=Paper=
{{Paper
|id=Vol-1818/paper1
|storemode=property
|title=Big Data and Context-Aware Computing Applications for Smart Sustainable Cities of the Future
|pdfUrl=https://ceur-ws.org/Vol-1818/paper1.pdf
|volume=Vol-1818
|authors=Simon Elias Bibri,John Krogstie
}}
==Big Data and Context-Aware Computing Applications for Smart Sustainable Cities of the Future==
https://ceur-ws.org/Vol-1818/paper1.pdf
Big Data and Context–Aware Computing Applications
for Smart Sustainable Cities
1
Simon Elias Bibri , John Krogstie2
1
Norwegian University of Science and Technology, Department of Computer and Informa-
tion Science and Department of Urban Design and Planning, Sem Saelands veie 9, NO–7491,
Trondheim, Norway
simoe@idi.ntnu.no
2
Norwegian University of Science and Technology, Department of Computer and Informa-
tion Science, Sem Saelands veie 9, NO–7491, Trondheim, Norway
john.krogstie@idi.ntnu.no
Abstract. Information processing is increasingly embedded in the systems and
processes of the contemporary city to enhance its operations, functions, and
designs. This has been fueled by the new digital transition in ICT enabled by an
integration of various forms of pervasive computing. Driving this transition
predominantly are big data analytics and context–aware computing and their
increasing amalgamation in a number of urban application domains, especially
when such analytics and computing share the same enabling technologies,
namely pervasive sensing devices, computing infrastructures, data processing
platforms, and wireless communication networks. The purpose of this paper is
to outline the key technological components of big data and context–aware
computing, to demonstrate the opportunities and applications computing has to
offer, and to identify the challenges it poses in the context of smart sustain-
able cities. We argue that combining big data analytics and context–aware
computing can be leveraged in the advancement of urban sustainability, as their
effects in this regard reinforce one another as to their efforts for transforming
urban life by employing the data–centric and smart applications and services to
improve, harness, and integrate urban systems as well as facilitate collaboration
among urban domains.
1 Introduction
As complex systems, cities with their systems and domains becoming more intercon-
nected and integrated and their processes being highly dynamic, rely more and more
on sophisticated technologies to realize their potential as to responding to and rising
to the challenge of sustainability. Among these technologies are big data analytics and
context–aware computing, which are rapidly gaining momentum and generating
worldwide attention in the realm of smart sustainable urban development (e.g., [1] [2]
[3] [4] [5]). Big and context data constitute fundamental ingredients for the next wave
of urban analytics and planning. Especially, there is a variety of potential uses of big
data and context–aware computing to address urban sustainability from the source
thanks to deep insights and intelligent decision support through data mining, machine
learning, business intelligence, modeling, simulation, and prediction. This points to
new opportunities and alternative ways to construct and operate future cities.
Smart sustainable cities typically rely on the ICT visions of the new wave of comput-
ing, a combination of various forms of pervasive computing, where everyday objects
and entities communicate with each other and collaborate across heterogeneous envi-
ronments to provide information and services to citizens and other urban stakeholders.
The prevalent forms of pervasive computing are Ubiquitous Computing (UbiComp),
Ambient Intelligence (AmI), the Internet of Things (IoT), and Sentient Computing
(SenComp) (see [6] for a detailed overview). Context−aware behavior and big data
capability are seen to be prerequisites for realizing pervasive computing applications
and systems pertaining to UbiComp, AmI, SenComp, and the IoT (e.g., [5] [7] [8]
[9]).
Context–aware applications and systems hold great potential to enhance urban opera-
tional functioning and services in terms of sustainability, efficiency, and the quality of
life. Indeed, the use of context aware technologies in the urban context involves a
wide variety of urban applications. This entails that the city takes the form of constel-
lations of context–awareness architectures, platforms, and applications across several
spatial scales, connected through wireless networks, which collect, process, analyze,
and reason about real–time data regarding urban life in relation to the underlying op-
erating and organizing systems and processes to generate inferences (situations,
events, locations, environmental states, spatiotemporal settings, etc.) for decision–
making processes. Hence, context–aware computing is a key component of the smart
(and) sustainable cities infrastructures. Having access to context information in smart
(and) sustainable city applications and systems plays a key role in supporting deci-
sion–making processes (e.g., [4] [5]). It is becoming increasingly evident that smart
urban environments based on context–aware technologies will be commonplace in
cities in the near future to support urban living in many ways (e.g., [10] [11]).
