Hindawi Publishing Corporation
International Journal of Distributed Sensor Networks
Article ID 259280
Editorial
Smart Learning with Sensor Network Technologies
Jason J. Jung,
1
Pankoo Kim,
2
Ngoc Thanh Nguyen,
3
and ChongGun Kim
4
1
Department of Computer Engineering, Chung-Ang University, Seoul 156-756, Republic of Korea
Department of Computer Engineering, Chosun University, Gwangju 501-759, Republic of Korea
3
Institute of Informatics, Wroclaw University of Technology, 50-370 Wroclaw, Poland
4
Department of Computer Engineering, Yeungnam University, Gyeongsan 712-749, Republic of Korea
2
Correspondence should be addressed to Jason J. Jung; j2jung@gmail.com
Received 12 October 2014; Accepted 12 October 2014
Copyright © Jason J. Jung et al. This is an open access article distributed under the Creative Commons Attribution License, which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Classical lectures for a number of students are character-
ized by several problems: (i) students are passive, and (ii)
interactions between the participants (students and lecturers)
are reduced. Last some years, studies have been devoted to
exploring how new media can be harnessed to support and
promote collaborative learning activities in large learning
groups. Prominent applications based on sensor technolo-
gies have been paid more attention. They include audience
response and monitoring system with sensors for accessing
the students’ retention and attention during lectures, as well
as smart devices (smartphones and smart pads) for collecting
feedbacks from the students [1–4]. Moreover, social media
(e.g., wikis, Twitter, and Facebook) can be regarded as a kind
of sensors.
Given a variety of sensor data from the learning envi-
ronment, efficient sensor data processing and management
remains a challenge in many research areas, for example,
information acquisition and stream processing as well as data
integration. Also, the number of diverse information process-
ing system architectures might be involved in these areas.
They need to exploit relevant solutions to support a number
of smart learning services (e.g., knowledge management and
decision making).
In this special issue, we received numerous outstanding
article submissions. We then sent these submissions to
qualified experts for review. Finally, based on the review
results and the suggestions of reviewers, four articles were
accepted to be included into the special issue. The articles are
simply introduced as follows.
The work by C.-M. Kim et al. entitled
“Design and assess-
ment of a virtual underwater multisensory effects reproduc-
ing simulation system”
proposes an immersive multisensory
effect reproduction system that provides an improved sense
of underwater reality for users. To verify the efficacy of the
proposed system and methods, they solicited participants
and conducted an experiment on presence and usability of
the primary evaluation elements of virtual reality under-
water simulation systems and the proposed multimodal
effect reproduction system maintained its usability while its
presence improved.
Another paper is
“Distributed abnormal activity detec-
tion in smart environments”
by C. Wang et al. This work
proposes distributed abnormal activity detection approach
(DetectingAct), which employs the computing and storage
resources of simple and ubiquitous sensor nodes, to detect
abnormal activity in smart environments equipped with
wireless sensor networks (WSN).
The paper
“Hand gesture and character recognition based
on Kinect sensor”
by T. Murata and J. Shin would like to
propose a method to see if Kinect sensor can recognize
numeric and alphabetic characters written with the hand in
the air. The proposed method found out that most people are
not used to writing in the air and are unfamiliar with Kinect
sensor, and it takes some time to master them both.
The paper entitled
“The research trends and the effective-
ness of smart learning”
by I. Ha and C. Kim proposes the
review study on smart learning. Although a lot of smart tools
have been applied for educational application, there are only
2
limited researches that demonstrate the educational effective-
ness of smart tools through experiment considerations.
The work entitled
“Analysis of college classes based on U-
CLASS system using personal mobile nodes”
by C.-G. Kim et
al. presented an interactive learning management system that
provides interactive communications between a professor
and students.
International Journal of Distributed Sensor Networks
Acknowledgments
We thank all the authors for their outstanding contributions.
We also want to express our deepest gratitude to all the
anonymous reviewers who devoted much of their precious
time to review all the papers. Their timely reviews greatly
helped us in selecting the best papers included in the special
issue. Finally, we hope you will enjoy reading these selected
papers as we did and you will find this issue informative and
helpful in keeping yourselves up-to-date in the fast changing
field of the “Ubiquitous Sensing and Cloud Computing.”
Jason J. Jung
Pankoo Kim
Ngoc Thanh Nguyen
ChongGun Kim
References
[1] J. J. Jung, “Social grid platform for collaborative online learning
on blogosphere: a case study of eLearning@BlogGrid,”
Expert
Systems with Applications,
vol. 36, no. 2, pp. 2177–2186, 2009.
[2] D. T. Nguyen and J. E. Jung, “Privacy-preserving discovery of
topic-based events from social sensor signals: an experimental
study on twitter,”
The Scientific World Journal,
vol. 2014, Article
ID 204785, 5 pages, 2014.
[3] X. H. Pham, T. T. Nguyen, J. J. Jung, and N. T. Nguyen, “<A,V>-
spear: a new method for expert based recommendation sys-
tems,”
Cybernetics and Systems,
vol. 45, no. 2, pp. 165–179, 2014.
[4] J. J. Jung, “Understanding information propagation on online
social tagging systems: a case study on Flickr,”
Quality and
Quantity,
vol. 48, no. 2, pp. 745–754, 2014.
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