Validation and Extension of the Smart City Ontology

Petr

anek and Mouzhi Ge

Faculty of Informatics, Masaryk University, Botanicka 68a, 602 00 Brno, Czech Republic

Keywords:

Smart City Ontology, Smart City Deﬁnition, Concept Validation.

Abstract:

Over the last decade, the concept of the Smart City has been extensively studied with the development of

modern societies. However, due to the complexity of Smart City, there does not exist a widely accepted

deﬁnition for the Smart City. More recently, Ramaprasad et al. in 2017 have proposed a Smart City ontology

that connects its relevant concepts with speciﬁed relations. This ontology thus can offer various paths by which

theory and practice contribute to the development and understanding of a Smart City. However, this ontology

is still lacking practical validations to verify its applicability, Therefore, in this paper, we select a set of critical

Smart City papers and validate this ontology by ﬁtting the papers into this ontology. Based on the validations,

we also further propose and discuss the possible extensions and consolidations for this Smart City Ontology.

1 INTRODUCTION

With the rapid development of urbanization, there

have been a variety of challenges that are proposed

to meet the demand of modern societies to ensure the

quality of life, sustainability, and economic growth.

The works that tend to address these challenges with

new technologies are usually associated with the term

Smart City. This term was initially developed in the

1990s, when there appeared different needs from the

cities e.g. to encompass sustainability by avoiding en-

vironmental pollution, efﬁcient use of energy and ma-

terials, and life cycle engineering (Hall et al., 2000).

Different smart services have been developed to of-

fer a better satisfaction for stakeholders’ needs. Thus,

Smart City can be explained in terms of a complex of

services exchanged by a network of actors intercon-

nected in order to share knowledge, resources, com-

petencies, and capabilities to perform better solution.

Even though there has been an extensive Smart

City research over the last decade, there does not

exist a shared deﬁnition of the Smart City concept.

For example, some deﬁnitions are focused on citizens

and stakeholders, ”A Smart City is a city well per-

forming in a forward-looking way in 6 characteristics

built on the smart combination of endowments and

activities of self-decisive, independent and aware ci-

tizens.” (Gifﬁnger et al., 2007), other deﬁnitions have

a technical focus such as “Smart City is deﬁned by

IBM as the use of information and communication

technology to sense, analyze and integrate the key in-

formation of core systems in running cities.” (Coc-

chia, 2014), or to promote certain speciﬁc technolo-

gies such as the Internet of Things (IoT) – “Smart City

is the product of Digital City combined with the In-

ternet of Things.” (Su et al., 2011). As far as we have

reviewed, those Smart City deﬁnitions are not con-

tradictory, rather complementary with each other. As

Cocchia (Cocchia, 2014) claimed, the reason is that

deﬁnitions were derived from real Smart City pro-

jects that were driven by technological innovation and

its application to speciﬁc urban areas. Since the pro-

jects’ environment was different, the process of Smart

City implementation varied. There are therefore many

deﬁnitions of Smart Cities using different division of

city domains and city services.

In order to unify different Smart City Concepts,

Ramaprasad et al. in 2017 (Ramaprasad et al., 2017)

have proposed a Smart City ontology that connects

different sets of Smart City concepts with speciﬁc re-

lations, which thus can offer a roadmap to go through

the Smart City designs and assess the Smart City

services from different perspectives. We also con-

sider this ontology can be a better way to organize

the Smart City concepts rather than just a single de-

ﬁnition. However, although this ontology follows a

proper design, it is not yet validated with Smart City

works. We, therefore, consider that it is valuable to

conduct further validation to assess the applicability

of this ontology.

This paper, therefore, aims to validate the Smart

City ontology by ﬁtting different Smart City papers

into this ontology. The contribution of this paper is

406

Št

epánek, P. and Ge, M.

Validation and Extension of the Smart City Ontology.