The notion of big data and its application in urban analytics has attracted great attention
over the past few years. The big data paradigm is fundamentally changing the way the
city functions, driving decision–making and service delivery within several domains. The
digital data are projected to grow from 2.7 Zettabytes to 35 Zettabytes by the year 2020
[12]. Big data is the next huge step for the urban world. Around the city, an exponentially
growing amount of data continues to be generated beyond imagination and the sheer vol-
ume of information that is already available out there, coming from different urban
domains, is at such high value that it would be astute for urban planners and strate-
gists in collaboration with ICT experts to exploit it for improving urban sustainability.
Indeed, big data provide fascinating opportunities to change how things can be done in
cities in terms of operation, management, and planning from a sustainability perspective.
Unsurprisingly, big data have become a new catchphrase in the ambit of smart sustain-
able cities, and big data analytics as a general area applied to various urban domains
has become of prime focus in the research field thereof. Big data analytics is associat-
ed with a wide variety of intelligent applications and services pertaining to different
dimensions of urban sustainability. Unquestionably, its main strength lies in the high
influence it will have on many facets of smart sustainable cities and their citizens
(e.g., [2][3][4]).
Despite the recent increase of research in big data in relation to city analytics and in
context awareness in relation to interactive applications in urban spaces, the bulk of
work tends to deal largely with applications and services in the realm of smart cities,
leaving important questions involving to what extent and in what ways can big data
and context–aware computing add to the dimension of sustainability in the ambit of
sustainable cities barely explored to date. In addition, a new research wave has started
to focus on how to enhance smart city approaches as well as sustainable city models
by combining these two urban development strategies in an attempt to achieve the
required level of the urban operational functioning, efficiency, and planning in line
with the goals of sustainable development [1]. One implication of this endeavor is that
big data and context–aware computing is being given a prominent role, and the evolv-
ing data–centric holistic approach is seen to hold great potential to address the chal-
lenge of urban sustainability under what is labelled ‘smart sustainable cities.’
2 Conceptual Background
2.1 Smart Sustainable Cities
Smart sustainable city is a new techno–urban phenomenon. So, the term only became
widespread during the mid–2010s [1] as a result of several intertwined global shifts.
The concept has emerged on the basis of five different developments, namely sustain-
able cities, smart cities, ICT, sustainable urban development, sustainability and envi-
ronmental issues, and urbanization and urban growth [13]. The term ‘smart sustain-
able city’, although not always explicitly discussed, is used to denote a city that is
supported by a pervasive presence and massive use of advanced ICT, which, in con-
nection with various urban domains and systems and how these intricately interrelate,
enables cities to become more sustainable and efficient and to provide citizens with a
better quality of life. A smart sustainable city is defined as ‘an innovative city that
uses…ICT and other means to improve quality of life, efficiency of urban operation
and services, and competitiveness, while ensuring that it meets the needs of present
and future generations with respect to economic, social, and environmental
aspects.’ [14].
2.2 Big Data Analytics/Computing
‘Big data’ is an evolving term that is used to describe the growth, proliferation,
diversification, heterogeneity, complexity, availability, and utilization of datasets,
which existing data processing applications and platforms are inadequate to handle.
This implies that big data encompass the use of tools and techniques that work
beyond the limits of normal data analytics methods involving storage, capture,
retrieval, integration, processing, analysis, and visualization for timely and accurate
decision–making purposes. There are many definitions of big data, and they tend to
offer different, yet related, perspectives of the concept, attempting to capture various
strands for complementary purposes. Big data is defined as ‘a high–volume, high–
velocity and high–variety information assets that demand cost–effective, innovative
forms of information processing for enhanced insight and decision–making.’ [2]
While there is still a lack of agreement about the definition of big data, there seems to
be consensus that big data will lead, with projected advancements, to immense and
fascinating opportunities in the coming years.
Big data can be characterized by what has come to be known as Vs. The main of
which—identified as the most agreed upon Vs—include volume, variety, and velocity
[15]. Additional Vs include veracity, validity, value, and volatility. The emphasis in
this paper is on the main characteristics of big data: the huge amount of data, the
velocity at which the data can be processed, and the wide variety of data types.
