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Chapter 41
Cloud, Edge, and Mobile Computing
for Smart Cities
Qian Liu, Juan Gu, Jingchao Yang, Yun Li, Dexuan Sha, Mengchao Xu,
Ishan Shams, Manzhu Yu, and Chaowei Yang
Abstract Smart cities evolve rapidly along with the technical advances in wireless
and sensor networks, information science, and human–computer interactions. Urban
computing provides the processing power to enable the integration of such tech-
nologies to improve the living quality of urban citizens, including health care, urban
planning, energy, and other aspects. This chapter uses different computing capabil-
ities, such as cloud computing, mobile computing, and edge computing, to support
smart cities using the urban heat island of the greater Washington DC area as an
example. We discuss the benefits of leveraging cloud, mobile, and edge computing
to address the challenges brought by the spatiotemporal dynamics of the urban heat
island, including elevated emissions of air pollutants and greenhouse gases, compro-
mised human health and comfort, and impaired water quality. Cloud computing
Q. Liu ·J. Yang ·Y. L i ·D. Sha ·M. Xu ·I. Shams ·M. Yu ·C. Yang (B
)
NSF Spatiotemporal Innovation Center & Department of Geography and GeoInformation
Science, George Mason University, Fairfax, USA
e-mail: cyang3@gmu.edu
Q. Liu
e-mail: qliu6@gmu.edu
J. Yang
e-mail: jyang43@gmu.edu
Y. L i
e-mail: yli38@gmu.edu
D. Sha
e-mail: dsha@gmu.edu
M. Xu
e-mail: mxu6@gmu.edu
I. Shams
e-mail: ishams@gmu.edu
M. Yu
e-mail: myu7@gmu.edu
J. Gu
Beijing Institute of Surveying and Mapping, Beijing, China
e-mail: gujuan@bism.cn
© The Author(s) 2021
W.Shietal.(eds.),Urban Informatics, The Urban Book Series,
https://doi.org/10.1007/978-981- 15-8983- 6_41
757
758 Q. Liu et al.
brings scalability and on-demand computing capacity to urban system simulations
for timely prediction. Mobile computing brings portability and social interactivity for
citizens to report instantaneous information for better knowledge integration. Edge
computing allows data produced by in-situ devices to be processed and analyzed at the
edge of the network, reducing the data traffic to the central repository and processing
engine (data center or cloud). Challenges and future directions are discussed for
integrating the three computing technologies to achieve an overall better computing
infrastructure supporting smart cities. The integration is discussed in aspects of band-
width issue, network access optimization, service quality and convergence, and data
integrity and security.
41.1 Introduction
41.1.1 Why Computing is Important in Smart Cities
Increasing global urbanization generates many problems, such as traffic conges-
tion, energy consumption, industrial waste, and heat islands (Rao and Rao 2012;
González-Gil et al. 2014;Lietal.2012; Zhong et al. 2017; Rizwan et al. 2008).
These problems produce serious negative impacts on urban residents. For example,
an urban heat island (UHI) in an urban area or metropolitan area is significantly
warmer than its surrounding rural areas due to human activities. UHI contributes
directly to environmental warming, industrial waste, air pollution, and heat-related
mortality (Petkova et al. 2016). In order to alleviate urban problems and achieve
sustainable development, a number of smart-city solutions have been the subject
of experiments in cities over the past two decades. Copenhagen Municipality uses
monitor sensors installed in different trash containers and information systems to
optimize waste handling (State of Green Denmark 2018). Seoul of South Korea has
smart meters installed in residential houses, office areas, and industrial facilities to
report in real time the consumption of electricity, water, and gas (Hwang and Choe
2013). Smart cities are supported by key information and communications technolo-
gies (ICT) including the Internet of things (IoT), computing platforms, big data, arti-
ficial intelligence (AI), geographical information, and others (Graham and Marvin
2002; Morán et al. 2016; Mitchell et al. 2013) (Fig. 41.1). Among them, diverse
sensors, stable communication networks, and sophisticated computing platforms are
three fundamental technologies for smart cities. Sensors are the smart-city’s sensory
organs, to capture and integrate data continuously in real time. Smart sensors, such
as monitoring cameras, smart meters, and wearable devices, are widely employed
to improve urban transportation, utility planning, parking-lot management, pollution
monitoring, and health care. The number of connected devices on the Internet will
exceed 50 billion by 2020 according to Cisco (2017). The communication network
is the smart-city’s transmission system, transmitting data from sensors to computing
platforms. Reliable, scalable, and high-speed networks, including wired and wireless
41 Cloud, Edge, and Mobile Computing for Smart Cities 759
Fig. 41.1 Key technologies of smart cities
networks, are fundamental infrastructure for such transmission. Computing platforms
support the management and analyses of relevant city data in a broader context, to
identify city-relevant events that require processing and action. A large quantity of
data is generated continuously from countless smart-city sensors. To store, process,
and analyze the massive heterogeneous data, a stable, scalable, fast computing plat-
form is required. For example, car drivers need a smart navigation system to provide
them with the optimal driving route in real time, updated dynamically with traffic
pattern and congestion changes. Different systems and devices using ICT have been
developed to monitor and forecast UHI in the past years. For example, France devel-
oped a Heat Health Watch Warning System to monitor heat waves that may result
in a large increase of mortality (Casanueva et al. 2019). Greece developed a UHI
modeling system to simulate and forecast heat islands in Athens (Giannaros et al.
2014). Richmond has handmade devices equipped in cars and bikes to map UHI
(Hoffiman 2018).
41.1.2 Major Computing Techniques in Smart City Studies
Washburn et al. (2009) described the smart city as using a collection of smart
computing technologies to manage critical infrastructure components and services.
A centralized cloud-computing architecture has been widely deployed in smart cities
to extend the storage capability and improve the processing velocity with character-
istics of elastically, on-demand, and pay-as-you-go computing resources (Yang and
Huang 2013). Cloud computing maximizes the utilization rate of physical resources
760 Q. Liu et al.
by adopting a series of technologies including virtualization and network secu-
rity. Virtualization is a core technology supporting cloud computing, and abstracts
actual hardware as virtual computer systems. Virtualization enables multiple oper-
ating systems to run on a computer system simultaneously and optimizes the use of
computing and storage resources. Practically, cloud computing virtualizes computer
resources and manages them in a resource pool to provide computing services over
the network, reducing the idle time of resources including CPU, RAM, network,
and storage. Public clouds (e.g., Amazon AWS, Microsoft Azure) are open to the
public, who pay to use them. On the other hand, a private cloud is delivered via a
secure private network and usually shared among people in a single organization.
Cloud computing provides the smart city with the computing capability to store and
access data and applications outside local computing environment through computer
networks (Kakderi et al. 2016).
The proliferation of IoT enables smart cities to collect a large number of data and
deploy a lot of applications at the edge to utilize these data (Shi et al. 2016). The data
and applications also produce challenges of near-real-time response, privacy, and
massive numbers of data for network transmission. Cloud computing alone is not
sufficient to address such challenges. A new computing paradigm, edge computing,
which shifts the data storage, processing and analyses to the end of the network, as
close as possible to the devices, is deployed (Shi et al. 2016). With the aid of edge
computing, the edges of network become data producers as well as data processors,
addressing the challenge of response time, bandwidth, data safety, and privacy (Shi
et al. 2016). Edge computing offers a number of benefits, including allowing services
to continue to operate when there is no connection to the Internet, and processing
data locally. This significantly reduces the network load with only processing results
(which are normally smaller in volume than raw data) being transmitted across the
network.
The past two decades have witnessed the increasingly use of mobile devices (such
as mobile phones, portable computers, wearable devices, and smart vehicles) and
rapid growth of wireless communication technology (Hashim Raza Bukhari et al.
2018). Data processing is shifted away from centralized computing centers to the
mobile devices of end user. With battery volume and network bandwidth limitations,
computing resources offered by mobile computing are not as reliable as the other
two computing frameworks. Nevertheless, they are portable and able to collect and
process data where cloud computing and edge computing are unavailable.
The three computing paradigms collaboratively provide a comprehensive and
reliable data store and processing framework to overcome the disadvantages of a
single device and enable a suite of applications of smart cities (Table 41.1) including:
transport and traffic management, utilities and energy management, environmental
protection and sustainability, public safety, and smart-city security.
