Recent Advancements in Big Data Technologies and Applications in
Computing, IoT and Computer Engineering Technology
J.UCS Special Issue
Ka Lok Man
(Xi'an Jiaotong-Liverpool University, China and Swinburne University of
Technology Sarawak, Malaysia
Ou (Owen) Liu
(Xi'an Jiaotong-Liverpool University, China
(KU Leuven, Belgium
(Southern Illinois University, Carbondale, IL, USA
Welcome to the special issue on "Recent Advancements in Big Data
Technologies and Applications in Computing, IoT and Computer
Engineering Technology". This issue presents ten high quality academic
papers. This mix provides a well-rounded snapshot of current research
in the field and provides a springboard for driving future work and
discussion. The ten papers presented in this volume are summarized as
1. "Longitudinal Healthcare Data Management Platform of Healthcare IoT
Devices for Personalized Services": Ahyoung Choi and Hangsik Shin
propose a platform that can be utilized for future healthcare service
to share accumulated healthcare data in various situations.
2. "An Experimental System for MQTT/CoAP-based IoT Applications in
IPv6 over Bluetooth Low Energy": Chi-Yi Lin, Kai-Hung Liao and
Chia-Hsuan Chang present an IPv6 over BLE experimental system based on
Raspberry Pi 3 and Nordic nRF51-DK development boards for
MQTT/CoAP-based IoT Applications.
3. "An Effective Risk Factor Detection and Disease Prediction (RFD-DP)
Model Applied to Hypertension": Dingkun Li, Yaning Li, Zhou Ye, Seon
Phil Jeone, Musa Ibrahim and Keun Ho Ryu illustrate a model called
Risk Factor Detection and Disease Prediction (RFD-DP) which
outperforms traditional feature selection and classification methods
in terms of accuracy, F-score, and AUC.
. "Target Selection in Head-Mounted Display Virtual Reality
Environments": Difeng Yu, Hai-Ning Liang, Feiyu Lu, Vijayakumar
Nanjappan, Konstantinos Papangelis and Wei Wang explore target
selection in HMD VR environments which assesses the performance of the
main selection metaphors/techniques under conditions that are relevant
to the VR environments, including a various index of difficulty
(derived from the Fitt's Law), target density, and target occlusion.
5. "Detection of Potholes Using a Deep Convolutional Neural Network":
Lim Kuoy Suong and Kwon Jangwoo design a deep Convolutional Neural
Network (CNN) based on YOLOv2 with a different architecture for the
detection of potholes.
6. "Verifying Secure Authentication Protocol for Communication between
IoT-based Medical Devices": Nipon Theera-Umpon, Kun-Hee Han, Woo-Sik
Bae, Sanghyuk Lee and Van Huy Pham develop a protocol which encrypts
the communication process and data to eliminate the likelihood of
personal information being leaked.
7. "Machine Learning Optimization of Parameters for Noise Estimation":
Yuyong Jeon, Ilkyeun Ra, Youngjin Park and Sangmin Lee propose a fast
and effective machine learning method of parameter optimization for
noise estimation of various types of noise.
8. "Crumbling Walls Log Quorum System based Name Resolution Routing
for CCN based IoT": Pir Imran Shah, Peer Azmat Shah, Sadaf Yasmin,
Zahoor-ur-Rehman, Akhlaque Ahmad, Yunyoung Nam and Seungmin Rho
present a Content Centric Networking (CCN) based approach in the IoT
environment to address scalability problems associated with
9. "The Generation of Electricity Load Profiles Using K-Means
Clustering Algorithm": Rūta Užupytė, Tomas Babarskis and Tomas
Krilavičius introduce an approach which is based on the periodicity
analysis and well-known clustering technique - K-means that can be
applied for identification for separate users load profiles and
clustering of load profiles.
10. "Modelling of Automotive Engine Dynamics using Diagonal Recurrent
Neural Network": Yujia Zhai, Kejun Qian, Fei Xue and Moncef Tayahi
apply a Diagonal Recurrent Neural Network (DRNN) to model SI engine
dynamics to achieve a balance between the modelling performance and
computational burden, and a moderate cost on computation.
We are beholden to all of the authors for their contributions to the
special issue. We would also like to thank the J.UCS editorial team
for their support.