• Volume denotes the amount of the data generated from a variety of sources that are
to be analyzed, amounting to terabytes, petabytes, exabytes, and zettabytes of data.
• Variety covers the diversity of data types, such as relational tables and databases,
social posts, mobile records, video, audio, images, and web content, i.e., variety of
structured, semistructured, and unstructured data.
• Velocity signifies the speed or pace at which the data flow from various sources
need to be created, processed, and analyzed. Here, time–sensitivity is of critical
importance. In this regard, the data can be real–time, near real–time, periodic,
streams, or batch.
In the urban context, the term ‘big data analytics’ refers to a collection of sophisticat-
ed and dedicated software applications run by powerful machines, which can turn
large amount of urban data into intelligence for decision–making processes in relation
to various urban domains. Generally, big data analytics relies on a combination of
several technologies, including data mining, business intelligence, and statistical ap-
plications [16]. The analysis category that is most used in the urban domain is data
mining. In this context, big data analytics targets optimization and intelligent decision
support for control, management, and planning purposes through the implementation
of optimization strategies and decision–taking processes in relation to a wide variety
of urban application domains, such as transport, traffic, environment, energy, land use,
health, and education.
2.3 Context–Aware Computing
As a fundamental aspect of everyday life, context shapes, influences, and changes the
patterns underlying the interaction of all kinds of intelligent entities, e.g. humans,
computers, and engineered systems, with their environment. Context–aware comput-
ing has been researched extensively by the HCI community since the late 1990s (e.g.,
[17] [18] [19]). As a prerequisite for realizing the various ICT visions of pervasive
computing, it aims to ‘support human action, interaction, and communication in vari-
ous ways wherever and whenever needed’ [1] by enabling computationally augment-
ed environments to provide the most efficient services by relying on contextual in-
formation and thus anticipating and responding intelligently to diverse needs accord-
ingly in a seamless and unobtrusive way. This applies particularly to new emerging
faces of cities, namely ambient, sentient, and ubiquitous cities as well as cities as an
Internet of everything.
The concept of context aware computing has been expanded beyond the ambit of HCI
applications to include urban applications, such as energy systems, transport systems,
communication systems, traffic systems, power grid systems, healthcare systems, ed-
ucation systems, security systems, and so on [2]. Here the focus is on the physical,
environmental, spatiotemporal, and socioeconomic aspects of the urban context.
Therefore, the concept of context–aware computing is associated with the operational
functioning of the city and the wellbeing of its citizens, that is, sustainability, efficien-
cy and the quality of life. Here the context denotes, drawing on [20], the environmen-
tal states and settings within the urban landscape that determine systems or ap-
plications’ behavior. And the contextual variables provided by the smart sustainable
cities infrastructures are understood as a means to understand the living environment
of citizens anywhere at any time in the city. Hence, by properly using all kinds of
contextual information about diverse urban variables, citizens can benefit from ap-
plications and services that automatically adapt to discovered context by changing
their behavior accordingly.
2.4 Context Awareness in the Urban Domain
As a form of computation performed to generate inferences from context data for de-
cision–making, context awareness is a central issue to ICT of the new wave of com-
puting in the sense that it is given a prominent role in the notion of intelligence in
relation to diverse application domains. In the urban context, the notion of intelli-
gence entails that the urban environment recognizes situations, events, states, settings,
and behaviors and intelligently reacts to them, anticipating and responding to different
needs in urban spaces. To establish context awareness functionality requires to collect,
aggregate, fuse, process, analyze, interpret, and propagate contextual information to
support decision–making needs and to control over processes and provide services.
This employs a wide variety of technologies, including sensor networks, machine
learning methods, hybrid modeling and reasoning techniques, intelligent agents, cloud
computing platforms, wireless communication networks, and middleware infrastruc-
tures [8].