Figure 41.2 illustrates the sensors and computing devices of a smart city and places
them into three types: different sensors collecting different information for different
purposes. The sensors also have embedded computing capabilities; for example,
moving sensors can be used to provide flexible data collection to dynamically cover
different regions with fast situation-aware processing capabilities such as navigation.
41 Cloud, Edge, and Mobile Computing for Smart Cities 761
Table 41.1 Application examples of cloud, edge and mobile computing in smart cities
Application
examples
Computing paradigm
Cloud computing Edge computing Mobile computing
Transport and
traffic
management
Using cloud
computing for
smart-city logistics
(Nowicka 2014)
Connected parking
meters (David 2018)
Location-aware mobile
applications (Altman
et al. 2015)
Utilities and
energy
management
Using cloud
computing for smart
grid energy
management (Bera
et al. 2015)
Street lighting (David
2018)
The use of GPRS
technology for electricity
network telecontrol
(Souza et al. 2016)
Environmental
protection and
sustainability
Using cloud
computing for climate
analysis and
simulation (Yang et al.
2017a,b)
Vehicular pollution
system based on IoT
(Rushikesh and
Sivappagari 2015)
Location-aware weather
report applications
(Altman et al. 2015)
Public safety and
smart-city
security
Cloud computing
services in medical
heath care solutions
(Kaushal and Khan
2014)
Smart home (Shi et al.
2016)
Healthcare applications
(Hameed 2003)
Lost child application
(Satyanarayanan 2010)
Fig. 41.2 Urban computing for smart cities include cloud computing (gray), edge computing
(orange), and mobile computing (blue) devices and capabilities
762 Q. Liu et al.
Edge-computing sensors act as fixed data collectors with various computing powers
depending on tasks assigned; for example, a higher edge-computing capacity enables
handling analytics for a larger area, like a neighborhood. All data and processes can
also be uploaded to the cloud’s centralized computing, for extensive data processing
and knowledge extraction or mining.
Computing serves as an indivisible capability to support effective and efficient
smart-city applications and research, through which massive smart-city data can be
processed in parallel and in a real-time manner. This chapter introduces the three
computing paradigms’ engagement in a smart city using UHI as a case study. A
workflow was proposed to integrate three computing techniques as a seamless inte-
gration for handling UHI problem (one of the severe urban challenges facing us today
especially with climate and global change).
This chapter starts with an introduction to urban computing in 41.1, followed by
the current status and challenges of computing in different smart-city scenarios.
Sections. 41.3,41.4 and 41.5 introduce, respectively, cloud computing, edge
computing, and mobile computing using UHI as a use case. The last section uses
UHI as an example to integrate the three computing paradigms through collaborative
workflow.
41.2 Computing for Smart Cities
41.2.1 Data and Model in Smart Cities
Smart cities require multiple data sources and reliable models to produce decision-
supporting information. It becomes especially challenging when a massive number
of smart devices and sensors are engaged. This section introduces five typical smart-
city applications, the data engaged, corresponding models, and their requirements
for computing.
41.2.1.1 Transport and Traffic Management
Transportation is one of the most important aspects for urban-living activities. Various
sources of transportation data are related to people’s travel and commuting, which
is a complicated and indispensable part of smart cities. For example, traffic data
are generated and collected by sensors in traffic vehicles (e.g., taxis, buses, metros,
trains, vessels, and planes) or monitors installed along the roads (e.g., loop sensors
and surveillance cameras). Commuting data refer to data that record people’s regular
movement in cities. Geo-tagged social network data collect posts (e.g., blogs, tweets)
through social networks which are tagged with geoinformation. Road network data
represent road segments and intersections, respectively. The transportation network
is modeled as a directed graph which includes transit routes and stop facilities of
41 Cloud, Edge, and Mobile Computing for Smart Cities 763
buses and metro networks. Point of interest (POI) data depict related information for
facilities, such as restaurants, shopping malls, parks, airports, schools, and hospitals
in the city, which helps guiding people to find their destinations.
To handle and integrate the complex data from different sources efficiently and to
satisfy various user groups, different models are used for intelligent transportation
systems, such as agent-based traffic management models (Sciences et al. 2011),
cognitive rationality-based decision-making models (Cascetta et al. 2015) and mixed-
ranked logit models (Liu et al. 2017).
41.2.1.2 Utilities and Energy Management
The large volume of data for utilities and energy management is increasingly adding
burden to urban computing systems, especially with the wide adoption of sensors,
wireless transmission, and network communication (Zhou et al. 2017). The input data
of smart-city energy systems include numeric data, text-based data, and audio-visual
data. Numeric data refer to the observations and collections from sensors and meters,
such as power quality, customer usage, and electrical production. Text-based data
sources are mainly internal and external communications, regulatory documents,
legal documents, and linguistic social media records. Audio-visual data are records
and social media data in the form of sound and video (Schuelke-Leech et al. 2015).
The utilities and energy management systems should be green, sustainable, and
with high operational speed and efficiency. Schuelke-Leech et al. (2015) demonstrate
how future sustainable energy systems will be smart and integrated with smart grids,
renewable sources, storage, and energy management and monitoring systems. The
energy and utility systems of cities are complicated because they have to satisfy a
huge number of requirements with comparably limited supply. The computational
systems need not only to integrate intermittent power sources efficiently and effec-
tively, but also to predict equipment failures and power outages, allowing utilities to
optimize their maintenance budgets. For example, Sheikhi et al. (2015) presented an
Energy Hub Model in a future vision of energy systems, which supported real-time
and two-way computational communication between utility companies and smart
energy hubs. Such models also allowed intelligent infrastructures at both ends, since
to manage power consumption necessitates large-scale real-time computing capabil-
ities to handle the communication and the storage of big data. These systems help
managers, employees, and consumers to make informed decisions based on data and
empirical investigation, rather than on intuition or past practice.
41.2.1.3 Environmental Protection and Sustainability
Environmental protection and sustainability also play important roles in smart cities.
The environmental resources refer to minerals, forests and grasslands, wetlands,
rivers, lakes, and the ocean. These natural resources have been exploited unduly, and
the inappropriate management of natural resources has caused severe environmental
764 Q. Liu et al.
degradation (Song et al. 2017). The data that urban environmental protection and
sustainability management systems are dealing with include hydrogeological data,
environmental surveillance data, ecological statistics, and meteorological data. The
data quantity and dimensions are big according to the characteristics of big data. The
functions of these data are not only to accurately present the current situation of the
environment but also to effectively predict the future and sustainability. Therefore,
powerful computational ability is needed to help governments and individual users
to prevent and settle environmental challenges.
As environmental protection and sustainability are important factors for the devel-
opment of smart cities, data collection and computational models have flourished
in this domain. Take the IoT and its associated computing model as an example:
the informational landscape of smart sustainable cities and big data applications is
augmented to achieve the required level of environmental sustainability (Bibri 2018).
For governments, the combination of 3D GIS and cloud computing is also offering
effective services in the environmental management of smart cities (Lv et al. 2018).
41.2.1.4 Public Safety and Security
Public safety and security are directly related to citizens’ wellbeing and their lives.
With the growth of different kinds of monitoring devices and systems, data from the
IoT, unmanned aerial vehicles (UAV) (Menouar et al. 2017), and social media are
leveraged to make our cities more and more safe and stable. Usually, the safety and
security issues are directly related to people’s life and property, and needs immediate
and accurate response from relevant personnel. Therefore, extremely high perfor-
mance in efficiency and accuracy is needed for safety and security models and
systems. Edge and mobile computing, which can share the burden of the central
cloud and improve processing speed, are ideal for the applications such as finding
a lost child (Shi et al. 2016). Wearable devices and medical sensors can measure
users’ health conditions and send health data to the processing unit for doctors’
further diagnosis.
To address these challenges, safety systems should include the following data
sources and model features: health care and monitoring systems; smart safety systems
for surveillance; smart systems of crisis management to support decision making,
early warning, monitoring and forecasting emergencies; centrally operated units
of police and integrated rescue systems (IRS); safe Internet connection and data
protection; and centers of data processing (Lacinák and Ristvej 2017).
