singapore,” pp. 74–82, 2017,

https://www.researchgate.net/publication/317422866_A_Microgrid_Test_bed_in_Si

ngapore_An_electrification_project_for_affordable_access_to_electricity_with_opti

mal_asset_management, DOI: 10.1109/MELE.2017.2685978.

[41] Y. S. Khoo, T. Reindl, A. G. Aberle, and S. Member, “Optimal Orientation and Tilt

Angle for Maximizing in-Plane Solar Irradiation for PV Applications in Singapore,”

vol. 4, no. 2, pp. 647–653, 2014,

https://www.researchgate.net/publication/260523918_Optimal_Orientation_and_Tilt

_Angle_for_Maximizing_in-

Plane_Solar_Irradiation_for_PV_Applications_in_Singapore, DOI:

10.1109/JPHOTOV.2013.2292743.

[42] W. Z. An, “Household Solar ‘ Plant ’ for Singapore,” no. October, pp. 25–27, 2016,

https://ieeexplore.ieee.org/document/7811504, DOI:

10.1109/ACCEPT.2016.7811504.

[43] S. D. Yadav, B. Kumar, and S. S. Thipse, “Biogas purification: Producing natural


gas quality fuel from biomass for automotive applications,” 2013 International

Conference on Energy Efficient Technologies for Sustainability, ICEETS 2013. pp.

450–452, 2013,

https://www.researchgate.net/publication/261501422_Biogas_purification_Producin

g_natural_gas_quality_fuel_from_biomass_for_automotive_applications, DOI:

10.1109/ICEETS.2013.6533425.

[44] D. L. K. Heng, “Bio Gas Plant Green Energy from Poultry Wastes in Singapore,”

Energy Procedia, vol. 143, pp. 436–441, 2017,

https://reader.elsevier.com/reader/sd/pii/S187661021736472X?token=E7BA82273E

871F82F38645DF2E76FBC53E35CBD92EDF5F5664CC637DB80301EB2B9FEF0

285DFE3BB7BDDE50964F10F49, DOI: 10.1016/j.egypro.2017.12.708.

[45] X. Xi and K. Leng, “Using system dynamics for sustainable water resources

management in Singapore,” vol. 16, pp. 157–166, 2013, https://ac.els-

cdn.com/S1877050913000185/1-s2.0-S1877050913000185-

main.pdf?_tid=e4f451df-6278-4fa0-978e-

371e02e13908&acdnat=1548375155_6e1a4129aafcb013571bb9f0d335c348, DOI:

10.1016/j.procs.2013.01.017.

[46] BID, “La ruta hacia las Smart Cities,” 2016,

https://publications.iadb.org/publications/spanish/document/La-ruta-hacia-las-smart-

cities-Migrando-de-una-gesti%C3%B3n-tradicional-a-la-ciudad-inteligente.pdf,

DOI: http://dx.doi.org/10.18235/0000377#sthash.qN13MvGV.dpuf.

[47] TELEFONICA, “La Ciudad como plataforma de Transformación Digital,” 2016,

https://www.telefonica.com/documents/341171/3261893/POLICY+PAPER_Smart+

Cities_ES+La+Ciudad+como+plataforma+de+Transformaci%C3%B3n+Digital++A

bril+2016.pdf/2c8ed5af-8690-44c2-aab0-4cbe3d1d89c2.

[48] T. Nam and T. A. Pardo, “Smart city as urban innovation,” 5th Int. Conf. Theory

Pract. Electron. Gov., p. 185, 2011,

https://www.researchgate.net/publication/221547712_Smart_city_as_urban_innovati

on_Focusing_on_management_policy_and_context, DOI:


10.1145/2072069.2072100.

[49] J. Wang, “Innovation and government intervention: A comparison of Singapore and

Hong Kong,” Res. Policy, vol. 47, no. 2, pp. 399–412, 2018,

https://reader.elsevier.com/reader/sd/pii/S0048733317302147?token=604022FDC5E

D64DD8F9017C3EB4BBD09BDF28D65A2585CA2D11996A644331B665028A8C

A44EB9DDC03BAC42CBE8A3CBB, DOI:

http://dx.doi.org/10.1016/j.respol.2017.12.008.