Context awareness has been defined in multiple ways, depending on the application
domain in terms of the number and nature of the subsets of the context of an entity
(e.g. transport system, traffic system, energy system, information system, etc.) that
can be integrated (sensed, conceptualized, and modeled) in the design and develop-
ment of a given application. Originated in pervasive computing the term ‘context
awareness’ is used to describe technologies that are ‘able to sense, recognize, and
react to contextual variables, that is, to determine the actual context of their use and
adapt their functionality accordingly or respond appropriately to features of that con-
text.’ [8]. In more detail, as a sensor–based perception, context awareness refers to an
application–specific trait that indicates that computer systems (HCI and urban ap-
plications) are able to acquire information about the different aspects of the relevant
context (physical, environmental, spatiotemporal, socioeconomic, behavioral, etc.) for
further processing, analysis, and interpretation to generate high–level abstractions of
context using machine learning and reasoning or hybrid modeling approaches to sup-
port intelligent decision–making and action–taking processes with respect to the effi-
ciency, management, and improvement of urban systems.
3 Big Data and Context–Aware Computing Components for Smart
Sustainable Cities
Like other application areas to which big data and context–aware computing is ap-
plied, smart sustainable cities require big data and context–aware components to be
put in place across several spatial scales in the form of advanced enabling technolo-
gies necessary for designing, developing, and deploying the diverse applications and
services that support urban sustainability. The technological components of big data
analytics and context–aware computing required for smart sustainable cities encom-
pass data processing platforms, pervasive sensing, wireless communication technolo-
gies and smart network infrastructures, advanced algorithms and techniques, privacy
and security mechanisms, open standards and standard rules (see [2] [4] for a detailed
account). For an overview of essential underpinning computational and conceptual tools
for understanding how context–aware computing operates in relation to urban oper-
ational functioning for improving performance and efficiency as well as in relation
to urban service delivery for enhancing the quality of life in the ambit of smart sus-
tainable cities, the reader is directed to [2]. Among the key themes addressed in
their work include context data processing and modeling, sensor observations and
dynamic urban models, a framework integrating key dynamic models and analytic
methods, the process of recognizing situations of urban life, and a basic multilayered
architecture of context data processing.
4 Big Data and Context–Aware Computing Opportunities and
Applications
The application of big data analytics and context–aware computing in smart sustain-
able cities offers clear prospects for achieving enhanced levels of sustainability in
terms of efficiency, planning, and the quality of life. One of the core ideas underlying
the use of such advanced technologies is to integrate solutions and facilitate collabo-
ration across applications and services to have better and effective utilization and
management of resources and infrastructure, more astutely strategic development, and
more efficient and faster urban services. Accordingly, combining big data and con-
text–aware applications has the potential to serve many urban domains and systems
and how they interrelate in the context of sustainability.
Irrespective of what connotation smart sustainable cities can be based on, whether be
it ambient, sentient, ubiquitous, or an Internet of everything, or a combination of
some or all of these urban constructs, such cities are said to denote urban spaces
loaded with clouds of data intended to shape the operational functioning and the expe-
rience of citizens of the city. Here, both context–aware computing and big data ana-
lytics are given a pivotal role, as all over the cities, big data and context–aware ap-
plications can monitor what is happening in urban environments, analyze, interpret,
and react to them at varying ways and across several spatial scales. In contrast to the
prevailing notion of smart cities of the future [3], which can ‘be understood as a col-
lection of plural research traditions, performed and commissioned by divergent actors
all with their own motivation and implicit understanding of what a city is or should
be’ [10], the main motivation behind the concept and development of smart sustain-
able cities, based on big data and context–aware applications, is to advance urban
sustainability by improving the contribution of cities to the goals of sustainable de-
velopment. Indeed, being varied, the objectives of big data, in particular, are more in
conjunction with the goals of big data urban stakeholders.
The prospect of smart and sustainable cities getting smarter is fast becoming the new
reality [2], and this is opening up new opportunities for increasing their contribution
to sustainability. This has been enabled by the recent advancements in several scien-
tific and technological areas within computing, notably context awareness, multi–sen-
sor data fusion, hybrid modeling and reasoning, machine learning, cloud computing
models, and wireless and mobile networks, and, more recently, big data analytics.