41.2.1.5 Urban Heat Island and Urban Computing
Urban computing utilizes the three computing paradigms to store, process, integrate,
model, and analyze various big data and phenomena, such as real-time data generated
by diverse smart sensors and devices, fundamental urban geographical data, social
media data, data on transportation on flooding, and on UHI. UHI is considered one
41 Cloud, Edge, and Mobile Computing for Smart Cities 765
of the major urban challenges and is caused by a set of complex factors, including
urban land use changes, solar radiation, anthropogenic heat sources, climate change,
urban development, and wind speed and direction (Memon et al. 2009). The negative
effects of UHI include: (1) increasing temperature in cities (Voogt and Oke 2003);
(2) contribution to global warming (Van Weverberg et al. 2008;EPA2016); (3)
air pollution (Sarrat et al 2006; Davies et al. 2008); (4) increasing energy demand
(Santamouris et al. 2001; Santamouris 2015); and (5) heat-related mortality (Guest
et al. 1999;Contietal.2005; Haines et al. 2006; Filleul et al. 2006; Hondula, et al.
2014).
To reduce the negative impact of UHI, remotely sensed data, stationary meteo-
rological monitoring data, building data, digital elevation data and other data were
integrated to model, monitor, simulate, and evaluate UHI in more than 100 cities in
the past 50 years. However, UHI studies involve big data storage, processing, and
modeling, which need complicated computing. There is no single efficient computing
architecture for large-scale or long-term UHI studies. This chapter takes UHI as an
example to introduce how the combination of cloud, edge, and mobile computing can
help addressing the smart city challenges in sequence of: (1) what are the computing
challenges of smart cities; (2) how the three computing paradigms can help address
the challenges; and (3) how to integrate the three computing paradigms to address
these challenges using UHI as an example.
41.2.2 Computing Challenges in Smart Cities
41.2.2.1 Big Data Handling
Urban data have been harvested from various sources including (1) remote sensing,
(2) in-situ sensing, (3) social sensing, (4) IoT sensing, and (5) simulation. The
collected data together provide a comprehensive view of the urban system: for
example, the underground water distribution network for water usage management
(Karwotetal.2016), real-time parking prediction (Vlahogianni et al. 2016), and 3D
city modeling for urban disaster management (Amirebrahimi et al. 2016). However,
the sensing and simulation produce large numbers of data that far exceed the storage
capacity of an individual computer. Taking remote sensing as an example, fine
spatiotemporal resolution imagery grows exponentially with spatial resolution. For
example, the volume of the Earth Observing System and Data Information System
(EOSDIS) data archive was more than 27.5 petabytes (PB) at the end of fiscal year
2018 (NASA Earth Science Data Systems Program Highlights 2018). Efficiently
storing such a large volume of data is a challenging task. Meanwhile, data are
produced in high velocity in a continuous manner with the development of advanced
techniques, such as water meters, which collect water usage data in a fixed interval
(e.g., every 30 s). The velocity of data requires streaming data collection and anal-
ysis methods for near-real-time applications. In addition, the heterogeneous data are
stored in various file formats, such as image, video, text, or audio, and pose grand
challenges to data management.
766 Q. Liu et al.
41.2.2.2 Compute-Intensive Modeling and Processing
The smart city is becoming a sophisticated ecosystem where massive data are being
collected and innovative solutions are being proposed to deliver smart services
(Anthopoulos 2015). Generally, those solutions rely on complicated data models and
analytics with the aid of the computer. Data models often represent objects or situa-
tions in the real world, and a digital model makes mathematical analysis possible. For
example, a trend in smart cities is to build three-dimensional (3D) models for visual-
ization and analytics such as skyline analysis, underground utility management, and
route selection (Yao et al. 2017; and see Sects. 41.5 and 41.6). Although a 3D model
can represent cities as virtual reality to support real 3D analysis, more computing
resources are needed for effective 3D rendering and analysis. Data analytics is an
important component of the big data paradigm. However, it comes after data collec-
tion, deduplication, completion, aggregation, harmonization, contextualization, and
filtering. These components of the process are essential to enable analytics to derive
useful insights. Different types of computing resources are required for different
components in the data process workflow. For example, moving partial computing
resources to the data collection sites for data cleaning can reduce the volume of data
transferred to the core computing platform, result in a lower bandwidth cost and a
higher analysis speed.
41.2.2.3 Data Security and Privacy
Security and privacy issues are two of the major challenges in smart-city computing
due to the identification information within the data and the security issues located
in the multiple computing layers. Generally, some of the raw data may contain
confidential or sensitive information related to people or governments; such data
processing should be protected against unauthorized usage. Taking cellular data for
example, a phone number in each record represents a real person and makes an indi-
vidual’s daily activities traceable, which may divulge the private affairs of people.
In the water distribution management system, a methodology for synthetic house-
hold water consumption was proposed to reproduce water consumption data due to
privacy constraints (Kofinas et al. 2018). Simultaneously, in smart-city applications,
data move over various computing layers through networks, some of which may be
insecure. In an application, data may be processed with more than one computing
technique including edge computing, mobile computing, and cloud computing. In
most cases, mobile devices and edge computing nodes need to connect via Wi-Fi
to upload data to the cloud-computing platform. Connection to unauthorized Wi-Fi
may bring security risks to the system. Besides network connection, distributed open-
source big data platforms like Hadoop and Elastic search are becoming increasingly
popular for distributed data storage and analytics, However, compared to commercial
solutions, these platforms lack sufficient security guarantees (Sharma and Navdeti
2014).
41 Cloud, Edge, and Mobile Computing for Smart Cities 767
41.2.2.4 Efficiency
A trend in smart-city’s applications is to extract information from big data, and
thus, lack of efficiency becomes a bottleneck of most data-analytical applications.
Different applications vary in levels of complexity and require different response
times. Navigation needs immediate optimal route suggestion (e.g., fastest route
option) based on real-time traffic data (Liebig et al. 2017). Predictions of hurricane
intensity help people prepare for severe weather, saving properties, and human lives
(Li et al. 2017). Applications like environmental sustainability are less sensitive to
the response time. Meanwhile, although a series of open-source big data platforms,
such as Apache Hadoop, Spark, HDFS, and MapReduce, have been developed and
adopted in various domains, these platforms are not specifically designed to support
spatiotemporal data. Performance issues are unavoidable when using these platforms
to process spatiotemporal data without any modification. Some research has been
done to customize these tools for domain adoption. Taking array-based raster data
for example, a hierarchical index was proposed to speed up the query process of grid
data stored in the HDFS file system (Hu et al. 2018). The development of an efficient
spatiotemporal computing platform is still in an initial stage; how to utilize and opti-
mize big data computing platforms to implement efficient smart-city applications
remains a challenge.
41.2.3 Generic Computing Architecture for Smart Cities
Cloud, edge, and mobile computing support different functions and applications
in the development of smart cities. To optimize the computation capability and
further overcome the challenges discussed in Sect. 41.3, different types of computing
paradigms should be utilized. Based on the characteristics and advantages of each
type of computing, a computing architecture for a smart-city system is proposed
(Fig. 41.3).
41.2.3.1 General Computing Modules in Smart Cities
The proposed architecture of computing system in smart cities contains the following
five parts:
(1) Application acquisition: The function of the application layer is to collect
requirements from users, then organize, and analyze them into the four aspects as
mentioned in Sect. 41.2.1: Transportation and traffic management, utilities and
energy management, environmental protection and sustainability, and public
and smart-city security.
(2) Visualization: The visualization layer is designed to visualize the applications in
the form of 2D and 3D maps, trajectories, images, charts, histograms, and others
768 Q. Liu et al.
Fig. 41.3 Generic computing architecture for smart cities
using technologies and software such as 2D mapping, 3D modeling, Jupyter,
and Zeppelin.
(3) High-performance analysis and modeling: As discussed in the former sections,
computing for smart cities is usually encountered with big data issues, and
high-performance computing techniques are essential to maintain a stable and
efficient computation system. This layer implements data analysis, modeling,
and prediction according to the applications.
(4) Data access and query: The system utilizes a data access and query layer to
retrieve and select data sources that satisfy the needs and orders from users.
Methods and techniques such as SQL, No-SQL, R-Tree, Quadtree, and spatial–
temporal indexing will be adopted according to the category of data.
(5) Data storage and infrastructure: This layer provides the hardware and physical
devices, including data storage facilities, as well as the servers and networks. The
smart-cities-related data sources will be stored in different categories according
to the requirements from uses, using database systems such as file storage,
Relational Database Management System (RDMS), No-SQL, array-based, and
linked-data databases.