[50] S. M. Lee and S. Trimi, “Innovation for creating a smart future,” Suma Negocios,

vol. 3, no. 1, pp. 1–8, 2018,

https://reader.elsevier.com/reader/sd/pii/S2444569X16300154?token=F9E36FA29E

A0F20E18A8434C90FE68D22C368C84B5314AC68F89C1F646B3F4639418AB4F

02C578C5D7D2D0D839AA76CC, DOI: http://dx.doi.org/10.1016/j.jik.2016.11.001.

[51] V. Ramos González, “Las TIC en el sector de la salud,” Bit, no. 163, pp. 41–45,

2007,

https://www.researchgate.net/publication/28168792_Las_TIC_en_el_sector_de_la_s

alud

[52] E. Tan and H. So, “Rethinking the Impact of Activity Design on a Mobile Learning

Trail : The Missing Dimension of the Physical Affordances,” vol. 8, no. 1, pp. 98–

110, 2015,

https://www.researchgate.net/publication/273890541_Rethinking_the_Impact_of_A

ctivity_Design_on_a_Mobile_Learning_Trail_The_Missing_Dimension_of_the_Phy

sical_Affordances, DOI: 10.1109/TLT.2014.2376951.

[53] W. Yang, N. H. Wong, and Y. Lin, “Thermal Comfort in High-rise Urban

Environments in Singapore,” Procedia Eng., vol. 121, pp. 2125–2131, 2015,

https://ac.els-cdn.com/S1877705815029112/1-s2.0-S1877705815029112-

main.pdf?_tid=28d57a52-4e24-4940-901b-

62588edfe336&acdnat=1548375556_c7c9bf8456ec64042fa52ad5b6120c85, DOI:

10.1016/j.proeng.2015.09.083.

[54] C. Deb, L. S. Eang, J. Yang, and M. Santamouris, “Forecasting Energy Consumption


of Institutional Buildings in Singapore,” Procedia Eng., vol. 121, pp. 1734–1740,

2015, https://ac.els-cdn.com/S1877705815029720/1-s2.0-S1877705815029720-

main.pdf?_tid=1b155e89-89aa-402f-87bc-

f6f9b523e1d5&acdnat=1548375597_2faf1e1292944fb053bd526a586f1de0, DOI:

10.1016/j.proeng.2015.09.144.

[55] T. Chaudhuri, Y. C. Soh, H. Li, and L. Xie, “Machine Learning Based Prediction of

Thermal Comfort in Buildings of Equatorial Singapore,” pp. 72–77, 2017,

https://www.researchgate.net/publication/316155534_Machine_Learning_based_Pre

diction_of_Thermal_Comfort_in_Buildings_of_Equatorial_Singapore, DOI:

10.1109/ICSGSC.2017.8038552.

[56] P. H. Chiu et al., “CFD Methodology Development for Singapore Green Mark

Building Application,” Procedia Eng., vol. 180, pp. 1596–1602, 2017,


10.1016/j.proeng.2017.04.322.

[57] N. H. Wong, E. Tan, O. Gabriela, and S. K. Jusuf, “Indoor Thermal Comfort

Assessment of Industrial Buildings in Singapore,” Procedia Eng., vol. 169, pp. 158–

165, 2016, https://www.sciencedirect.com/science/article/pii/S1877705816332210,

DOI: 10.1016/j.proeng.2016.10.019.

[58] G. Happle, E. Wilhelm, J. A. Fonseca, and A. Schlueter, “Determining air-

conditioning usage patterns in Singapore from distributed, portable sensors,” Energy

Procedia, vol. 122, pp. 313–318, 2017,


10.1016/j.egypro.2017.07.328.

[59] Y. Liu and Y. Wu, “Smart Maintenance via Dynamic Fault Tree Analysis : A Case

Study on Singapore MRT System,” 2017,

https://ieeexplore.ieee.org/document/8023149, DOI: 10.1109/DSN.2017.50.

