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Advances in Parallel and Distributed Computing and Ubiquitous Services

E-BookPDF1 - PDF WatermarkE-Book
236 Seiten
Englisch
Springer Nature Singaporeerschienen am23.01.20161st ed. 2016
This book contains the combined proceedings of the 4th International Conference on Ubiquitous Computing Application and Wireless Sensor Network (UCAWSN-15) and the 16th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT-15). The combined proceedings present peer-reviewed contributions from academic and industrial researchers in fields including ubiquitous and context-aware computing, context-awareness reasoning and representation, location awareness services, and architectures, protocols and algorithms, energy, management and control of wireless sensor networks. The book includes the latest research results, practical developments and applications in parallel/distributed architectures, wireless networks and mobile computing, formal methods and programming languages, network routing and communication algorithms, database applications and data mining, access control and authorization and privacy preserving computation.



Professor James J. Park received his Ph.D. degree in Graduate School of Information Security from Korea University, Korea. From December, 2002 to July, 2007, Dr. Park had been a research scientist of R&D Institute, Hanwha S&C Co., Ltd., Korea. From September, 2007 to August, 2009, He had been a professor at the Department of Computer Science and Engineering, Kyungnam University, Korea. 

Professor Gangman Yi received his master's degree in Computer Sciences at Texas A&M University, USA in 2007, and doctorate in Computer Sciences at Texas A&M University, USA in 2011. In May 2011, he joined System S/W group in Samsung Electronics, Suwon, Korea. He joined the Department of Computer Science & Engineering, Gangneung-Wonju National University, Korea, since March 2012. Dr. Yi has been researched in an interdisciplinary field of researches.
Professor Young Sik Jeong is Professor of Multimedia Engineering in Dongguk University. He received his master's degree in Computer Sciences at Korea University, Korea, and, doctorate in Computer Sciences at Korea University. His Research and Interests are 'IaaS cloud-based service technology for multimedia content processing', 'Big multimedia data processing in the mobile cloud-based technologies', 'Multimedia data processing middleware development in the WSN environment', 'CPS-related technology for multimedia content service', and 'Cloud service technologies for IoT based technical support'. 




Professor Hong Shen is Professor (Chair) of Computer Science in the School of Computer Science, University of Adelaide. He received his B.Eng. degree from Beijing University of Science
and Technology, M.Eng. degree from University of Science and Technology of China, Ph.Lic. and Ph.D. degrees from Abo Akademi University, Finland, all in Computer Science.mehr
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Produkt

KlappentextThis book contains the combined proceedings of the 4th International Conference on Ubiquitous Computing Application and Wireless Sensor Network (UCAWSN-15) and the 16th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT-15). The combined proceedings present peer-reviewed contributions from academic and industrial researchers in fields including ubiquitous and context-aware computing, context-awareness reasoning and representation, location awareness services, and architectures, protocols and algorithms, energy, management and control of wireless sensor networks. The book includes the latest research results, practical developments and applications in parallel/distributed architectures, wireless networks and mobile computing, formal methods and programming languages, network routing and communication algorithms, database applications and data mining, access control and authorization and privacy preserving computation.



Professor James J. Park received his Ph.D. degree in Graduate School of Information Security from Korea University, Korea. From December, 2002 to July, 2007, Dr. Park had been a research scientist of R&D Institute, Hanwha S&C Co., Ltd., Korea. From September, 2007 to August, 2009, He had been a professor at the Department of Computer Science and Engineering, Kyungnam University, Korea. 