41.2.3.2 Computing Methods Integration
Computing procedures are embedded in all the layers of the proposed computing
architecture for smart cities, through a series of security controls, encryption, stan-
dardization, authentication, authorization, governance, curation, and network tech-
niques. The core computing methods of smart cities contain central cloud computing,
edge computing, and mobile computing. In the central cloud platform, data centers
provide complex analysis and visualization capabilities, as well as hardware facili-
ties and infrastructure for the cloud. The servers are linked with high-speed networks
to provide services for clients. Normally, data centers are built and located in less
populated places, with a high power-supply stability and a low risk of disaster (Dinh
41 Cloud, Edge, and Mobile Computing for Smart Cities 769
et al. 2013). The edge-computing platform is connected with the central cloud by the
Internet. They have dual communication with each other to enable data interactions.
The edge servers can share and reduce the burden of central servers, and as a result
increase the speed of processing and delivering data. The mobile-computing plat-
form is the mobile devices of the end users, which has a certain capability to process
data along with mobility. Mobile devices can also be connected to central clouds by
wireless networks for data transmission. Edge- and mobile-computing platforms are
connected with each other in applications where interactions are needed.
In the architecture, the three computing paradigms are connected and assist each
other, where there are distinctions between them in the collaboration of processing
smart cities’ services and applications. Different from cloud computing requiring
all parts to be connected to the central cloud, where large volumes of data are
processed to find optimization solutions or support decisions, edge computing relo-
cates crucial data processing to the edge of the network, rather than constantly deliv-
ering data back to a central server. Therefore, edge-enabled devices can gather and
process data in real time, allowing them to respond faster and more effectively,
while mobile computing relates to the emergence of new devices and interfaces and
has the data processing capability on the mobile devices. Moreover, the central-
ized cloud could perform extremely complex data processing, storing, and analytics.
Edge computing usually performs less intricate data processing than central clouds,
storing and forwarding. However, some mobile devices can only implement simple
and limited data processing. By integrating the three computing paradigms, the effi-
ciency challenges of intensive big data processing and computing can be remitted.
Direct connection between edges, mobile devices, and the central cloud with a stable
and secure network will guarantee the safety and security of the whole system.
41.3 Cloud Computing for Smart Cities
41.3.1 Methodology
Cloud computing is developed and improved based on the evolution of parallel
computing, distributed computing, and grid computing (Jadeja and Modi 2012;
Yang and Raskin 2009). Parallel computing allows many computation processes
to run simultaneously, which achieves high performance in a divide-and-conquer
fashion (Fu et al. 2015). Distributed computing contains components located on
different networked computers which communicate and cooperate with each other to
achieve a common computing objective (Yang et al. 2008). The inexpensive computer
nodes and high-speed networks make possible the function of distributed computing
systems (Jonas et al. 2017). Grid computing organizes a network of heterogeneous
computer resources to work together and achieves high performance for processing
and executing resource-hungry tasks like those normally allocated to supercomputers
(Wang et al. 2018). Different from the above-mentioned computing modes, cloud
770 Q. Liu et al.
computing is a model for enabling convenient, on-demand network access to a shared
pool of configurable computing resources (NASA 2010), instead of a local machine
or remote server handling applications.
Cloud computing is capable of scheduling and balancing the distribution of
resources according to real utilization demand, and billing according to the usage.
Using different techniques and according to different budgets, cloud computing
extends subscription-based access to data, platforms, infrastructure, and software,
approaches that are referred to as data as a service (DaaS), platform as a service
(PaaS), infrastructure as a service (IaaS), and software as a service (SaaS) (Subashini
and Kavitha 2011; Yang et al. 2011).
41.3.2 Challenges, Motivations and Opportunities
Past research (Gong et al. 2010; Zhang et al. 2010; Yang and Huang 2013; Mahmood
2011) identified the features and advantages of cloud computing as:
(1) Hyperscale. Some Internet companies have developed large-scale cloud-
computing platforms for business applications, and the practical clouds have
a considerable scale. For example, Google cloud computing (Xiong et al. 2017)
has millions of servers; Amazon, IBM, Microsoft, Saleforce, Ali, and Tencent
(Hashem et al 2015; Rittinghouse and Ransome 2016), and other agencies have
hundreds of thousands of servers in their clouds. Conceptually, a cloud can
provide users with unprecedented computing power.
(2) Virtualization. Cloud-computing supports users to access services at any loca-
tion using a variety of terminals and devices. The requested resources come from
the cloud, which uses virtualization techniques to separate computer resources
and services from underlying fixed physical entities (Gong et al. 2010). The
application runs above in the cloud without specifying a server. Simple network
connection enables users to benefit from super-powerful services via multiple
devices, such as a computer, a PAD, or a mobile phone.
(3) Reliability. Cloud computing uses the capability of fault tolerance and isomor-
phic interchangeability of computing nodes and other strategies to ensure high
reliability and availability (Dai et al. 2009). Compared with traditional in-house
computing infrastructures, cloud computing is more reliable and consistent.
(4) Universality. Cloud computing is not specific to any particular applications. It
can support a variety of applications under the support of a single cloud. The
same cloud infrastructure can be shared by different applications at the same
time (Yang et al. 2016).
(5) Scalability. The capabilities and scales of the cloud can be modified and extended
dynamically to meet the needs of applications and growth (Lehrig et al. 2015).
Scalability allows cost-effective running of workloads that make a very high
demand on servers but only for short periods of time or occasionally.
41 Cloud, Edge, and Mobile Computing for Smart Cities 771
(6) On-demand. Users could request and receive access to cloud service offerings,
like the traditional infrastructure utilities of water, electricity, and gas. Based on
a pool of physical and virtual resources in the cloud, operations such as creating,
stopping, and terminating could be conducted at any time without waiting for
delivery and purchasing processes (Etro 2015). Usage monitoring tools of the
cloud can record usage details for billing.
(7) Cost savings. The “pay-as-you-go” characteristic of cloud service enables
personal and business clients to access the cloud from extremely cheap and
price-flexible computing nodes. The automatic system of the cloud reduces
the cost of data center management by deleting the basic maintenance budget.
The lack of physical infrastructure removes the operational expenses of power,
storage, administration and even labor costs.
Considering the advantages listed above, cloud computing can help to address the
following computing challenges of smart cities:
(1) Unity and efficiency. Through the architecture of the IaaS model, cloud
computing integrates various frameworks, hardware brands, and computing
models of servers to the traditional data centers and provides a unified plat-
form of application based on the cloud operating systems (Mitton et al. 2012).
Meanwhile, with the virtualization techniques, cloud computing can be flexibly
and effectively partitioned, allocated, and integrated over a potentially infinite
number of storage and computing resources, and optimize the efficiency ratio
according to application and requirements.
(2) Large-scale infrastructure. Infrastructure management of hardware and soft-
ware is mainly responsible for the monitoring and management of large-scale
foundational computing resources (Jin et al. 2014). Fundamental software
resources include stand-alone operating systems, middleware, databases, and so
on. Fundamental hardware resources include three main devices in the network
environment: computing (server), storage (storage device), and network (switch,
router, and other devices). The advantages of infrastructure management center
are: (1) to manage the assets of the basic software and hardware resources;
(2) to support the status and performance monitoring of the basic hardware;
(3) to trigger alarms for abnormal situations, and remind users to maintain the
abnormal equipment; (4) to carry out long-term statistical analysis of the basic
software and hardware resources; and (5) to provide a decision-making basis
for high-level resource scheduling.
(3) Sustainable and green energy. Facing the burden of large-scale fundamental
software and hardware resources, green and energy-saving operation and main-
tenance management of this basic infrastructure is an inevitable demand for the
supplier of cloud computing (Wibowo et al. 2018).
Presently, users often purchase large amounts of equipment to guarantee peak
business operation demands. But for actual operation processes, the load of the
equipment is generally low (Mastelic and Brandic 2015), especially in the low-
loading period. A long-term low utilization rate will lead to a large waste of
resources and energy.
772 Q. Liu et al.
A cloud-computing data center supports multi-tenant applications of resources.
The utilization rate of resources can be effectively improved through the histor-
ical statistical information of business, and the coordination of business/resource
scheduling management. In typical applications, a cloud-computing data center
using energy-saving technology can increase the load of resources to a signifi-
cantly higher level (Rong et al. 2016), removethe loss in the process of resources’
scheduling, and double the resources’ payload. During night operations, when
the overall load of the data center decreases, the unused resources can be trans-
ferred to the idle mode, to maximize the green, low-carbon and energy-saving
operation of the data center (Hao et al. 2012).