DOI: 10.5220/0006818304060413

In Proceedings of the 20th International Conference on Enterprise Information Systems (ICEIS 2018), pages 406-413

ISBN: 978-989-758-298-1

Figure 1: Smart City ontology redrawn to a mind map.

two-fold: (1) we have conducted a practical valida-

tion for Smart City ontology by verifying whether dif-

ferent papers follow respective routes along the onto-

logy, (2) we have further developed and extended the

ontology based on elements used in papers and mis-

sing in the ontology, elements having alternatives in

papers and the ontology and ﬁnally in papers not used

elements.

The remainder of the paper is organized as fol-

lows. Section 2 revisits the Smart City ontology and

summarizes the ontology into Figure 1. Based on the

ontology, section 3 uses a set of Smart City papers to

validate the ontology in practice. From the validation,

section 4 discusses the results and possible extensions

of the Smart City ontology. Finally, section 5 conclu-

des the paper and outlines the future work.

2 SMART CITY ONTOLOGY

There can be found more than 36 distinct deﬁnitions

of the Smart City concept in a current scientiﬁc li-

terature that make this research ﬁeld ambiguous. The

core of the problem lies mainly in the word Smart that

can be perceived in many ways. At the beginning of

the Smart City ﬁeld, the word Smart meant to use an

IT anywhere in a city. Lately, the focus of the term

moved from using IT to city goals. It is connected to

the realization that a city is a living organism and it

needs to fulﬁll speciﬁc goals for the city to preserve

its existence. Then, city functions were identiﬁed for

a Smart City evaluation in the last few years.

In (Ramaprasad et al., 2017), the authors studied

Smart City deﬁnitions and other relevant scientiﬁc

studies from different ﬁelds with the intention to unify

a view of the Smart City concept. They came up with

a Smart City Ontology (see Figure 1) that integrates

36 Smart City deﬁnitions and other Smart City publi-

cations.

This ontology deﬁnes Smart City concept as a

function of two main parameters, which are Smart and

City. For each parameter, there is a function that ex-

plains the dimensions of the parameter. The concept

Smart contains Structure, Function, Focus, and Semi-

otics. The City consists of Stakeholders and Outco-

mes.

For a deeper understanding of the ontology, every

dimension from Smart and City functions is deﬁned

as a set of components (or classes). The class of

Structure is constructed by Architecture, Infrastruc-

ture, Systems, Services, Policies, Processes, and Per-

sonnel elements. The Function class contains Sense,

Monitor, Process, Translate and Communicate ele-

ments. The Focus dimension contains Cultural, Eco-

nomic, Demographic, Environmental, Political, So-

cial, Technological and Infrastructural elements. For

Semiotics, there are Data, Information, and Know-

ledge elements. For the two classes in the City, Sta-

keholders include Citizens, Professionals, Commu-

nities, Institutions, Business, and Governments ele-

ments, while Outcomes are constructed by Sustaina-

bility, Quality of Life, Equity, Livability, and Resi-

lience elements. The presented ontology is more a

proposal than a uniﬁed deﬁnition. As authors them-

selves stated, it can be expanded or reduced depen-

Validation and Extension of the Smart City Ontology

407

ding on a context of its use. There are different pos-

sible applications for the ontology such as assessing

the level of smartness of their cities from many per-

spectives at different levels of complexity, providing

a roadmap for new smart city designs, and guiding

cooperative thinking among government agencies and

other stakeholders.

2.1 Alignment of Smart City Ontology

In order to further explain the Smart City ontology,

we have aligned three typical papers to this ontology,

indicating which paper follows which path according

their contexts. For example, (Babar and Arif, 2017)

proposes and tests a generic Smart City architecture

which aims at efﬁcient planning and decision ma-

king for Smart Cities. Its intended parameters are in-

clusivity and elasticity and it is built upon IoT and

Big Data analytics. The architecture consists of three

main parts each with its own purpose. The essen-

tial part takes care of data acquisition & aggregation

using mainly IoT elements. The second part of the ar-

chitecture pre-processes data, which means it checks

the validity, normalizes it, scales it and ﬁlters useful

data that are stored. The last part of the architec-

ture uses the pre-processed data for making intelligent

decisions and communicating events to citizens. We

can translate the idea of the paper into the following

function aligned with the Smart City ontology:

(BabarandAri f , 2017) =

f ( f (architecture + (monitor, process,

translate, communicate) + urban + data)

+ f (citizens + quality O f Li f e))

(1)

The paper (Uribe-Perez and Pous, 2017) propo-

ses a solution to problems of communication within

the Smart City. More speciﬁcally, the paper says that

the ICT plays a key role in every Smart City and this

ICT needs to be efﬁcient and valuable for the city. A

city using ICT doesn’t need to be smart. For the ICT

to be more efﬁcient and valuable for the city, the in-

terconnection and mutual communication are one of

the parameters. Therefore, authors of the paper pro-

pose a communication architecture inspired by a hu-

man nervous system. The architecture is composed of

(1) a sensing layer containing a sensor network, (2) an

access layer with Smart GateWays to process a low-

level information and act consequently, (3) data layer

with 3 types of databases to store data, (4) a platform

layer to supervise and manage the city and the last,

(5) application layer to provide services. This pro-

posed communication architecture should be dynamic

and scalable with ensuring a fast response when a not

complex event appears. Let us transform the idea of

the paper to ﬁt in the ontology function:

(Uribe − PerezandPous, 2017) =

( f (architecture + (sense, monitor,

translate, communicate) + urban

+ (data, in f ormation))

+ f (stakeholders + resilience))

(2)

Another paper (Chen et al., 2016) describes an au-

tomotive sensing platform used in the city to obtain

data from different parts of the city by cars equipped

with sensors. In general, an automotive sensing has

two main advantages. The sensors can be powered by

a car battery. It solves energetic constraints that are

usually present. The second advantage is a size and

a weight of the sensors which is not that restrictive

as for instance in case of mobile phone sensors. The

study describes a sensing platform built upon garbage

collecting trucks equipped with sensors to sense data

and send it to servers where can be converted to the

required format and processed further. In the words

of ontology function:

(Chenet al., 2016) =

f ( f (plat f orm + (monitor, process,

communicate) + data))

(3)

3 VALIDATION OF THE SMART

CITY ONTOLOGY

In order to validate the Smart City ontology, we have

selected several Smart City papers by searching aca-

demic databases and well-known publishers such as

ScienceDirect, Scopus, as well as general Google se-

arch with keywords like Smart City or Smart Service.

We limited the search to up-to-date papers from 2010

to 2017. The search resulted in papers that contain a

process that describes how the services and solutions

in Smart City are designed for a certain purpose and

we gave a special preference on the papers with hig-

her citations. Therefore, we selected 22 Smart City

papers. Based on the selected papers, we validated

the Smart City ontology by matching the paper to dif-

ferent dimensions of the ontology.

At ﬁrst, we introduce a table divided into 3 ﬁgu-

res (see Figures 2, 3, 4) containing the data from se-

lected papers ﬁtting the ontology. Figure 2 and Fi-

gure 3 show information about the appearance of ele-

ments from the ontology smart dimension. As it can

ICEIS 2018 - 20th International Conference on Enterprise Information Systems

408

Figure 2: A table showing occurrence of ontology smart terms (structure, function) in studied papers.

Figure 3: A table showing occurrence of ontology city terms (focus, semiotics) in studied papers.

be seen, the papers contain nearly every part of the

ontology glossary. The interesting ﬁnding is that the

papers do not contain infrastructural focus. The word

infrastructure appears a lot, but always in a context of

a structure. Less used is also focus on politics and de-

mographics and the translate function. On the other

hand, data and information are mentioned in nearly

every paper.