Professor Gangman Yi received his master's degree in Computer Sciences at Texas A&M University, USA in 2007, and doctorate in Computer Sciences at Texas A&M University, USA in 2011. In May 2011, he joined System S/W group in Samsung Electronics, Suwon, Korea. He joined the Department of Computer Science & Engineering, Gangneung-Wonju National University, Korea, since March 2012. Dr. Yi has been researched in an interdisciplinary field of researches.
Professor Young Sik Jeong is Professor of Multimedia Engineering in Dongguk University. He received his master's degree in Computer Sciences at Korea University, Korea, and, doctorate in Computer Sciences at Korea University. His Research and Interests are 'IaaS cloud-based service technology for multimedia content processing', 'Big multimedia data processing in the mobile cloud-based technologies', 'Multimedia data processing middleware development in the WSN environment', 'CPS-related technology for multimedia content service', and 'Cloud service technologies for IoT based technical support'. 




Professor Hong Shen is Professor (Chair) of Computer Science in the School of Computer Science, University of Adelaide. He received his B.Eng. degree from Beijing University of Science
and Technology, M.Eng. degree from University of Science and Technology of China, Ph.Lic. and Ph.D. degrees from Abo Akademi University, Finland, all in Computer Science.

Inhalt/Kritik

Inhaltsverzeichnis
1;Message from the UCAWSN 2015 General Chairs;6
2;Message from the UCAWSN 2015 ProgramChairs;8
3;Organization;10
4;Message From the PDCAT 2015 General Chairs;12
5;Organization Committee;13
6;Contents;15
7;1 Rhymes+: A Software Shared Virtual Memory System with Three Way Coherence Protocols on the Intel Single-Chip Cloud Computer;18
7.1;Abstract;18
7.2;1 Introduction;18
7.3;2 Related Works;19
7.4;3 Intel SCC;20
7.5;4 Three Way Coherence;20
7.5.1;4.1 Shared Physical Memory;20
7.5.2;4.2 Distributed Shared Memory;21
7.5.3;4.3 Shared Physical Memory-on-Chip;21
7.6;5 Performance Evaluations;23
7.6.1;5.1 Small Problem Size;23
7.6.2;5.2 Medium Problem Size;23
7.6.3;5.3 Large Problem Size;25
7.7;6 Conclusion;25
7.8;Acknowledgements;25
7.9;References;26
8;2 Review and Comparison of Mobile Payment Protocol;27
8.1;Abstract;27
8.2;1 Introduction;27
8.3;2 Background and Related Work;28
8.3.1;2.1 Primitive Payment Transaction;28
8.3.2;2.2 Components of Mobile Payment;28
8.3.3;2.3 Mobile Payment Procedure;29
8.4;3 Technology of Mobile Payment;29
8.5;4 Security of Mobile Payment;30
8.5.1;4.1 Security Properties;30
8.5.2;4.2 Cryptography Concept;31
8.6;5 Analysis of Existing Secure Mobile Payment Protocols;31
8.6.1;5.1 Methodology Aspect;33
8.6.2;5.2 Security Aspect;33
8.6.3;5.3 Peformance Aspect;35
8.7;6 Conclusion;35
8.8;References;35
9;3 POFOX: Towards Controlling the Protocol Oblivious Forwarding Network;37
9.1;Abstract;37
9.2;1 Introduction;37
9.3;2 POFOX Design;38
9.3.1;2.1 Communication Engine;39
9.3.2;2.2 Topology Discovery;39
9.3.3;2.3 Spanning Tree Protocol;41
9.4;3 POF Network Testbed;43
9.4.1;3.1 Devices;43
9.4.2;3.2 Functionality Test;44
9.4.3;3.3 Performance Test;44
9.4.3.1;3.3.1 Connection Time;44
9.4.3.2;3.3.2 Avoiding Broadcast Storm;45
9.5;4 Conclusion and Future Work;45
9.6;Acknowledgements;46
9.7;References;46
10;4 An Experimental Study on Social Regularization with User Interest Similarity;47
10.1;Abstract;47
10.2;1 Introduction;48
10.3;2 Related Work;49
10.4;3 New Similarity Functions;49
10.5;4 Social Regularization;50
10.5.1;4.1 Model 1: Friend Relationship Regularization (FRR);50
10.5.2;4.2 Model 2: Shared Friend Regularization (SFR);51
10.6;5 Experimental Analysis;51