(4) Privacy and security. In the cloud-computing environment, the centralized and
large-scale management of basic resources shifts the security problems to the
server side in the data center. From the specialization perspective, end users
can achieve business security through the security mechanism of the cloud data
center, without consuming too much resources and power (Jin et al. 2014; Sen
2015). At the same time, cloud-computing centers will be directly responsible
for the security of all users and specifically focus on the main security risks
including data access risk, data storage risk, information management risk, data
isolation risk, legal investigation support risk, as well as sustainable development
and migration risk.
The security control of cloud computing can be integrated by the basic hardware
and software security design. The architecture, strategy, authentication, encryption,
and other aspects of a cloud-computing system ensure the information security of
cloud-computing servers.
Cloud computing reduces the risk of data loss or leakage from individuals by
storing data in a centralized database (Chang and Ramachandran 2015). At the same
time, a cloud-computing center also uses a variety of backup methods in security and
disaster recovery to guarantee that data will not be lost or illegally tampered with.
41.3.3 Urban Heat Island Use Case
Remote-sensing data analysis of a large area is a traditional approach to extract
temperature information of cities for UHI modeling and prediction. Google Earth
Engine (GGE) is a cloud-based platform sharing large numbers of satellite data online
and allowing data analysis and processing on the fly (Gorelick et al. 2017).
Chakraborty and Lee (2019) implemented the SUE algorithm on the Google Earth
Engine platform using MODIS images to calculate the UHI intensity for over 9500
urban clusters using over 15 years of data, making this one of the most comprehensive
characterizations of the surface UHI to date. They designed an interactive, public-
facing Web application to query UHI intensities of almost all urban clusters based on
GGE. Ravanelli et al. (2018a,b) took advantage of GGE and the Climate Engine (CE)
tool to process the huge amount of satellite Earth observation data (6000 Landsat
41 Cloud, Edge, and Mobile Computing for Smart Cities 773
images) over the period of 1992–2011 and realized wide spatiotemporal monitoring
of surface UHI and its connection with land cover changes. Yu et al. (2019) utilized
cloud-based computing of spatial and landscape analysis to identify the multi-scale
spatiotemporal patterns and characteristics of regional heat islands.
Cloud-computing techniques enable researchers to calculate geophysical parame-
ters from large numbers of remote-sensing data with high and efficient performance.
The cloud-computing platform, like Google Earth Engine, assists users to store and
manage original raw datasets and provides interactive SaaS for customized algo-
rithms deployment and running for specific UHI-related use cases. These functions
are successful in addressing the computing challenges of big data handling, efficiency,
computing-intensive modeling and processing, and data security.
41.4 Edge Computing for Smart Cities
41.4.1 Methodology
With the development of computation technology and hardware, a large number
of smart devices are integrated with sensors, enabling them to acquire real-time
data and information from the environment. This phenomenon has culminated in the
captivating concept of the IoT in which all smart things, such as smart cars (Morabito
et al. 2018), wearable devices (Chen et al. 2017), sensors and industrial and utility
components (Mehta et al. 2018) are connected via networks and empowered with
data analytics that are significantly changing the way we work, live, and play. In
the past few years, many scientific and industrial organizations have introduced and
implemented the concept of IoT in various fields such as smart homes, smart cities,
smart traffic, and smart environments. Edge computing is a new paradigm in which
extensive computing and storage resources are placed to provide cloud-computing
capabilities at the edge (variously referred to as cloudlets or micro data centers) of the
Internet (Satyanarayanan 2010). Edge computing is a mesh network of micro-data
centers that process or store data locally and push all received data to a centralized
data center or cloud-storage repository (Butler 2017). By implementing computation
closer to the edge of the network, analytics of complex data can be realized in near-
real time. In applications, the forms of edge are various; for example, a gateway at
a smart home is the edge between home devices and the central cloud; a micro-data
center and a cloudlet are the edge between a smartphone and the central cloud.
The main function of edge computing is to ingest, store, filter, and send data
to the central cloud systems (“What Is Edge Computing?|GE Digital” n.d.). At the
heart of a smart city, there is widespread deployment of IoT sensor networks, which
provide a regular flow of data that allows for effective and efficient management of
services and assets. Typical deployment scenarios include a large scope of content:
from bus tracking to traffic light management, street lighting control, air quality, and
pollution monitoring. We envision that edge-computing could have similar impact on
774 Q. Liu et al.
our society as that of cloud computing. Edge computing provides new possibilities in
IoT applications, particularly for those tasks relying on AI techniques such as object
detection (Ananthanarayanan et al. 2017), face recognition (Hu et al. 2016), language
processing (Lewis et al. 2014), and obstacle avoidance (Zhang and Ye 2016).
41.4.2 Challenges, Motivations, and Opportunities
Nowadays, a smart city relies on the infrastructure of edge computing to leverage
most of the up-to-date data-driven technologies. With edge computing, services can
be ensured to flow continuously through local data processing even when the Web
connection is interrupted (Abbas et al. 2017). For example, driverless cars and other
modern IoT devices are designed to be built with enough processing capability, so that
they can perform some of the computation themselves at the edge, without sending it
to the central cloud. Edge-computing technology provides an attractive and resilient
platform for cities, while at the same time reducing backhaul costs (Tran et al. 2017a,
b), both in terms of the amount of data required and the sharing of connections by
creating a mesh network.
There are challenges both in the big data generated and in creating the neces-
sary network infrastructure to support an increasing number of end devices. Edge
computing offers a solution to many of the challenges described in Sect. 41.2.2, which
opens up many possibilities for smart cities. According to the advantages discussed
above, edge computing can contribute to the following computing challenges of
smart cities:
(1) Latency and efficiency. In a high-efficiency computing system, any device
connected to the Internet has to be responsive in a short period of millisec-
onds. Any lag in the communication between network and devices is termed
latency. Edge computing can eliminate the latency issue as it works on the
principle of a more distributed network. This kind of system has the capability
to guarantee real-time information processing and maintains a more reliable
network (Hu et al. 2015). On the other hand, edge-computing processes the
massive data generated by different types of IoT devices at the edge of network,
instead of transmitting them to the centralized cloud infrastructure. Therefore,
edge computing can provide services with faster response and greater quality
in comparison with cloud computing, which greatly improves the efficiency of
collecting, transferring, processing, and analyzing data generated by arrays of
IoT devices.
(2) Privacy and security. Security concerns are more related to the transfer of data
over a network to the central cloud. In an edge architecture, any outage would be
limited to the edge devices and local applications. Therefore, edge computing
will improve privacy and security by omitting the transmission since the data
are stored and processed in or closer to the edge devices (He et al. 2018).
With the improvement of authentication technology, the privacy and security
41 Cloud, Edge, and Mobile Computing for Smart Cities 775
of edge computing can be further guaranteed by the emergence of biometric
authentication such as fingerprint authentication, face authentication, touch-
based, or keystroke-based authentication (Yi et al. 2015; Zhou et al. 2017).
(3) Internet load reduction. According to the Cisco Global Cloud Index (“Cisco
Global Cloud” n.d.), the amount of traffic running through cloud-computing
networks will increase to 14.1 zettabytes per year in 2020. This immersive
amount of traffic can be removed from the central cloud by processing some of
the data closer to the edge. Additionally, moving the processing of data away
from the central cloud can minimize the network burden where the Internet
bandwidth is limited (Lyu et al. 2018).
(4) Sustainability. Edge-computing systems provide the capability of decentralizing
computation power, which support fault tolerance in that when one of the edge
devices fails, other nodes and associated IT assets will still remain operational
(Ning et al. 2019). This concept is similar to the cloud disaster recovery strategy
(“Disaster Recovery Planning Guide|Architectures,” n.d.) by using multiple
available zones and regions to ensure that the data and applications are not
lost in a catastrophic event.
Edge computing introduces a new concept that computing should happen as close
as possible to the data sources. With this architecture, a request could be generated
from the top of the computing paradigm and processed at the edge. By deploying edge
computing, software engineers can create additional applications that utilize edge-
computing platforms to leverage existing technology and benefit the smart cities in
the following ways (“Smarter Cities with Edge Computing” n.d.):
(1) Streetlighting. A number of cities are in the process of upgrading their street-
lights to lower-power LEDs. With the major cost of these upgrades being the
physical fitting, edge appliances can be added to provide lighting controls (Xing
et al. 2018).