Subsequently, significant opportunities exist for UbiComp, AmI, the IoT, and Sen-
Comp in relation to modernizing the urban model in terms of different dimensions of
sustainability. Indeed, the range of urban applications that utilize these new technolo-
gies in connection with sustainability is potentially huge, as these technologies—
combined through what has been identified as big data and context–aware comput-
ing—usher in automation in nearly all urban domains. Efforts emanating from these
technological fields transform and influence every aspect of urban life [11]. Accord-
ingly, in view of their synergy and integration in terms of their functioning, UbiComp
and AmI application areas include healthcare and social support, public services,
learning and tele–working within the networked home, social groupings and commu-
nity building, social inclusion, public safety, energy efficiency (smart buildings, smart
transport, smart grid, smart planning, etc.), environmental monitoring, disaster man-
agement, transport and mobility, water and waste management, planning and design,
and large–scale deployments in relation to smart cities. Likewise, the IoT application
areas encompass environmental monitoring and protection (air and water quality and
atmospheric conditions), urban infrastructure monitoring and management, waste
management, energy management, medical and health systems, public safety, envi-
ronment and disaster, building automation, natural ecosystems, transportation, and
large–scale deployments in relation to smart cities. Overlapping with AmI, SenComp
application areas involve transportation, safety and environmental impact, traffic and
street light control systems, energy conservation, waste management, measuring and
surveying buildings, civil security, and social and public services. Therefore, there is a
lot to achieve with the deployment and implementation of these advanced solutions
offered by ICT of the new wave of computing—if its potential is well focused on ur-
ban sustainability in terms of exploiting the benefits of big data and context–aware
computing in the ambit of smart sustainable cities. Context awareness and big data
technologies and their applications and uses play a significant role in realizing the key
aspects of the improvement of the contribution of smart and sustainable cities to sus-
tainability. The link between urban sustainability and big data and context–aware
computing provides insights into understanding how urban ICT of the new wave of
computing can add a whole new dimension to existing smart cities and sustainable
cities.
Furthermore, the demarcation lines between UbiComp, AmI, the IoT, and SenComp
pertain in large part to the kinds of applications they offer in relation to the urban sus-
tainability domain, rather than to the core enabling technologies they normally share
in terms of sensing, computing, and networking infrastructures. The underlying dis-
tinctions include the way in which the respective applications are used in connection
with different urban domains, namely relying on context–aware computing, big data
analytics, or a combination of these technologies. Regardless, combing these ap-
plications provide fascinating opportunities to advance smart sustainable cities in
terms of the processes that operate and organize urban life: physical and spatial struc-
tures, infrastructure, administration, ecosystem services, and public services as re-
gards to achieving the goals of sustainable development. Hence, the idea of coupling,
integrating, and coordinating UbiComp, AmI, the IoT, and SenComp technologies and
their novel applications is invaluable, if not necessary, at the level of (big data and
context–aware) applications with regards to the advancement of urban sustainability.
Figure 1 shows the employment of the core enabling technologies of ICT of the new
wave of computing to connect urban domains and systems to related applications and
services in smart sustainable cities, using big data and context–aware computing. Ur-
ban domains and systems generate large amount of data, datasets and contextual data,
as input for big data and context–aware applications, respectively, which in turn gen-
erate deep insights and inferences for decision–making processes to improve urban
operational functioning, planning, and the quality of life in line with sustainability.
Put differently, through pervasive sensing, computing, and networking infrastructures,
big data and context–aware applications collect, store, manage, process, analyze, and
model data pertaining to various urban domains and systems to discover new knowl-
edge and generate context knowledge, respectively, to help decision–makers to im-
prove urban performance and planning in relation to resources, infrastructures, net-
works, facilities, and services and their interrelationships in the context of sustainabil-
ity.
For example, smart transport is associated with all ICT applications of the new wave
of computing. That is, UbiComp, AmI, the IoT, and SenComp aim at improving all
forms of transportation and mobility in cities. The IoT devices can assist in intercon-
necting various aspects of transportation systems (vehicles, infrastructure, drivers,
roads, etc.) in terms of integrating communication, management, control, and infor-
mation processing units across these systems, which results in smart traffic control,
road assistance and safety, smart parking, logistic and fleet management, vehicle con-
trol, and toll collection systems [2]. AmI and UbiComp can substantially mitigate the
negative impacts of transport on the environment triggered by saturated transport
networks, endemic congestion, urban density, and community expansion by providing
advanced forms of virtual mobility and thus curbing demand for actual one. AmI can
moreover detect traffic patterns by analyzing real–time data, predicting traffic condi-
tions, adjusting traffic controls to mitigate road congestion as well as reduce road ac-
cidents by opening new roads, enhancing transport infrastructure, and directing vehi-
cles to alternative roads [4]. In addition, it provides advanced location–based services
related to on–board navigation systems (using GPS) that allow, by means of multi–
media presentations wirelessly transmitted and displayed on different kinds of de-
vices, effective use of existing infrastructure and thus reduce energy consumption ad
thus emissions by helping drivers to select cost– and time–efficient driving routes.