10.6.1;5.1 Dataset;51
10.6.2;5.2 Comparisons;52
10.7;6 Conclusion;53
10.8;Acknowledgments;53
10.9;References;53
11;5 Representing Higher Dimensional Arrays into Generalized Two-Dimensional Array: G2A;55
11.1;Abstract;55
11.2;1 Introduction;55
11.3;2 Generalized Two Dimensional Representation (G2A) Scheme;56
11.3.1;2.1 G2A for TMA(4);56
11.3.2;2.2 G2A for TMA(n);57
11.4;3 Comparison Between TMA and G2A for Matrix Operations;57
11.4.1;3.1 Matrix-Matrix Addition/Subtraction Algorithms;58
11.4.2;3.2 Matrix-Matrix Multiplication Algorithms;59
11.5;4 Experimental Results;60
11.6;5 Related Works;61
11.7;6 Conclusion;61
11.8;References;61
12;6 A Portable and Platform Independent File System for Large Scale Peer-to-Peer Systems and Distributed Applications;63
12.1;Abstract;63
12.2;1 Introduction;63
12.3;2 User Space File System;64
12.4;3 Metadata Structures;66
12.5;4 Implementation;67
12.6;5 Evaluation;68
12.7;6 Related Work;70
12.8;7 Conclusion;70
12.9;References;70
13;7 OCLS: A Simplified High-Level Abstraction Based Framework for Heterogeneous Systems;72
13.1;Abstract;72
13.2;1 Introduction;72
13.3;2 Related Works;73
13.4;3 OCLS Framework;74
13.4.1;3.1 OCLS Abstraction Layer;74
13.4.2;3.2 OCLS Library;75
13.4.3;3.3 Kernel Data Type and Data Movement;75
13.4.4;3.4 Runtime Data Structures;76
13.5;4 Case Study;76
13.6;5 Evaluation;77
13.6.1;5.1 Code Size Comparison;78
13.6.2;5.2 Performance;78
13.6.3;5.3 Stability;79
13.7;6 Conclusion;80
13.8;Acknowledgments;80
13.9;References;80
14;8 Hierarchical Caching Management for Software Defined Content Network Based on Node Value;81
14.1;Abstract;81
14.2;1 Introduction;82
14.3;2 Hierarchical Cache Model;82
14.3.1;2.1 Software Defined Content Network Architecture;83
14.3.2;2.2 Node Value;83
14.3.3;2.3 Hierarchical Cache Model;84
14.4;3 Cache Decision Strategy Based on Node Value;85
14.5;4 Experiment and Analysis;85
14.6;5 Conclusion;87
14.7;Acknowledgements;87
14.8;References;87
15;9 Interoperation of Distributed MCU Emulator/Simulator for Operating Power Simulation of Large-Scale Internet of Event-Driven Control Things;88
15.1;Abstract;88
15.2;1 Introduction;89
15.3;2 Research Motivation;90
15.4;3 Proposed Architecture;91
15.5;4 Our Approach for Implementation Method;93
15.6;5 Conclusion;94
15.7;Acknowledgements;94
15.8;References;95
16;10 The Greedy Approach to Group Students for Cooperative Learning;96
16.1;Abstract;96
16.2;1 Introduction;96
16.3;2 Related Work;97
16.3.1;2.1 Cooperative Learning;97
16.3.2;2.2 Finding a Team of Experts;98
16.3.3;2.3 Grouping Students with Ability;98
16.4;3 Framework;98
16.4.1;3.1 Preliminaries;98
16.4.2;3.2 Problem;101
16.5;4 Algorithm;101
16.6;5 Conclusions;102
16.7;References;102
17;11 Secure Concept of SCADA Communication for Offshore Wind Energy;103
17.1;Abstract;103
17.2;1 Introduction;103
17.3;2 Need to Secure Communication;104
17.4;3 Security Concepts;105
17.4.1;3.1 Threats;105
17.4.2;3.2 Security Processes;105
17.5;4 Apply Security to Wind Energy;107
17.6;5 Conclusion;108
17.7;Acknowledgements;108
17.8;References;108
18;12 ASR Error Management Using RNN Based Syllable Prediction for Spoken Dialog Applications;110
18.1;Abstract;110
18.2;1 Introduction;110
18.3;2 Method;111
18.3.1;2.1 Overall Architecture;111
18.3.2;2.2 ASR Error Detection;111
18.3.3;2.3 Syllable Prediction RNN-Based Error Correction (SPREC);113
18.4;3 Experiments;115
18.4.1;3.1 ASR Error Detection;115
18.4.2;3.2 ASR Error Correction;116
18.5;4 Conclusion;116
18.6;Acknowledgements;116
18.7;References;117
19;13 A Protection Method of Mobile Sensitive Data and Applications Over Escrow Service;118
19.1;Abstract;118
19.2;1 Introduction;118