The third ﬁgure, Figure 4, contains more white

places than the previous one. It means that when pa-

pers speak about stakeholders, they, in most cases,

mention citizens and governments. There is proba-

bly a less focus on other city stakeholders in this re-

search ﬁeld. Regarding the outcomes, they are men-

tioned quite often, but usually in a sense that Smart

City should aim at their fulﬁllment. In a very small

number of papers were outcomes directly connected

to the main idea of the papers.

In the following section, we intend to position the

papers into the ontology, which aims to validate this

ontology by checking whether the relevant Smart City

papers can be classiﬁed by the ontology, if not, what

Validation and Extension of the Smart City Ontology

409

Figure 4: A table showing occurrence of ontology city terms in studied papers.

kind of extensions can be made to improve the onto-

logy. For example, in the following validations, some

terms might be out of the glossary of the Smart City

ontology, they can be thus considered as the extensi-

ons to the ontology glossary. Therefore, the selected

Smart City papers are used to test the validity of this

ontology rather than using the ontology to classify the

selected papers.

Table 1: Smart City Ontology validation by selected papers.

Paper

Ontology validation

(Uribe-Perez

and Pous,

2017)

The communication architecture

to sense, monitor, translate and

communicate the urban data and

information to stakeholders for

a resilience.

(Masutani,

2015)

A route control method to

enhance sensing coverage of

a crowdsensing system.

(Bifulco et al.,

2017)

Living Labs to develop services

innovation. Living Labs to

enhance citizens’ engagement in

urban life.

(Blaschke

et al., 2011)

OGC framework to integrate

remote sensing and sensor webs

for decision makers to have

better urban knowledge.

table continues

continued table

Paper

Ontology validation

(Nam and

Pardo, 2011)

Strategic principles to strengthen

human infrastructure and

governance for institutional

improvement and citizen

engagement.

(Chen et al.,

2016)

A platform to monitor, process

and communicate data.

(Adame et al.,

2016)

An IoT hybrid monitoring system

to provide location, status, and to

track patients and assets for

improving healthcare

environments.

(Cardone et al.,

2013)

A crowd-sensing platform to

proﬁle users A crowd-sensing

platform to autonomously choose

people for deciding who to

involve inparticipation and to

quantify the performance of their

sensing. A crowd-sensing

platform to collect sensing data

from smartphones for delivering

sensing/actuation tasks to users.

table continues

ICEIS 2018 - 20th International Conference on Enterprise Information Systems

410

continued table

Paper

Ontology validation

(Harrison et al.,

2010)

Systems to sense data to get basic

information about the city or

services. Systems to interconnect

other systems to get better more

complex information about the

city or services. Systems to

analyze, model, optimize,

visualize operations of city

services to get even more complex

information about the city or

services.

(Mrazovic

et al., 2017)

Mobility policies to restrict points

of interest and routes among them

by governments for sustainable

growth.

(Bertoncini,

2015)

A framework to improve a Smart

City infrastructure integration for

cost-efﬁciency, energy efﬁciency,

and reduced carbon footprints.

(Perera et al.,

2014)

Sensing as a Service model to

interconnect different data to

extended service providers for

opportunities to build innovative

value added solutions that make

the decision making process

efﬁcient and effective in IoT

paradigm.

(Schaffers

et al., 2011)

Collaboration frameworks to

integrate Future Internet testbeds

&Living Lab environments for

fostering innovation ecosystems.

(Solanas et al.,

2014)

A smart health concept to propose

complex information about

patient’s health and environment

or trigger processes by using

Smart City elements for a better

quality of life.

(Babar and

Arif, 2017)

The architecture to monitor,

process, translate and

communicate the urban data to

citizens for a quality of life.

(Sanchez et al.,

2014)

A deployment and

experimentation architecture to

provide a suitable platform

for large scale experimentation

and evaluation of IoT concepts

under real-life conditions.

table continues

continued table

Paper

Ontology validation

(Semanjski

et al., 2017)

A model to infer transport mode

from mobile sensed data for better

understanding people’s travel

behavior.

(Peterson et al.,

2010)

A service to use vehicle batteries

to store grid electricity generated

at off-peak hours for off-vehicle

use during peak hours for

balancing electricity consumption.