(2) Security cameras. Nowadays, CCTV cameras have been a critical tool in modern
policing systems. Edge computing can allow low-cost wireless IP cameras to
be deployed in these systems, which will offer considerably less cost (Yi et al.
2017).
(3) Health emergency and public safety management. For applications that require
real-time prediction and low latency such as health emergencies (Wang et al.
2017) and public safety (Zhang and Ye 2016) management, edge computing is
also an appropriate paradigm since it could save the data transmission time as
well as simplify the network structure. Decisions and diagnosis could be made
and distributed from the edge of the network, which is more efficient compared
with collecting information and making decisions at a central cloud.
(4) Location awareness. For geoinformatics-based applications such as transporta-
tion and utility management, edge computing exceeds cloud computing due to
location awareness (Shi et al. 2016). In edge computing, data could be collected
and processed based on geographic location without being transferred to the
central cloud.
776 Q. Liu et al.
41.4.3 Urban Heat Island Use Case
Unlike cloud computing, edge devices are commonly decentralized. In order to
monitor UHI from distributed sensors, edge computing offers closer contacts to
each individual sensor, thus reducing energy consumption and response time during
the transfer of observation data (Ngoko et al. 2018). Edge devices are those mounted
directly on the edge for urban sensing of properties such as microclimate, having
better durability compared to wireless devices. Densely distributed buildings in urban
areas work as an ideal candidate for the deployment of edge devices, providing close
proximity to the UHI impact factors such as temperature, humidity, and wind speed.
Due to climate change, heating and cooling consume significant energy in buildings.
These sectors contribute greatly to UHI and can be monitored by smart building
sensors (Seitz et al. 2017). Lightweight tasks like data cleaning and basic decision
support can be performed, and therefore contributes to UHI mitigation. Applications
that support edge computing can benefit the field of UHI in: (1) allowing users to
browse and query the UHI of cities around the world from a gateway; (2) providing
a means to access real-time datasets from the edge without any latency; and (3)
allowing users to search for a city of interest, query cities to generate charts of
seasonal and long-term surface UHI, and download the UHI data.
41.5 Mobile Computing for Smart Cities
41.5.1 Methodology
Mobile computing could be described as a form of human–computer interaction
where the computer is portable and transported during normal usage (Qi and Gani
2012; Akherfi et al. 2018). The fundamental concepts of mobile computing include:
(1) communication, (2) hardware, and (3) software. Specifically, the communication
concept refers to the wireless networks, data traffic, and protocols. The hardware
could be any type of mobile device, which includes: (1) laptops, (2) tablets, (3)
smartphones, (4) carputer, and others. The category boundaries of such devices are
blurry, as more and more portable devices are installed with microchips and wireless
modules, all of which have some computing power and the ability to transfer data
through networks as a part of the mobile-computing hardware (Tong et al. 2016).
The software in mobile computing consists of the applications in mobile device
hardware, such as customized industry software, data collection applications, and
Web br o w sers.
In the past decade, mobile computing has developed in two ways (Kumar et al.
2013): (1) deployment of sensors, and (2) growth in smartphones. It was also chal-
lenged by the explosion of big data (Laurila et al. 2012). Different from purpose-
oriented IoT, mobile devices are integrated with multi-purpose sensors, such as GPS
receivers, accelerometers, gyroscopes, and microphones. With the growth in both
41 Cloud, Edge, and Mobile Computing for Smart Cities 777
smartphone technologies and number of users, mobile devices are transitioning from
specialized and customized platforms to powerful computing interfaces (Al-Turjman
2018). Mobile computing itself is also becoming a computing offloading contrib-
utor. The application layer of mobile computing faces various challenges due to its
features. However, with the fast growth in communication technologies, including
4G and 5G networks and high-speed city Wi-Fi (Tran et al. 2017a,b), and mobile
technologies in general, the number of applications running on mobile devices is
growing at an exponential rate.
41.5.2 Challenges, Motivations, and Opportunities
In addition to most computing architectures in a wired network, mobile computing
is different in the following aspects (Qi and Gani 2012): (1) Mobility: mobile-
computing nodes or devices are expected to be portable and transportable; the
computing power is not physically limited to a certain location and follows the prin-
ciple of bringing computing to the data instead of transferring the data to computing
resources. (2) The diversity of network conditions: the networks that mobile devices
use are often not fixed; communication could be achieved through high-bandwidth or
low-bandwidth networks; and the mobile device may even operate offline. (3) Incon-
sistency: as mobile devices are limited by their battery power and wireless network
conditions, the inconsistency of communication and change of working status are
expected and requires the mobile devices to switch modes to adapt to specific situa-
tions. (4) Asymmetric communication: wireless networks are often set with different
bandwidths for downlink and uplink, which causes asymmetric communications
between backend servers and local devices. (5) Low reliability: wireless commu-
nications are susceptible to interference; the security issues are enlarged in such
networks and affect the reliability of mobile computing (Qi and Gani 2012).
The rapid development of mobile computing and smartphone applications is
enabling integrated growth of smart-city applications. As stated in Sect. 41.2.2,
mobile computing can help to improve the following challenges of smart-cities
computing:
(1) Satisfy the need of users from different areas. Mobile computing supports smart-
city computing in the forms of mobility and flexibility, which could help both
end users and policy makers to meet different computing demands in different
scenarios. Application use cases include services in higher education (Gikas
and Grant 2013), and location-based services in general, which all utilize the
mobility side of smart devices and allow them to act as both a data collector and
data user (Raja et al. 2018). Another application of mobile computing is to utilize
and integrate smart devices in smart spaces (Zheng and Ni 2010). The concept
of the smart city is a big domain with enough space for the expansion and adapt-
ability of mobile computing. Research topics including dynamic offloading for
778 Q. Liu et al.
mobile devices (Huang et al. 2012) and mobile cloud computing are all inter-
active examples of smart devices in smart spaces. Mobile cloud computing has
been envisioned since 2009 as a combination of cloud computing and mobile
computing, which leverages the mobility side of mobile computing and inte-
grates with the elastic computing power from cloud computing (Tong et al. 2016;
Dinh et al. 2013; Fernando et al. 2013). When integrated with cloud-computing
power, it could also serve as an edge-computing device in the cloud-computing
network.
(2) Computing efficiency and near-real-time analysis and feedback. Smart device
holders are often fed with various information or data through sensors on the
smart devices; with mobile-based computing power, stream-like data flow could
be analyzed locally and uploaded to the centralized databases at the same time.
End users with smart devices on hand could get feedback or results immediately;
routing and mapping services, language translation services, and instant weather
services are all good examples of this (Talukdar 2010). At the same time, public-
security services and danger-awareness services could also be provided through
mobile computing and locally based services (Aubry et al. 2014), such as the
lost child and healthcare applications discussed in Sect. 41.1.2. The challenges
in smart-city implementations bring new motivations and opportunities for the
development of mobile computing and vice versa.
As one of its important components, mobile computing is enhancing the smart-
city experience in the following aspects: (1) Transport and traffic management for
both personal end users and policy makers; (2) Utilities and energy monitoring across
the network, and (3) Improving public safety and smart-city security awareness.
41.5.3 Urban Heat Island Use Case
Mobile computing and mobile-based technologies are integrating innovative
concepts and ideas to increase UHI awareness and aid city design to reduce the
UHI effect. As Wong et al. (2014) mentioned in their reviews, tools have been
developed and implemented to allow users to gather instantaneous energy perfor-
mance feedbacks on their decisions and plans of building designing, such as the
building orientation and thermal performance, through mobile-based applications
(i.e., iPad/smartphone application). At the same time, mobile devices provide volun-
teered geographic information (VGI) to enhance the near-real-time estimation of
UHI. For example, Koukoutsidis (2018) utilized mobile crowdsensing to estimate
the mean area temperature in a linear region that exhibits the UHI effect.
41 Cloud, Edge, and Mobile Computing for Smart Cities 779
41.6 Case Study
41.6.1 Urban Heat Island (UHI)
The direct cause of UHI is urbanization, which leads to the loss of more vegetation
and causes more surfaces to be paved or covered with impervious materials such as
cement, asphalt, buildings, and walls. Challenges are revealed due to the complexity
of the composition of UHI impact factors. Major ones are stated by Oke (1982)in
his previous studies and include: (1) the inherent complexity of the city-atmosphere
system; (2) the lack of clear conceptual and theoretical frameworks; and (3) the
expense and difficulty of observation in cities. UHI is a very common challenge to
all urban areas in the world, although in megacities it is serious and less so in small
towns.
