W eric wong proceedings of the 4th international conference on computer engineering and networks CENet2014

W. Eric Wong
Editor

Proceedings of the
4th International
Conference on
Computer Engineering
and Networks
CENet2014


Proceedings of the 4th International Conference
on Computer Engineering and Networks



W. Eric Wong
Editor

Proceedings of the 4th
International Conference
on Computer Engineering
and Networks
CENet2014


Editor
W. Eric Wong
University of Texas at Dallas
Plano, TX, USA

ISBN 978-3-319-11103-2
ISBN 978-3-319-11104-9 (eBook)
DOI 10.1007/978-3-319-11104-9
Springer Cham Heidelberg New York Dordrecht London
Library of Congress Control Number: 2014957309
© Springer International Publishing Switzerland 2015
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Contents

Volume I
Part I

Algorithm Design

1

A Spatiotemporal Cluster Method for Trajectory Data . . . . . . . .
Yunbo Chen, Hongchu Yu, and Lei Chen

2

Use Case Points Method of Software Size Measurement
Based on Fuzzy Inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Yue Xie, Jilian Guo, and Anwei Shen

11

ATPG Algorithm for Crosstalk Delay Faults of High-Speed
Interconnection Circuits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Yuling Shang and Pei Zhang

19

3

4

Application of a Fuzzy-PID Control Method to Synchronized
Control of a Multistepping Motor . . . . . . . . . . . . . . . . . . . . . . . . .

Wenhao Shi, Lu Shi, Fang An, Zhouchang Wu,
Zhongxiu Weng, and Lianqing Zhao

3

27

5

The Improved Bayesian Algorithm to Spam Filtering . . . . . . . . .
Hongling Wang, Gang Zheng, and Yueshun He

37

6

Evolution of Community Structure in Complex Networks . . . . . .
Lei Zhang, Jianyu Li, Shuangwen Chen, and Xin Jin

45

7

Construction of a Thermal Power Enterprise
Environmental Performance Evaluation Model . . . . . . . . . . . . . .
Xiaofei Liao, Huayue Li, Weisha Yan, and Lin Liu

55

A Mobile Localization Algorithm Based

on SPSO Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Maoheng Sun and Azhi Tan

65

8

v


vi

9

Contents

A Fast and Accurate Algorithm of Subspace Spectrum
Peak Search Based on Bisection Method . . . . . . . . . . . . . . . . . . . .
Yu Wang, Hong Jiang, and Donghai Li

73

10

A CRF-Based Method for DDoS Attack Detection . . . . . . . . . . . .
Yu Wang, Hong Jiang, Zonghai Liu, and Shiwen Chen

11

Advanced SOM Algorithm Based on Extension Distance

and Its Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Haitao Zhang, Binjun Wang, and Guangxuan Chen

89

A Trilateral Centroid Localization and Modification
Algorithm for Wireless Sensor Network . . . . . . . . . . . . . . . . . . . .
Yujun Liu and Meng Cai

97

Simulation Study on Trajectory Tracking in Manipulator
Based on the Iterative Learning Control Algorithm . . . . . . . . . . .
Yanfen Luo

107

An Improved Gaussian Mixture Model
and Its Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Guang Han

115

GPU Acceleration for the Gaussian Elimination
in Magnetotelluric Occam Inversion Algorithm . . . . . . . . . . . . . .
Yi Xiao and Yu Liu

123

12

13

14

15

16

17

18

19

20

Artificial Neural Networks in Biomedicine
Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Jiri Krenek, Kamil Kuca, Aneta Bartuskova, Ondrej Krejcar,
Petra Maresova, and Vladimir Sobeslav

81

133

SOC Prediction Method of a New Lithium Battery
Based on GA-BP Neural Network . . . . . . . . . . . . . . . . . . . . . . . . .
Kai Guan, Zhiqiang Wei, and Bo Yin

141

Compressed Sensing for Channel State Information
(CSI) Feedback in MIMO Broadcast Channels . . . . . . . . . . . . . . .
Yuan Liu and Kuixi Chen

155

Implementation and Performance Evaluation
of the Fully Enclosed Region Upper Confidence Bound
Applied to Trees Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Lin Wu, Ying Li, Chao Deng, Lei Chen,
Meiyu Yuan, and Hong Jiang

163

A New Linear Feature Item Weighting Algorithm . . . . . . . . . . . .
Shiyuan Tian, Hui Zhao, Guochun Wang, and Kuan Dai

171


Contents

21

22

23

24

25

26

27

28

vii

Trust Value of the Role Access Control Model
Based on Trust . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Xiaohui Cheng and Tong Wang

