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    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 Kim4 1 Department of Computer Engineering, Chung-Ang University, Seoul 156-756, Republic of Korea 2 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 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 characterized 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 technologies 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 environment, 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 processing 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 assessment of a virtual underwater multisensory effects reproducing 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 underwater simulation systems and the proposed multimodal effect reproduction system maintained its usability while its presence improved. Another paper is “Distributed abnormal activity detection 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 effectiveness 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 effectiveness of smart tools through experiment considerations. The work entitled “Analysis of college classes based on UCLASS 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. 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, “spear: a new method for expert based recommendation systems,” 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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