19.3;2 Related Works;119
19.4;3 The Proposed Scheme;120
19.5;4 Conclusion;125
19.6;Acknowledgments;126
19.7;References;126
20;14 GPU-Based Fast Refinements for High-Quality Color Volume Rendering;127
20.1;Abstract;127
20.2;1 Introduction;128
20.3;2 Description of Method;129
20.3.1;2.1 Boundary Refinements;129
20.3.2;2.2 Fast GPU-Based Refinement;130
20.4;3 Results and Discussion;131
20.5;4 Conclusion;132
20.6;Acknowledgments;133
20.7;References;133
21;15 Beacon Distance Measurement Method in Indoor Ubiquitous Computing Environment;134
21.1;Abstract;134
21.2;1 Introduction;135
21.3;2 Filter-Based Beacon Distance Measurement;135
21.3.1;2.1 Beacon Distance Measurement Framework;135
21.3.2;2.2 Definition of Indoor Environment;135
21.3.3;2.3 Filters of Beacon Distances;136
21.4;3 Experiment;138
21.5;4 Conclusion;138
21.6;Acknowledgements;138
21.7;References;138
22;16 Indoor Location-Based Natural User Interface for Ubiquitous Computing Environment;140
22.1;Abstract;140
22.2;1 Introduction;140
22.3;2 User Location Estimation Framework in Indoor Environment;141
22.3.1;2.1 Location-Based NUI/NUX Concept;141
22.3.2;2.2 Process of User Location Estimation;142
22.4;3 Experiment;142
22.5;4 Conclusion;144
22.6;Acknowledgements;144
22.7;References;145
23;17 Flexible Multi-level Regression Model for Prediction of Pedestrian Abnormal Behavior;146
23.1;Abstract;146
23.2;1 Introduction;146
23.3;2 Flexible Multi-level Regression Model for Prediction of Abnormal Behavior;147
23.3.1;2.1 Abnormal Behavior Diagram of Prediction System;147
23.3.2;2.2 Data Classification;148
23.3.3;2.3 Flexible Multi-level Regression Model;149
23.3.4;2.4 Behavior Prediction;150
23.4;3 Scenario Application;150
23.5;4 Conclusion;151
23.6;Acknowledgements;152
23.7;References;152
24;18 Automatic Lighting Control Middleware System Controlled by User's Emotion Based on EEG;153
24.1;Abstract;153
24.2;1 Introduction;153
24.3;2 Related Research;154
24.4;3 Emotional Lighting Control Middleware System;155
24.4.1;3.1 Structure of System;155
24.4.2;3.2 Emotional Lighting Control Middleware System Application;156
24.5;4 Experiment and Results;157
24.5.1;4.1 EEG Analysis;157
24.5.2;4.2 Experiment;157
24.6;5 Conclusion;158
24.7;Acknowledgments;158
24.8;References;158
25;19 Hand Recognition Method with Kinect;160
25.1;Abstract;160
25.2;1 Introduction;160
25.3;2 Related Work;161
25.4;3 Hand Region Detection Algorithm;162
25.4.1;3.1 Hand Segmentation;163
25.4.2;3.2 Outlines Detection;164
25.4.3;3.3 Finger Count Recognition;164
25.5;4 Experiment;165
25.6;5 Conclusion;165
25.7;Acknowledgments;166
25.8;References;166
26;20 A Study on the Connectivity Patterns of Individuals Within an Informal Communication Network;167
26.1;Abstract;167
26.2;1 Introduction;167
26.3;2 Theoretical Background;168
26.3.1;2.1 Organizational Learning and Organizational Communication Structure;168
26.4;3 Model;169
26.4.1;3.1 Entities and Their Individual Structural Importance;169
26.5;4 Experimental Setup;170
26.6;5 Results;170
26.7;6 Conclusion;172
26.8;References;172
27;21 Grid Connected Photovoltaic System Using Inverter;173
27.1;Abstract;173
27.2;1 Introduction;174
27.3;2 Parameter of PV Cell and I-V Characteristic Curves of Solar Cell;174
27.3.1;2.1 Short-Circuit Current;175
27.3.2;2.2 Maximum Power Point;175
27.4;3 Control of Grid Connected Power Converter;176
27.5;4 Conclusion;177
27.6;References;178
28;22 The Cluster Algorithm for Time-Varying Nonlinear System with a Model Uncertainty;179
28.1;Abstract;179
28.2;1 Introduction;179
28.3;2 The Input-Output Linearization for the Time-Varying Nonlinear System with an Uncertainty;180
28.4;3 The Proposed Algorithm;181
28.5;4 Simulation;182