179

Universal Approximation by Generalized Mellin
Approximate Identity Neural Networks . . . . . . . . . . . . . . . . . . . .
Saeed Panahian Fard and Zarita Zainuddin

187

Research and Application of Function Optimization
Based on Artificial Fish Swarm Algorithm . . . . . . . . . . . . . . . . . .
Meiling Shen, Li Li, and Dan Liu

195

Robust Hand Tracker Using Joint Temporal Weighted
Histogram Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Zhiqin Zhang, Fei Huang, and Linli Tan

201

Combination of User’s Judging Power and Similarity
for Collaborative Recommendation Algorithm . . . . . . . . . . . . . . .
Li Zhang, Yuqing Xue, and Shuyan Cao

209

An Improved Naı¨ve Bayes Classifier Method in Public
Opinion Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Yun Lin, Jie Wang, and Rong Zou

219

Overseas Risk Intelligence Monitoring Based on Computer
Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Peipei Su

227

Quality of Service-Based Particle Swarm Optimization
Scheduling in Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . .
Shuang Zhao, Xianli Lu, and Xuejun Li

235

Part II

Data Processing

29

Improving Database Retrieval Efficiency . . . . . . . . . . . . . . . . . . .
Shaomin Yue, Wanlong Li, Dong Han,
Hui Zhao, and Jinhui Cheng

30

Improving TCP Performance in Satcom Links
by Packet-Loss Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Yuan He, Minli Yao, and Xiong Xiong

31

Key Management Scheme in Cluster for WSNs . . . . . . . . . . . . . .
Xiaoming Liu and Qisheng Zhao

32

An Energy-Saving Method for Erasure-Coded Distributed
Storage System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Lei Yang and Shi Liu

245

253

263

271


viii

33

Contents

LF: A Caching Strategy for Named Data Mobile
Ad Hoc Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Li Zhang, Jiayan Zhao, and Zhenlian Shi

279

34

Topological Characteristics of Class Collaborations . . . . . . . . . . .
Dong Yan and Keyong Wang

35

Cluster Key Scheme Based on Bilinear Pairing for Wireless
Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Xiaoming Liu and Qisheng Zhao

299

The LDP Protocol Formal Description and Verification
Based on CPN Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Rengaowa Sa, Baolier Xilin, Yulan Zhao, and Neimule Menke

305

36

37

38

39

40

41

Self-Adaptive Anomaly Detection Method for Hydropower
Unit Vibration Based on Radial Basis Function (RBF)
Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Xueli An

323

Design and Implementation of Virtual Experiment
System Based on Universal Design . . . . . . . . . . . . . . . . . . . . . . . .
Yun Liu, Guoan Zhao, Dayong Gao, and Zengxia Ren

331

Effects of Information Services on Economic Growth
in Jilin Province . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Fang Xia, Bingbing Zhao, and Xiaochun Du

341

Day-Ahead Electricity Demand Forecasting
Using a Hybrid Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Zirong Li, Xiaohe Zhang, Yan Li, and Chun Liu

349

Membrane System for Decision-Making Problems . . . . . . . . . . . .
Lisha Han, Laisheng Xiang, and Xiyu Liu

43

Operational Model Management C/S System
Based on RUP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Rui Guo

45

315

A Fast Distribution-Based Clustering Algorithm
for Massive Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Xin Xu, Guilin Zhang, and Wei Wu

42

44

291

Decision Analysis Method Based on Improved Bayesian
Rough Set and Evidence Theory Under Incomplete
Decision System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Zhihai Yang, Weihong Yu, Yan Chen, and Taoying Li
An Enhanced Entropy-K-Nearest Neighbor Algorithm
Based on Attribute Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Lingyun Wei, Xiaoli Zhao, and Xiaoguang Zhou

357

365

371

381


Contents

46

47

48

49

50

51

ix

Synthetic Safety Analysis: A Systematic Approach
in Combination of Fault Tree Analysis and Fuzzy
Failure Modes and Effect Analysis . . . . . . . . . . . . . . . . . . . . . . . .
Guannan Su, Linpeng Huang, and Xiaoyu Fu

389

Evaluation Model of Internet Service Provider Attraction
Based on Gravity Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Lihua Heng, Gang Chen, and Zongmin Wang

399

An Application of Ecological Adaptation Evaluation
of Orthoptera in Daqinggou Nature Reserve Using SPSS . . . . . . .
Chunming Liu, Tao Meng, and Bingzhong Ren

407

Intelligent Diagnostics Applied Technology of Specialized
Vehicle Based on Knowledge Reasoning . . . . . . . . . . . . . . . . . . . .