(Borgia, 2014)

An IoT paradigm to support

Smart City ideas.

(Angelidou,

2017)

Different strategies to enhance

citizen participation and civic

innovation.

(Farahani et al.,

2018)

A holistic architecture to sense,

monitor, process, translate and

communicate data to doctors and

citizens for better quality of life.

(Nadal et al.,

2017)

An automated methodology to use

airborne sensors for identifying

feasibility of implementation of

rooftop greenhouses in non-

-residential urban areas.

It can be seen that there are three types of papers.

Some are very speciﬁc and they can be very easily

ﬁtted into the ontology, e.g. (Mrazovic et al., 2017).

In the second category, there are papers about more

high-level ideas or concepts, e.g. (Farahani et al.,

2018). These papers cover more ways in the ontology

(the functions of IT architecture are sensing, monito-

ring, processing, translating as well as communica-

ting and they can serve to all the city stakeholders).

The third category of papers contains overview and

conceptual papers. They discuss less speciﬁc topics,

and those papers cover a very broad area of ideas.

Therefore, they may consist of a high number of ele-

ments in the Smart City ontology. Our validation is

mainly focused on the ﬁrst two categories of papers,

rather than overview and conceptual papers.

4 DISCUSSIONS

Based on the validation results, we found that most

papers are ﬁtting into different paths in the Smart City

ontology, indicating the positive validity of this Smart

City ontology. We, therefore, suggest that instead

of ﬁnding a uniﬁed deﬁnition for Smart City, con-

structing and consolidating a comprehensive Smart

City ontology can be more practical to deﬁne the

Validation and Extension of the Smart City Ontology

411

Smart City research.

Our validation results also show that this ontology

can be extended, for example, in the structure com-

ponent, additional elements can be included such as a

methodology, analysis, platform, and approach. Also,

in the function component, elements such as collect,

improve, extend, optimize and handle can be added

to the ontology. This brings to the discussion that a

set of keywords in the ontology may refer to the same

element or the same operation, e.g. approach, method

and methodology may refer to the same term but just

with different phrasing formats. Therefore, consoli-

dating the terms in the ontology can be considered as

a foundational future work for a validated Smart City

ontology.

The independence and scope of the terms propo-

sed in the ontology glossary need to be further vali-

dated, for example, some of our selected Smart City

papers use “Platform” as their structure, which is not

included in the current ontology. There can be over-

laps between platform and system or platform can be

used to dock systems. Therefore, a set of commonly

agreed terms with clear scopes should be derived for

the Smart City ontology. Furthermore, some of the

terms are used together such as service system, where

in the ontology service and system are considered as

two independent terms. Our validation can thus help

to improve the ontology glossary by further clarifying

independence and scope of the ontology glossary.

It can be seen that some elements in the ontology

are rarely used in different works, for example, trans-

late in the function, demographical and infrastructural

in focus, professionals in stakeholders and livability

in outcomes. It may indicate that some element is not

in the focus of the current Smart City research, for

example, professionals may not be one of the well-

focused stakeholders in Smart City research. It may

otherwise indicate that some elements in the ontology

can be omitted or merged to other elements e.g. the

professionals can be considered as part of the citi-

zen. Thus checking the reliability and scope of the

elements in the ontology is critical.

On the other hand, some elements in the ontology

are widely addressed in different Smart City papers.

For example, systems in the structure, monitor in the

function, environmental in focus, data, and informa-

tion in semiotics, citizen and government in stakehol-

ders, and sustainability in outcomes. We may infer

the up-to-date priorities in the current Smart City re-

search. Furthermore, our validation results indicate

which paths in the Smart City ontology can be consi-

dered as main research streams in a Smart City rese-

arch.