28.6;5 Conclusion;183
28.7;References;183
29;23 Integrated Plant Growth Measurement System Based on Intelligent Circumstances Recognition;184
29.1;Abstract;184
29.2;1 Introduction;185
29.3;2 Related Studies;185
29.3.1;2.1 Growth of Plants;185
29.3.2;2.2 Growth Measurement;186
29.4;3 Growth Measurement System Based on Intelligent Circumstances Recognition;186
29.4.1;3.1 Image Processing;187
29.4.2;3.2 Image Analysis;187
29.4.3;3.3 Three-Dimensional Measurement of a Plant Using a Stereo Camera;188
29.4.4;3.4 Machine Learning;188
29.5;4 Conclusion;188
29.6;References;189
30;24 A Study on the Big Data Business Model for the Entrepreneurial Ecosystem of the Creative Economy;190
30.1;Abstract;190
30.2;1 Introduction;191
30.3;2 Related Research;192
30.4;3 Big Data Business Strategies in the Creative Economy;192
30.5;4 Conclusion;194
30.6;References;194
31;25 Implementation of Intelligent Decision-Based Smart Group Scheduler;196
31.1;Abstract;196
31.2;1 Introduction;197
31.3;2 Related Work;197
31.4;3 Requirement Analysis;198
31.4.1;3.1 Function-Related Requirement;198
31.4.2;3.2 Data-Related Requirement;199
31.4.3;3.3 Interface-Related Requirement;199
31.4.4;3.4 User-Related Requirement;199
31.5;4 System Structure;200
31.6;5 Conclusion;200
31.7;References;201
32;26 Implementation of MCA Rule Mapper for Cloud Computing Environments;202
32.1;Abstract;202
32.2;1 Introduction;203
32.3;2 Related Work;204
32.4;3 Requirement Analysis;205
32.4.1;3.1 Function-Related Requirement;205
32.4.2;3.2 Interface-Related Requirement;205
32.4.3;3.3 User-Related Requirement;205
32.5;4 System Structure;205
32.6;5 Module Specification;206
32.7;6 Anticipated Results and Contributions;206
32.8;References;207
33;27 A Simple Fatigue Condition Detection Method by using Heart Rate Variability Analysis;208
33.1;Abstract;208
33.2;1 Introduction;208
33.3;2 Related Work;209
33.4;3 Data Collection and Analysis;209
33.4.1;3.1 Time Domain Analysis;209
33.4.2;3.2 HRV Data of PPG Measuring Device;210
33.5;4 Results;211
33.6;5 Conclusion;213
33.7;References;213
34;28 Insider Detection by Analyzing Process Behaviors of File Access;214
34.1;Abstract;214
34.2;1 Introduction;215
34.3;2 Related Work;215
34.4;3 File Access Behavior Analysis;216
34.4.1;3.1 Dataset;216
34.4.2;3.2 File Access Behavior Analysis;217
34.4.2.1;3.2.1 File Path Dispersion;217
34.4.2.2;3.2.2 Operation Type;218
34.4.2.3;3.2.3 File Type;219
34.4.2.4;3.2.4 Velocity;219
34.4.3;3.3 File Access Behavior Model;220
34.5;4 Model Verification;220
34.5.1;4.1 OCSVM Based Semi-supervised Detection;221
34.5.2;4.2 K-Means Based Unsupervised Detection;221
34.5.3;4.3 Discussion;222
34.6;5 Conclusion and Future Work;223
34.7;Acknowledgements;223
34.8;References;223
35;29 Analysis of the HOG Parameter Effect on the Performance of Vision-Based Vehicle Detection by Support Vector Machine Classifier;225
35.1;Abstract;225
35.2;1 Introduction;225
35.3;2 HOG Descriptor Parameters;226
35.3.1;2.1 Computation Steps of HOG Features;226
35.3.2;2.2 Parameters of HOG Descriptor;227
35.4;3 HOG Parameter Space Exploration by Experiments;227
35.4.1;3.1 Experimental Setup;227
35.4.2;3.2 Experimental Results;228
35.5;4 Conclusion;231
35.6;References;232
36;30 A Fast Algorithm to Build New Users Similarity List in Neighbourhood-Based Collaborative Filtering;233
36.1;Abstract;233
36.2;1 Introduction;234
36.3;2 The TwinSearch Algorithm;235
36.3.1;2.1 Algorithm Design;235
36.3.2;2.2 Time Complexity Analysis;236
36.4;3 Experimental Evaluation;237
36.4.1;3.1 Datasets and Experimental Settings;237
36.4.2;3.2 Experimental Results;237
36.5;4 Conclusion;239
36.6;Acknowledgements;239
36.7;References;239mehr