Licai Bi, Yujie Cheng, Dong Hu, and Weimin Lv

415

On the Evaluation of Influence of Golf Websites
in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Fangzhi Liu

425

An Ensemble Learning Approach for Improving
Drug–Target Interactions Prediction . . . . . . . . . . . . . . . . . . . . . .
Ru Zhang

433

52

A Complementary Predictor for Collaborative Filtering . . . . . . .
Min Chen, Wenxin Hu, and Jun Zheng

53

Knowledge Discovery from Knowledge Bases
with Higher-Order Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Guangyuan Li

451

Numerical Analysis on High-Altitude Airdrop Impact

Processing of Water Bag . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hong Wang, Tao Xu, and Yahong Zhou

459

Heterogeneous Data Sources Synchronization
Based on Man-in-the-Middle Attack . . . . . . . . . . . . . . . . . . . . . . .
Yunze Wang and Yinying Li

467

54

55

56

Direct Forecast Method Based on ANN in Network
Traffic Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Congcong Wang, Gaozu Wang, Xiaoxiao Zhang,
and Shuai Zhang

Part III
57

443

477

Pattern Recognition

Visual Simulation of Three-Point Method Guidance
Trajectory for Antitank Missile . . . . . . . . . . . . . . . . . . . . . . . . . .
Mengchun Zhong, Cheng Li, and Hua Li

487


x

58

59

60

Contents

A Fast and Accurate Pupil Localization Method Using
Gray Gradient Differential and Curve Fitting . . . . . . . . . . . . . . .
Yuhui Lin, Zhiyi Qu, Yu Zhang, and Huiyi Han

495

A Method for the Chinese-Tibetan Machine Translation
System’s Syntactic Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Zangtai Cai

505

Analysis of Micro-Doppler Features of an Armored Vehicle
Based on EMD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Wanjun Zhang, Minjie Niu, and Xiaoying Wu

513

61

Kinect-Based 3D Color Reconstruction . . . . . . . . . . . . . . . . . . . . .
Li Yao, Guosheng Dong, and Guilan Hu

62

Application of Image Retrieval Based on the Improved
Local Binary Pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Zhen Sun, Xichang Wang, and Jiang Liu

63

Intelligent Detection of Complex Gaps in Live Working
Based on Video Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Yu Fan, Kangxiong Yu, Xiaoqing Tang, Heping Zheng,
Li Yu, and Ge Zhang

521

531

539

64

Face Detection Based on Landmark Localization . . . . . . . . . . . . .
Peng Liu, Songbin Li, Qiongxing Dai, and Haojiang Deng

65

Image Enhancement Using a Fractional-Order
Differential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Guo Huang, Li Xu, Qingli Chen, and Tao Men

555

An Improved Fractional Differential Method for Image
Enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Qingli Chen and Guo Huang

565

On Qualitative Analysis of High-Contrast Patches
in Range Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Qingli Yin

573

An Improved Method of Tracking and Counting
Moving Objects Using Graph Cuts . . . . . . . . . . . . . . . . . . . . . . . .
Mingjie Zhang and Baosheng Kang

583

Automatic Detection of Pharyngeal Fricatives in Cleft
Palate Speech . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Yan Xiao and Mangui Liang

591

Improved Chinese Word Segmentation Disambiguation
Model Based on Conditional Random Fields . . . . . . . . . . . . . . . .
Fanjin Mai, Shitong Wu, and Taoshi Cui

599

66

67

68

69

70

547


Contents

71

Mobile Real-Time Monitoring System
Based On Human Action Recognition . . . . . . . . . . . . . . . . . . . . . .
Lin Chai, Zhiqiang Wei, and Zhen Li

xi

607

72

A Kind of Image Classification Method Study . . . . . . . . . . . . . . .
Guoqing Wu, Bingheng Yang, and Liang Lv

73

Weld Pool Image Processing and Feature Extraction
Based on the Vision of the CO2 Welding . . . . . . . . . . . . . . . . . . . .
Xiaogang Liu and Xiaowei Ji

625

On Energy Distribution Characteristics
of Froth Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Yanpeng Wu, Xiaoqi Peng, Yanpo Song, and Qian Jiang

635

Classification Performances of Extreme Learning
Machine with Choquet Integral . . . . . . . . . . . . . . . . . . . . . . . . . .
Aixia Chen, Zhiyong Liang, and Zhen Guo