[60] P. Joglekar and V. Kulkarni, “Data oriented view of a smart city: A big data

approach,” 2017 Int. Conf. Emerg. Trends Innov. ICT, pp. 51–55, 2017,

https://www.researchgate.net/publication/318408220_Data_oriented_view_of_a_sm


art_city_A_big_data_approach, DOI: 10.1109/ETIICT.2017.7977009.

[61] S. Jun, H. Sun, and S. Choi, “Technological Forecasting & Social Change Ten years

of research change using Google Trends : From the perspective of big data

utilizations and applications,” Technol. Forecast. Soc. Chang., vol. 130, no.

November 2017, pp. 69–87, 2018,


https://doi.org/10.1016/j.techfore.2017.11.009.

[62] D. Zehe, A. Knoll, W. Cai, and H. Aydt, “Simulation Modelling Practice and Theory

SEMSim Cloud Service : Large-scale urban systems simulation in the cloud,” vol.

58, pp. 157–171, 2015,

https://www.sciencedirect.com/science/article/pii/S1569190X15000805, DOI:

http://dx.doi.org/10.1016/j.simpat.2015.05.005.

[63] E. Al Nuaimi, H. Al Neyadi, N. Mohamed, and J. Al-jaroodi, “Applications of big

data to smart cities,” J. Internet Serv. Appl., 2015,

https://www.researchgate.net/publication/284196317_Applications_of_big_data_to_

smart_cities, DOI: 10.1186/s13174-015-0041-5.

[64] A. J. Jara, D. Genoud, and Y. Bocchi, “Big Data in Smart Cities : From Poisson to

Human Dynamics,” 2014, https://ieeexplore.ieee.org/document/6844735, DOI:

10.1109/WAINA.2014.165.

[65] K. Laohalidanond, P. Chaiyawong, and S. Kerdsuwan, Municipal Solid Waste

Characteristics and Green and Clean Energy Recovery in Asian Megacities, vol. 79.

Elsevier B.V., 2015,


10.1016/j.egypro.2015.11.508.

[66] S. Bin, Y. Zhiquan, L. Sze, C. Jonathan, D. Koh, and D. Kurle, “A big data analytics

approach to develop industrial symbioses in large cities,” Procedia CIRP, vol. 29,

pp. 450–455, 2015,


10.1016/j.procir.2015.01.066.


[67] B. Song, Z. Yeo, P. Kohls, and C. Herrmann, “Industrial Symbiosis: Exploring Big-

data Approach for Waste Stream Discovery,” Procedia CIRP, vol. 61, pp. 353–358,

2017, https://www.sciencedirect.com/science/article/pii/S2212827116314147, DOI:

10.1016/j.procir.2016.11.245.

[68] X. Kong et al., “Mobility Dataset Generation for Vehicular Social Networks Based

on Floating Car Data,” IEEE Transactions on Vehicular Technology. 2017,

https://ieeexplore.ieee.org/document/8242676, DOI: 10.1109/TVT.2017.2788441.

[69] A. Sergio, and P. Fourie, “Multi-agent urban transport simulations using OD

matrices from mobile phone data,” Procedia Comput. Sci., vol. 130, pp. 803–809,

2018, https://www.sciencedirect.com/science/article/pii/S1877050918305015, DOI:

10.1016/j.procs.2018.04.139.

[70] D. Milne and D. Watling, “Big data and understanding change in the context of

planning transport systems,” J. Transp. Geogr., no. November, pp. 0–1, 2017,


http://dx.doi.org/10.1016/j.jtrangeo.2017.11.004.

[71] L. Yuqing, “Research on Personal Information Security on Social Network in Big

Data Era.” 2017, https://ieeexplore.ieee.org/document/8104477, DOI:

10.1109/ICSGEA.2017.91.

[72] C. Shao, A. Moens, and M. Crompton, “The Global Changing Privacy Landscape,”

no. March, pp. 924–929, 2014, https://ieeexplore-ieee-

org.bdigital.udistrital.edu.co/stamp/stamp.jsp?tp=&arnumber=6758533, DOI:

10.1109/LCNW.2013.6758533.