We further found that inside the ontology, it is dif-

ﬁcult to commonly agree on the scope of some ele-

ments, especially for the architecture. The Smart City

ontology considers architecture as a part of the struc-

ture. Some papers, e.g. (Babar and Arif, 2017), con-

sider architecture as one big scope that covers all the

concepts in a Smart City. Therefore, we further pro-

pose to reﬁne the scope of the elements in the Smart

City ontology.

5 CONCLUSION

In this paper, we have described the Smart City onto-

logy that is recently proposed by (Ramaprasad et al.,

2017). In order to better explain this ontology, we

have aligned three papers to explain the possible paths

from the ontology aspect. To further validate this on-

tology, we have selected a total of 22 Smart City pa-

pers and veriﬁed the validity of this ontology by mat-

ching the papers to the attributes of the ontology. Ba-

sed on the validation results, we found that this onto-

logy can be further reﬁned and extended. Given dif-

ferent nature of the Smart City papers, some papers

may not ﬁt into this ontology but most papers can be

explained by this ontology. As future works, we plan

to align the design of the Smart Service with the ro-

admap that is indicated by the Smart City ontology.

The implementation of a Smart Service may provide

further insights into the ontology.

REFERENCES

Adame, T., Bel, A., Carreras, A., Meli

a-Segu

ı, J., Oliver,

M., and Pous, R. (2016). Cuidats: An rﬁd-wsn hy-

brid monitoring system for smart health care environ-

ments. 78.

Angelidou, M. (2017). The role of smart city characteris-

tics in the plans of ﬁfteen cities. Journal of Urban

Technology, 24(4):3–28.

Babar, M. and Arif, F. (2017). Smart urban planning using

big data analytics to contend with the interoperability

in internet of things. Future Generation Computer Sy-

stems, 77:65–76.

Bertoncini, M. (2015). Multi-resource optimized smart ma-

nagement of urban energy infrastructures for impro-

ving smart city energy efﬁciency. In SMARTGREENS

2015 - 4th International Conference on Smart Cities

and Green ICT Systems, Proceedings, pages 107–114.

Cited By :2.

Bifulco, F., Tregua, M., and Amitrano, C. C. (2017). Co-

governing smart cities through living labs. top eviden-

ces from eu. Transylvanian Review of Administrative

Sciences, 2017(50E):21–37. Cited By :1.

Blaschke, T., Hay, G. J., Weng, Q., and Resch, B. (2011).

Collective sensing: Integrating geospatial technolo-

ICEIS 2018 - 20th International Conference on Enterprise Information Systems

412

gies to understand urban systems-an overview. Re-

mote Sensing, 3(8):1743–1776. Cited By :40.

Borgia, E. (2014). The internet of things vision: Key featu-

res, applications and open issues. Computer Commu-

nications, 54:1–31. Cited By :172.

Cardone, G., Foschini, L., Bellavista, P., Corradi, A., Bor-

cea, C., Talasila, M., and Curtmola, R. (2013). Fos-

tering participaction in smart cities: A geo-social cro-

wdsensing platform. IEEE Communications Maga-

zine, 51(6):112–119. Cited By :93.

Chen, Y., Nakazawa, J., Yonezawa, T., Kawsaki, T., and

Tokuda, H. (2016). Cruisers: A public automotive

sensing platform for smart cities. In Proceedings - In-

ternational Conference on Distributed Computing Sy-

stems, volume 2016-August, pages 767–768. Cited

By :3.

Cocchia, A. (2014). Smart and digital city. pages 13–43.

Farahani, B., Firouzi, F., Chang, V., Badaroglu, M., Con-

stant, N., and Mankodiya, K. (2018). Towards fog-

driven iot ehealth: Promises and challenges of iot in

medicine and healthcare. Future Generation Compu-

ter Systems, 78:659–676.

Gifﬁnger, R., Fertner, C., Kramar, H., Kalasek, R., Pichler-

Milanovi

c, N., and Meijers, E. (2007). Smart cities:

Ranking of European medium-sized cities. Vienna

University of Technology, Vienna.