Also, using GPS, information can be gathered and predictions be made regarding pol-
lution density to generate localized air quality alerts. Furthermore, SenComp, AmI,
and UbiComp can, at varying degrees, provide advantages associated with smart traf-
fic lights and signals in terms of controlling the traffic flow in cities. This enhances
their traffic patterns and transport systems, improves commuting, and handles high
volume of traffic congestion by measuring different parameters of the traffic flow (the
positions and speeds of cars, traffic speed and density, traffic conditions or jams, wait-
ing time at the lights, etc.) using different types of sensors (GPS, loop sensors, remote
sensors, etc.). Big data analytics enables ‘data streaming to process and communicate
traffic information collected through sensors, smart traffic lights, and on–vehicle de-
vices to drivers via smartphones or other communication devices.’ [4].
5 Challenges to Big Data and Context–Aware Computing
There are numerous challenges facing big data and context–aware computing that
need to be addressed and overcome. These challenges concern the design, develop-
ment, deployment, and implementation of related applications on a city–wide scale,
from heterogeneous hardware components through to service delivery systems and
application actions and from data through to mining, modeling, intelligence, simula-
tion, and prediction with respect to the operational functioning and planning of smart
sustainable cities. They are technological, computational, and analytical in nature,
including data management, analysis, and mining; data(base) integration and net-
worked–based coupling; urban simulation models and urban intelligence functions;
privacy and security; and data proliferation, sharing, and quality (see [2] for a detailed
account), in addition to organizational, financial, institutional, and regulatory chal-
lenges [4] [3]. By understanding, exploiting, and extending the available computa-
tional and analytical capabilities of, or advancing knowledge on, big data and con-
text–aware computing in terms of conceptions, techniques, methods, principles, para-
digms, and risks, great opportunities can be realized in terms of both improving and
integrating urban systems as well as facilitating collaboration among urban do-
mains through data–centric and intelligent applications and services in the context
of smart sustainable cities. This entails ensuring that the open issues stemming from
those challenges are under investigation and scrutiny by the socio–technical entities
within the technological innovation system of big data and context–aware computing,
namely industry consortia, business communities, research institutes, universities,
policy makers and networks, and governmental agencies.
6 Conclusion
Big data analytics and context–aware computing are rapidly growing areas of ICT
that are becoming ever more important to smart sustainable cities. Specifically, big
data and context–aware computing and its uses will play a significant role in realizing
the key characteristics of smart sustainable cities, namely operation and service effi-
ciency, life quality enhancement, and intelligent management of natural resources and
infrastructures. Indeed, huge expectations for gains are being placed on the ongoing
research within big data analytics and context–aware computing. This is justified by
the opportunities enabled by their amalgamation with regard to understanding, moni-
toring, analyzing, and planning smart sustainable cities to improve their contribution
to sustainability. Worth noting is that their effects in this regard reinforce one another
as to their efforts for transforming urban life by employing the data–centric and smart
applications and services to improve, harness, and integrate urban systems as well as
facilitate collaboration among urban domains.
Big data and context–aware applications have the potential to serve a wide variety of
urban domains and add value to how their diverse components interrelate with respect
to operation, management, and planning. Among the key smart applications and ser-
vices enabled by big data and context–aware computing include smart transport,
smart energy, smart environment, smart planning, smart design, smart grid, smart traf-
fic, smart buildings, smart education, smart healthcare, and smart safety. Many cities
in ecologically and technologically advanced nations have already started integrating
big data and context–aware computing, actively engaging in smart sustainable initia-
tives and projects in the hopes of reaping sustainability benefits by developing and
implementing data–centric and smart applications across urban environments.
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