UHI is usually measured in three scales: boundary UHI, canopy UHI, and surface
UHI. Boundary UHI is measured from the altitude of the rooftop to the atmosphere. It
is generally used to investigate the UHI effect at mesoscale and is acquired by using,
for example, radiosondes. Canopy UHI is measured at the altitude that ranges from
the ground surface to the rooftop. An assessment of canopy UHI is most suitable for
a microscale study and is generally derived based on weather station data. Surface
UHI is measured at the Earth surface level. Researchers have often used satellite
images (e.g., thermal bands of Landsat TM/ETM/OLI, MODIS, AVHRR) to obtain
the effect of surface UHI (Zhang et al. 2009). Researchers used remotely sensed
data and stationary meteorological monitoring data to analyze the UHI changes and
effects in the long or short term (Earl et al. 2016), as well as the relationship between
UHI and land cover changes (Chen et al. 2006; Charkraborty and Lee 2019). A lot
of research has simulated and evaluated UHI and its effect on the future by using
numerical modeling based on real-time meteorological data (Morris et al. 2015).
41.6.2 UHI Challenges and Opportunities
From the aforementioned scientific challenges, UHI introduces its own computing
challenges, mostly concentrated on handling the aspects of the expense and diffi-
culty of observation in cities. These challenges include: (1) management of hetero-
geneous data sources; (2) integration of a huge volume of remotely sensed data and
real-time meteorological data; and (3) a large amount of computation in modeling,
visualizing, simulating, and predicting. Cloud computing has existed in the long
term for allocating computing resources to enable the auto-scalable modeling and
detecting in many study fields and has proved to be an efficient and economical
solution (Yang et al. 2017a). Google Earth Engine is a cloud-computing platform,
offering intrinsically parallel computational resources, and enabling monitoring and
measurement of changes in the Earth’s environment, at planetary scale, on a large
catalog of Earth observation data (Moore and Hansen 2011). An implementation of
780 Q. Liu et al.
large-scale correlation between land surface temperature and land cover alteration
research is conducted upon this platform and has illustrated the capability of using
cloud computing for efficient UHI monitoring (Ravanelli et al. 2018a,b).
The emergence of 5G and IoT technologies in the current era is bringing opportuni-
ties to facilitate advances in urban microclimate study with finer spatiotemporal reso-
lution beyond just satellite imagery analysis (Li et al. 2018). Voogt and Oke (2003)
argued that thermal remote sensors have a credible ability to observe the surface
UHI and require consideration of the intervening atmosphere and surface radiative
properties, leading to extra conversions and corrections. With implementing sensor
device networks directly into the environment, urban environmental factors like air
temperature are more accurately measured. These sensor networks can be designed
and implemented for advanced urban microclimate and environment modeling (Jha
et al. 2015). Challenges follow when considering the real-time streaming nature of
IoT, as it requires the capacity of ingesting the large number of data and producing
results with higher speed that is beyond the capability of conventional architec-
tures (Rathore et al. 2018). Santamouris (2015) analyzed heat island magnitude and
characteristics in one hundred cities and regions and indicated that analysis of 43%
station measurements are only based on one station from urban and one from rural.
According to the Gartner, up to 20.4 billion IoT devices will be connected machine-
to-machine by 2020 (Meulen 2017), offering great potential to increase the number
of sensors utilized for UHI research.
Since the first time it was introduced by Howard (1818), in the past 200 years,
numerous studies have been developed to model UHI intensity, simulate, and
predict UHI effects. However, it was proved from analyzing one hundred Asian
and Australian cities and regions, that a systematic analysis like a workflow is still
needed (Santamouris 2015). Coupling with aforementioned computing techniques
(cloud computing, edge computing, and mobile computing), the following introduces
a theoretical integrated workflow to enable the efficient data storage and processing
for handling urban informatics challenges and using UHI as an example. This work-
flow targets the last two scientific challenges of UHI, and the overall architecture is
illustrated in Fig. 41.4, starting from collecting urban observation data with mobile
devices to the centralized cloud-based data analysis, and finishing with generating
intelligent supportive materials for UHI monitoring and managing.
41.6.3 Integrated Workflow
41.6.3.1 Mobile Computing for Local Fast Response
Data in Fig. 41.4 are directly collected by sensors within a large sensor network
deployed in the urban environment. Data streams into the workflow by entering the
first gate: mobile computing. In general, the capacity of mobile devices is low, and due
to the limitations like battery life, only lightweight preprocessing like data cleaning
and reorganizing can be performed at the mobile computing stage. However, in situ
41 Cloud, Edge, and Mobile Computing for Smart Cities 781
Fig. 41.4 Overall architecture of computing for UHI
monitoring coupled with light data understanding can reduce time latency for jobs
that do not require extensive computation but only the ability to make simple judg-
ments. For instance, alarms setup on a mobile device with constrained temperature
threshold can be triggered responsively when unexpected heat is detected. Though
the computing capabilities of mobile devices are low, with hundreds and thousands
of contributions from them, appreciable computational resources are preserved for
more intensive works like microscale UHI modeling (Mirzaei 2015).
41.6.3.2 Edge Computing for Data Preprocessing and Direct
Microcontrol
Besides collecting data on the edge and passing the raw data to the cloud like mobile
computing, edge computing offers more capacities for better data preprocessing.
With the increasing data volume, uploading everything raw to the cloud can take a
significant amount of time, and the heavy duty that is loaded to the center cluster
can exceed the limit of the computing resources. To fill the gap between mobile
computing and cloud computing, enhancing the performances regarding response
time, data transform, data safety, and privacy, edge computing is integrated to the
workflow to allow downstream data representing cloud services and upstream data
782 Q. Liu et al.
representing IoT services (Sun and Ansari 2016; Shi et al 2016; Yannuzzi et al. 2014).
Similarly, works that do not require much computation can be done directly from the
edge and provide feedbacks to the sensors to reduce time lag (Gerla 2012). Data from
the Array of Things (AoT) (University of Chicago 2019; see Sect. 4.7) project at the
University of Chicago monitors local temperatures and other environmental elements
from networks composed of hundreds of sensors, providing observations with the
resolution of seconds. The high-velocity data transfers within the network can cause
traffic congestion due to the limited bandwidth. The Google cloud platform supports
edge computing with AI, enabling potential real-time data analytics (Google 2019).
41.6.3.3 Cloud Computing for Massive Data Processing and Analytics
Like every big data problem, a sensor dataset at fine temporal resolution for
UHI monitoring (e.g., streaming AoT data) introduces a data storage challenge.
Cloud computing as the final layer of UHI data processing and analyzing has been
well studied for enabling heavy computations by transferring big data storing and
processing from a local to a centralized cluster (Yang et al. 2017b). Empowered with
the auto-expandable nature of the virtual storage mechanism, data streamed from
sensors transfer through edges to the center for better management. With the well-
resourced computing capacity, the cloud cannot only process the data that mobile and
edge devices cannot, but also accelerate the processing beyond a standalone server.
IoT networks are massive and can be distributed with different protocols estab-
lished by different management departments. Therefore, UHI-related attributes like
temperature, humidity and wind speed from different networks are potentially
captured with sensors powered by different standards. Data heterogeneity is one
of the major concerns and the massive data cleaning workload requires significant
computational capability. The cloud as a centralized computing resource pool offers
sufficient capacity for such workload (Botta et al. 2014). As mentioned, there are
many factors contributing to UHI study. Changing the composition leads to require-
ments for model parameter adjustments. SaaS as introduced in Sect. 41.3.1 and
provided with cloud computing allows users to duplicate a model directly from a
current version and customize the new one to fit the new environment. Advantages
include reduced model-building time and decreased human error when transferring
the experimental environment.
41.6.3.4 Mobile-Edge-Cloud Integrated Computing for UHI
A weather forecast example provided by a previous study indicated the basic work-
flow when the simulation is decomposed into a process-oriented pipeline (Tsahalis
et al. 2013). Weather research shares conceptual similarities to UHI, and thus, their
example is applied here as a base version of the conventional workflow. Heusinkveld
et al. (2010) carried out an assessment of UHI intensity in Rotterdam using an inno-
vative mobile bio-meteorological measuring platform mounted on a cargo bicycle.