Hall, R. H., Bowerman, B., Braverman, J., Taylor, J., Todo-

sow, H., and von Wimmersperg, U. (2000). The vision

of a smart city.

Harrison, C., Eckman, B., Hamilton, R., Hartswick, P.,

Kalagnanam, J., Paraszczak, J., and Williams, P.

(2010). Foundations for smarter cities. IBM Journal

of Research and Development, 54(4). Cited By :160.

Masutani, O. (2015). A sensing coverage analysis of a route

control method for vehicular crowd sensing. In 2015

IEEE International Conference on Pervasive Compu-

ting and Communication Workshops, PerCom Works-

hops 2015, pages 396–401. Cited By :4.

Mrazovic, P., Larriba-Pey, J. L., and Matskin, M. (2017).

Improving mobility in smart cities with intelligent

tourist trip planning. In Proceedings - International

Computer Software and Applications Conference, vo-

lume 1, pages 897–907.

Nadal, A., Alam

us, R., Pipia, L., Ruiz, A., Corbera, J., Cu-

erva, E., Rieradevall, J., and Josa, A. (2017). Urban

planning and agriculture. methodology for assessing

rooftop greenhouse potential of non-residential areas

using airborne sensors. Science of the Total Environ-

ment, 601-602:493–507.

Nam, T. and Pardo, T. A. (2011). Conceptualizing smart

city with dimensions of technology, people, and insti-

tutions. In ACM International Conference Proceeding

Series, pages 282–291. Cited By :233.

Perera, C., Zaslavsky, A., Christen, P., and Georgakopou-

los, D. (2014). Sensing as a service model for smart

cities supported by internet of things. Transacti-

ons on Emerging Telecommunications Technologies,

25(1):81–93. Cited By :205.

Peterson, S. B., Whitacre, J. F., and Apt, J. (2010). The

economics of using plug-in hybrid electric vehicle bat-

tery packs for grid storage. Journal of Power Sources,

195(8):2377–2384. Cited By :275.

Ramaprasad, A., S

anchez-Ortiz, A., and Syn, T. (2017). A

uniﬁed deﬁnition of a smart city. In Electronic Go-

vernment, pages 13–24. Springer International Publis-

hing, Cham.

Sanchez, L., Mu

noz, L., Galache, J. A., Sotres, P., Santana,

J. R., Gutierrez, V., Ramdhany, R., Gluhak, A., Krco,

S., Theodoridis, E., and Pﬁsterer, D. (2014). Smart-

santander: Iot experimentation over a smart city test-

bed. Computer Networks, 61:217–238. Cited By :113.

Schaffers, H., Komninos, N., Pallot, M., Trousse, B., Nils-

son, M., and Oliveira, A. (2011). Smart cities and

the future internet: Towards cooperation frameworks

for open innovation, volume 6656 of Lecture Notes in

Computer Science (including subseries Lecture Notes

in Artiﬁcial Intelligence and Lecture Notes in Bioin-

formatics). Cited By :256.

Semanjski, I., Gautama, S., Ahas, R., and Witlox, F. (2017).

Spatial context mining approach for transport mode

recognition from mobile sensed big data. Computers,

Environment and Urban Systems, 66:38–52.

Solanas, A., Patsakis, C., Conti, M., Vlachos, I., Ra-

mos, V., Falcone, F., Postolache, O., Perez-Martinez,

P., Pietro, R., Perrea, D., and Martinez-Balleste, A.

(2014). Smart health: A context-aware health para-

digm within smart cities. IEEE Communications Ma-

gazine, 52(8):74–81. Cited By :112.

Su, K., Li, J., and Fu, H. (2011). Smart city and the ap-

plications. In 2011 International Conference on Elec-

tronics, Communications and Control (ICECC), pages

1028–1031.

Uribe-Perez, N. and Pous, C. (2017). A novel communi-

cation system approach for a smart city based on the

human nervous system. Future Generation Computer

Systems, 76:314–328. Cited By :1.

Validation and Extension of the Smart City Ontology

413