[73] I. Yaqoob, I. A. T. Hashem, Y. Mehmood, A. Gani, S. Mokhtar, and S. Guizani,

“Enabling communication technologies for smart cities,” IEEE Commun. Mag., vol.

55, no. 1, pp. 112–120, 2017,

https://www.researchgate.net/publication/309210290_Enabling_Communication_Te

chnologies_for_Smart_Cities, DOI: 10.1109/MCOM.2017.1600232CM.

[74] D. Estrin, “Participatory sensing: applications and architecture [Internet

Predictions],” IEEE Internet Comput., vol. 14, no. 1, pp. 12–42, 2010,


https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5370818, DOI:

10.1109/MIC.2010.12.

[75] Z. Xiao, H. Lim, and L. Ponnambalam, “Participatory Sensing for Smart Cities : A

Case Study on Transport Trip Quality Measurement,” vol. 13, no. 2, pp. 759–770,

2017,

https://www.researchgate.net/publication/314270951_Participatory_Sensing_for_Sm

art_Cities_A_Case_Study_on_Transport_Trip_Quality_Measurement, DOI:

10.1109/TII.2017.2678522.

[76] a Zanella, N. Bui, a Castellani, L. Vangelista, and M. Zorzi, “Internet of Things for

Smart Cities,” IEEE Internet Things J., vol. 1, no. 1, pp. 22–32, 2014,


10.1109/JIOT.2014.2306328.

[77] Y. Mehmood, C. Görg, M. Muehleisen, and A. Timm-Giel, “Mobile M2M

communication architectures, upcoming challenges, applications, and future

directions,” EURASIP J. Wirel. Commun. Netw., vol. 2015, no. 1, p. 250, 2015,

https://link.springer.com/article/10.1186/s13638-015-0479-y, DOI: 10.1186/s13638-

015-0479-y.

[78] S. T. Ng, F. J. Xu, Y. Yang, and M. Lu, “A Master Data Management Solution to

Unlock the Value of Big Infrastructure Data for Smart, Sustainable and Resilient

City Planning,” Procedia Eng., vol. 196, no. June, pp. 939–947, 2017,


10.1016/j.proeng.2017.08.034.

[79] J. Wan, D. Li, C. Zou, and K. Zhou, “M2M communications for smart city: An

event-based architecture,” Proc. - 2012 IEEE 12th Int. Conf. Comput. Inf. Technol.

CIT 2012, pp. 895–900, 2012,

https://www.researchgate.net/publication/261279762_M2M_Communications_for_S

mart_City_An_Event-Based_Architecture, DOI: 10.1109/CIT.2012.188.

[80] A. Schmitt, “Dynamic bridge generation for IoT data exchange via the MQTT

protocol,” vol. 130, pp. 90–97, 2018,



10.1016/j.procs.2018.04.016.

[81] E. Wilhelm et al., “Wearable Environmental Sensors and Infrastructure for Mobile

Large-Scale Urban Deployment,” vol. 16, no. 22, pp. 8111–8123, 2016,


10.1109/JSEN.2016.2603158.

[82] F.Shrouf, “Smart Factories in Industry 4.0.” 2014,

https://ieeexplore.ieee.org/document/7058728, DOI: 10.1109/IEEM.2014.7058728.

[83] W. Rong, G. T. Vanan, and M. Phillips, “The internet of things (IoT) and

transformation of the smart factory,” 2016 International Electronics Symposium

(IES). Proceedings. pp. 399–402, https://ieeexplore.ieee.org/document/7861039,

DOI: 10.1109/ELECSYM.2016.7861039.

[84] Y. Shee, Y. Ting, J. Sze, and C. Low, “Internet-of-Things Enabled Real-Time

Monitoring of Energy Efficiency on Manufacturing Shop Floors,” Procedia CIRP,

vol. 61, pp. 376–381, 2017,


10.1016/j.procir.2016.11.242.

[85] T. Y. Kyaw and A. K. Ng, “Smart Aquaponics System for Urban Farming,” Energy

Procedia, vol. 143, pp. 342–347, 2017,


10.1016/j.egypro.2017.12.694.

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