41 Cloud, Edge, and Mobile Computing for Smart Cities 783
Physiologically equivalent temperatures were calculated directly from the measure-
ments and the intensity of UHI was evaluated in real time. Coupling with the IoT
and mobile devices empowered a real-time urban microclimate analysis framework
that integrated with the sensor network and cloud computing (Rathore et al. 2018);
our workflow gains the experience from both. This enhanced framework composed
of cloud computing, edge computing, and mobile computing is able to success-
fully address the previously introduced UHI challenges. Starting from measuring the
geographic environmental of ground, air, and water, mobile computing can directly
sense these parameters and give a quick response (e.g., a UHI detection alarming
system) with minor data manipulation before entering the major processing and
modeling procedures. Edge computing offers a higher computational capacity, miti-
gating the heavy workload that is initially carried by the centralized module. Building-
scale UHI (i.e., building energy model) is limited to the study of an isolated building,
requiring less computational resources as it considers less neighborhood environ-
mental impacts (Mirzaei 2015). Therefore, UHI modeling, visualizing, simulating,
and predicting for a smaller UHI study scale (i.e., building scale) can be directly
computed on the edge for more efficiency. There are many tasks that cannot be satis-
fied with the limited resources from mobile computing or edge computing, such as
heterogeneous data integration, and larger scale (e.g., microclimate) UHI modeling.
The cloud as a big centralized resources pool is powered with enormous computing
capabilities. UHI-related observation data like temperature, humidity, and wind-
speed are transferred from sensors to the cloud after a certain effort made by mobile
computing and edge computing for data cleaning and preprocessing. Heterogeneous
data integration on the cloud will be triggered for the massive data coupled with
mixed data types and data standards. Large-scale UHI modeling, simulating, etc.,
are performed within the cloud. Elasticity that is offered as one of the key features of
the cloud dispatches computing resources on demand and surpasses the traditional
method of using a single computer for analysis, saving resources while providing
enough capacity for the heavy tasks. All three computing paradigms work seamlessly
from getting the sensor data to processing, analyzing, and decision support, enabling
an efficient and effective workflow as a whole to handle the UHI challenges.
These three computing components should be leveraged and kept in balance
when applied to UHI monitoring, data analysis, and problem solving. For instance,
deploying edge nodes with higher computing capacity may increase the operational
cost for processing the IoT data streams compared to processing them in the central-
ized cloud (Sun and Ansari 2016). Understanding the tradeoffs among the different
interfacings of the three is crucial for maximizing the workflow efficiency and opti-
mizing the computing architecture design. Many other smart-city applications are
encountering similar problems, and the demonstrated UHI analytical workflow can
be broadly applied when integrating computing components.
784 Q. Liu et al.
41.7 Summary
This chapter introduced the contribution and recent advances of computing for smart
cities. The general challenges of computing in smart cities were introduced and
include heterogeneous sources of big data, resulting from the unprecedented number
of smart sensors and devices, various needs from users in multiple domains, data
security, sustainability, and efficiency. To address the challenges, cloud computing,
edge computing, and mobile computing were discussed for their advantages and
limitations in smart-city applications. Cloud computing provides a unified and effi-
cient platform, large-scale base infrastructure, sustainable and green software and
hardware development and addresses system security and recovery issues. Edge
computing helps reduce observation latency and increase the efficiency of data collec-
tion, improve data privacy and security, reduce data transmission load on computer
network, and provide a sustainable decentralization of computing needs. Mobile
computing contributes to the smart city with computational mobility and flexibility,
and computing efficiency and near-real-time analysis. The characteristics of different
computing paradigms were exemplified in the case study of urban heat island. With
multiple computing paradigms leveraged, smart-city applications and services can
be provided in a more efficient and effective fashion.
41.7.1 The Future of Urban Computing for Smart Cities
Big data and IoT are labeled as the primary drivers for the cloud, edge, and mobile
computing. The development of mobile computing is increasing at an accelerating
speed. With the fast implementation of 5G networks and closer integration with
cloud computing, the mobile-computing system is merging with the cloud-computing
network and serving as the network edge. The phrase mobile cloud computing has
been frequently referenced in the mobile-computing field (Fernando et al. 2013;
Akherfi et al. 2018). When the mobility of mobile computing interacts with the elastic
computing power from cloud computing, it will push the whole computing network to
a new decentralized computing stage and accelerate the smart-city process. Smarter
devices, faster networks, and longer battery lives are the foreseeable future; the
transformation of mobile computing and interaction with other computing fields will
be the norm.
With the increasing number of mobile devices (phones, drones, cars, etc.), the need
for interaction with nearby edge resources will become apparent. Coupled with better
processing, computing, and power capacity, as well as the decentralized characteristic
of mobile computing, edge computing is expected to provide significantly improved
throughput, better performance, and real-time responses, moving both computing
and data closer to the user and customizing the processing requirements from each
user. Edge computing and mobile computing are both capable of handling localized
41 Cloud, Edge, and Mobile Computing for Smart Cities 785
data for fast action for a certain range of area size. However, the increasing urban data
volume and cross-city geo-analysis are also driving centralized cloud computing.
Ever since the infrastructure was developed for cloud computing, the combined
use of private and public clouds is engaged for many more individual and busi-
ness purposes. As a mature platform to integrate powerful computing capabili-
ties, large data storage and on-demand data analysis, cloud computing will lead
cities toward a smart age—an age based on fully connected, interactive decision-
supporting environment. Within the smart city, a variety of devices (e.g., domestic
appliances and semiautomatic vehicles) will connect to the cloud-based Internet
for sensing, recording, sharing, and analyzing numerous human-related activities.
Coupled with the help from artificial intelligence algorithms, cloud computing will
serve companies, governments, and individual residents with smarter solutions.
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Qian Liu is a Ph.D. candidate majoring in Geography and
Geoinformation Science (GGS) at George Mason University
(GMU). She serves as a graduate research assistant in the
National Science Foundation (NSF) Spatiotemporal Innovation
Center. Her research mainly focuses on geographical events
detection and segmentation, machine learning applications in
natural phenomena, climate data downscaling, global precipita-
tion climatology analysis, remote sensing, and geographical data
fusion.
Juan Gu is Senior Engineer of Geographical Information
Science at Beijing Institute of Surveying and Mapping. She
is interested in building smart cities using cut-edge geospatial
technologies.
41 Cloud, Edge, and Mobile Computing for Smart Cities 793
Jingchao Yang is a Ph.D. candidate majoring in Geoinforma-
tion Sciences at GMU. He has worked on several NSF and
NASA funded projects as a Research Assistant for the NSF
Spatiotemporal Innovation Center. He is currently applying the
IoT dataset and machine learning algorithms to build a temper-
ature forecasting model in urban areas.
Yun Li is a Ph.D. candidate majoring in Earth Systems and
Geoinformation Sciences (ESGS) at GMU. Her research mainly
focuses on improving geospatial data discovery using machine
learning-based methods, high-performance computing, and
outreaches to spatiotemporal analytics for environmental and
climate data.
Dexuan Sha is a Ph.D. candidate majoring in ESGS at
GMU. He serves as a graduate research assistant in the NSF
Spatiotemporal Innovation Center. His research mainly focuses
on cyberinfrastructure and big data platforms, high spatial
resolution remote sensing, spatiotemporal computing, and
knowledge graphing.
794 Q. Liu et al.
Mengchao Xu obtained his Ph.D. from the GGS Department at
GMU. His research mainly focuses on cloud computing, high-
performance computing networks, spatial database systems,
and precipitation downscaling. He is a GIS data engineer for
autonomous driving systems.
Ishan Shams is a Ph.D. student majoring in ESGS at GMU. His
research mainly focuses on high-performance computing and
spatiotemporal platform visualization.
Manzhu Yu is Assistant Professor of GIScience at the Depart-
ment of Geography, Pennsylvania State University. She received
her bachelor’s degree in Remote Sensing from Wuhan Univer-
sity in 2012 and doctoral degree in Earth System and Geoin-
formation Science from George Mason University in 2017. Her
research focuses on spatiotemporal theories and applications,
atmospheric modeling, environmental analytics, big data and
cloud computing, and the capability to use the above to solve
pressing issues in natural hazards and sustainability.
41 Cloud, Edge, and Mobile Computing for Smart Cities 795
Chaowei Yang is Professor of Geographical Information
Science at George Mason University, where he founded and
directs the Center for Intelligent Spatial Computing and the
NSF Spatiotemporal Innovation Center. He is interested in
analyzing, learning, mining, and identifying spatiotemporal
patterns and principles to enable scientific discovery and
engineering development.
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