Intelligent Services for Smart Cities
J.UCS Special Issue
Jason J. Jung
(Chung-Ang University, Seoul, Korea
j2jung@gmail.com)
Andrés Muñoz
(Universidad Católica San Antonio, Murcia, Spain
amunoz@ucam.edu)
The Smart City concept has flourished in the last decade since its
first appearance in late 1990s. The dramatic increase in the
importance of this topic has been possible thanks to the rapid
development of the technology, especially due to the emergence of a
myriad of sensors aimed to gather different urban data (e.g., weather,
traffic, use of energy, flows of people, etc.) [Anthopoulos
2017]. While technological progress seems to be reaching an acceptable
maturity level, the focus has now shifted to the development of
intelligent applications to processing such urban data. These new
challenges range from low-level services such as dealing with the size
and variety of urban data or handling the complexity of the underlying
physical models [Hashem et al. 2016, Nam and Pardo 2011], to
high-level applications such as platforms for cultural heritage
management or intelligent parking applications [Koukopoulos et
al. 2017, Muñoz and Botía 2010], without forgetting the concerns on
protecting individuals' privacy in all these applications
[Martinez-Balleste et al. 2013].
The first paper in this issue, entitled "A Study on
Context-Relationship with Context-Attributes for a Smart Service
Generation in Smart City" and authored by Hoon Ko, Seogchan Hwang,
Libor Mesicek, Jongsun Choi, Junho Choi and Pankoo Kim, focuses on
developing more efficient services in smart buildings through the
analysis of the relationships among context-attributes such as user's
location, movements and service history. Authors design a smart
service model to track user's contexts and offer only the most
accurate services in each different context by different strategies
such as service sustainability or service merging. A case of study is
presented to illustrate the functionality of the proposed service
model.
In the second paper, entitled "Air-Pollution Prediction in Smart
Cities through Machine Learning Methods: A Case of Study in Murcia,
Spain" and authored by Raquel Martínez-España, Andrés
Bueno-Crespo, Isabel Timón, Jesüs Soto, Andrés Muñoz, and José
M. Cecilia, different machine learning techniques have been evaluated
for a selection of the most influence climatic variables to predict
ozone level in smart cities. Moreover, by using hierarchical
clustering techniques based on air quality data collected as time
series, it is shown that, as a case of study, the Region of Murcia
(Spain) can be divided into two zones to study the ozone level. Among
the machine learning models analyzed, Random Forest has been proved as
the best technique for predicting pollution according to ozone
levels. Thus, this paper provides some compelling solutions to
efficiently deal with air pollution problems in smart cities.
The third paper, entitled "Linking User Online Behavior across Domains
with Internet Traffic" and authored by Yuanyuan Qiao, Yan Wu, Yaobin
He, Libo Hao, Wenhui Lin and Jie Yang, introduces the idea of the
Online With Offline (OWO) era in smart cities, where the majority of
the population is using various online services to connect friends,
watch videos, listen to music, download resources and so on. Citizens'
online behaviors are separated by different domains, which may cause
serious problem in the area of cross-domain recommendation,
advertising, and criminal tracking in both the online and offline
worlds, since it is a very challenging task to link user online
behaviors belonging to the same physical person. As an alternative,
authors propose a novel hybrid model called Online Behavior Linkage
across Domains (OBLD) to link user's online behavior across domains
with Internet traffic.
The fourth paper, entitled "Crowd Sensing for Urban Security in Smart
Cities" and authored by Bruno Fernandes, Fábio Silva, Cesar Analide,
and José Neves, aims to provide a proof of concept for crowd sensing
and smart clothing, evaluating its feasibility and practical potential
for any city that wants to cross borders and become a smart one. The
achieved results exceeded expectations with a Smart Scanner being able
to sense 98% of the crowd, thus providing relevant insights on
important points of interest for vulnerable road users. In turn, a
Smart Clothing study has been performed to strengthen the citizen
sensor, allowing pedestrians, runners and cyclists to be equipped with
a sweater holding BLE (Bluetooth Low Energy) transmitters, making
these vulnerable users visible on the road. Advocating for
transparency and reliability, all produced software has been released
as open source in GitHub.
In the fifth paper, entitled "Multi-scaled Spatial Analytics on
Discovering Latent Social Events for Smart Urban Services" and
authored by O-Joun Lee, Yunhu Kim, Hoang Long Nguyen, and Jai E. Jung,
a novel smart city application has been presented by developing an
adaptive method to discover latent social events by using social
data. Three steps are proposed to achieve this goal: discovering areas
of interest (AOIs), allocating texts from social media to the AOIs and
detecting social events in each AOI according to the texts. These
steps rely on the use of keywords and temporal distribution of the
social texts.
This special issue has been achieved by a number of fruitful
collaborations. We would like to thank the editor in chief of Journal
of Universal Computer Science (JUCS), Christian Gütl, for his kind
support and help during the entire process of publication. This was
possible thanks to the work of the renowned researchers that provided
their anonymous reviews.
Finally, we are most grateful to the authors for their valuable
contributions and for their willingness and efforts to improve their
papers in accordance with the suggestions and comments from reviewers.
Jason J. Jung and Andrés Muñoz
Seoul (Korea) and Murcia (Spain) March, 2018.
References
[Anthopoulos 2017] Anthopoulos, L. G.: The Rise of the Smart City. In
Understanding Smart Cities: A Tool for Smart Government or an
Industrial Trick?, 5-45, 2017. Springer, Cham
[Hashem et al. 2016] Hashem, I.A.T., Chang, V., Anuar, N.B., Adewole,
K., Yaqoob, I., Gani, A., Ahmed, E., Chiroma, H.: The role of big data
in smart city. International Journal of Information Management,
36(5):748-758, 2016.
[Koukopoulos et al. 2017] Koukopoulos, Z., Koukopoulos, D. and Jung,
J.J.: A trustworthy multimedia participatory platform for cultural
heritage management in smart city environments. Multimedia Tools and
Applications, 76(24):25943-25981, 2017.
[Muñoz and Botía 2010] Muñoz, A., Botía,J.A.: Developing an
intelligent parking management application based on multi-agent
systems and semantic web technologies. In International Conference on
Hybrid Artificial Intelligence Systems, 64-72,
2010. Springer.
[Martinez-Balleste et al. 2013] Martinez-Balleste, A.,
Pérez-Martínez, P.A., Solanas, A.: The pursuit of citizens' privacy:
a privacy-aware smart city is possible. IEEE Communications Magazine,
51(6):136-141, 2013.
[Nam and Pardo 2011] Nam, T., Pardo, T.A.:
Conceptualizing smart city with dimensions of technology, people, and
institutions. In Proceedings of the 12th annual international digital
government research conference: digital government innovation in
challenging times, 282-291, 2011.
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