Autor

Professor James J. Park received his Ph.D. degree in Graduate School of Information Security from Korea University, Korea. From December, 2002 to July, 2007, Dr. Park had been a research scientist of R&D Institute, Hanwha S&C Co., Ltd., Korea. From September, 2007 to August, 2009, He had been a professor at the Department of Computer Science and Engineering, Kyungnam University, Korea. 

Professor Gangman Yi received his master's degree in Computer Sciences at Texas A&M University, USA in 2007, and doctorate in Computer Sciences at Texas A&M University, USA in 2011. In May 2011, he joined System S/W group in Samsung Electronics, Suwon, Korea. He joined the Department of Computer Science & Engineering, Gangneung-Wonju National University, Korea, since March 2012. Dr. Yi has been researched in an interdisciplinary field of researches.
Professor Young Sik Jeong is Professor of Multimedia Engineering in Dongguk University. He received his master's degree in Computer Sciences at Korea University, Korea, and, doctorate in Computer Sciences at Korea University. His Research and Interests are 'IaaS cloud-based service technology for multimedia content processing', 'Big multimedia data processing in the mobile cloud-based technologies', 'Multimedia data processing middleware development in the WSN environment', 'CPS-related technology for multimedia content service', and 'Cloud service technologies for IoT based technical support'. 




Professor Hong Shen is Professor (Chair) of Computer Science in the School of Computer Science, University of Adelaide. He received his B.Eng. degree from Beijing University of Science
and Technology, M.Eng. degree from University of Science and Technology of China, Ph.Lic. and Ph.D. degrees from Abo Akademi University, Finland, all in Computer Science.