643

Facial Expression Recognition Using
Color-Depth Cameras . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Kezhen Xie, Zhen Li, and Zhiqiang Wei

649

74

75

76

77

78

79

80

81

82

Parallel Pipeline Implementation for Moving
Objects Detection in Traffic Video Analysis
on a Heterogeneous Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Teng Li, Yong Dou, Jingfei Jiang, and Peng Qiao

615

659

A Novel Optical Flow Algorithm Based on Bionic Features
for Robust Tracing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Weiwu Ren, Xiao Chen, Xiaoming Wang, and Mingyang Liu

669

An Obstacle Detection System for a Mobile Robot
Based on Radar-Vision Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . .
Xiao Chen, Weiwu Ren, Mingyang Liu, Lisheng Jin, and Yue Bai

677

De-noising Method for Echocardiographic Images
Based on the Second-Generation Curvelet Transform . . . . . . . . .
Haihong Xue, Binjin Chen, Kun Sun, and Jianguo Yu

687

A Novel Method for Image Segmentation Using Pulse-Coupled
Neural Network Based on Root Mean Square
of Gray Scale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hongliang Shi, Jian Rong, and Xinmin Zhou
Combined Similarity-Based Spectral Clustering Ensemble
for PolSAR Land Cover Classification . . . . . . . . . . . . . . . . . . . . .

Lu Liu, Dong Sun, and Junfei Shi

695

705


xii

83

84

Contents

An Algorithm for Human Face Detection in Color Images
Based on Skin Color Segmentation . . . . . . . . . . . . . . . . . . . . . . . .
Chunqiang Zhu
A Social Network Service-Based Environment Monitoring
System in Home . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Jiajin Zhang, Lichang Chen, Quan Gao, Zhaobo Huang,
Lin Guo, and Yanxin Yang

713

719

Volume II
Part IV

Cloud Computing

85

Cloud Computing Security Issues and Countermeasures . . . . . . .
Ziqian Xiao and Jingyou Chen

86

A Time-Aware QoS Prediction Approach to Web Service
Recommendation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Xuejie Zhang, Zhijian Wang, Weijian Zhang, and Fang Yang

739

Design Issue and Performance Analysis of Data Migration
Tool in a Cloud-Based Environment . . . . . . . . . . . . . . . . . . . . . . .
Shin-Jer Yang, Chung-Chih Tu, and Jyhjong Lin

749

87

88

89

90

91

92

93

Fuzzy Time Series Forecasting Algorithm Based
on Maximum Interval Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Che Liu, Yunfei Zhang, Fang Yang, Wenhuan Zhou,
and Xin Lv

731

761

Assessing the Effectiveness of Cloud Computing in European
Countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Petra Maresˇova´ and Kamil Kucˇa

769

Coordination Strategies in a Cloud Computing Service
Supply Chain Under the Duopoly Market . . . . . . . . . . . . . . . . . . .
Lingyun Wei, Xiaohan Yang, and Xiaoguang Zhou

777

A Novel Approach to Trust-Aware Service
Recommendation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Guoqiang Li, Lejian Liao, Dandan Song, Zhenling Zhang,
and Jingang Wang

A Web Service Discovery Method Based on Data
Segmentation and WordNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Tingna Liu and Ling Jiang
OpenSource Automation in Cloud Computing . . . . . . . . . . . . . . .
Vladimir Sobeslav and Ales Komarek

787

797
805


Contents

94

95

96

97

98

99

Utilization of Cloud Computing in Education with Focus
on Open-Source Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . .
Vladimir Sobeslav, Josef Horalek, and Jakub Pavlik
A Survey of Extended Role-Based Access Control

in Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hongjiao Li, Shan Wang, Xiuxia Tian, Weimin Wei,
and Chaochao Sun

101

102

103

104

813

821

Coordination Strategy in an SaaS Supply Chain
with Asymmetric Information About the Market . . . . . . . . . . . .
Lingyun Wei, Jiafei Ling, and Xiaoguang Zhou

833

One More Efficient Parallel Initialization Algorithm
of K-Means with MapReduce . . . . . . . . . . . . . . . . . . . . . . . . . . .
Bingliang Lu and Shuchao Wei

845

Equipment Information Management System Based
on Web Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Gangguo Li, Wu Qin, Tingyi Zhou, Yang wang,
and Xiaofeng Zhu
Maximal Service Profit in MAS-Based Cloud Computing
Considering Service Security . . . . . . . . . . . . . . . . . . . . . . . . . . .
Shengji Yu, Hongyu Chen, and Yanping Xiang

Part V
100

xiii

853

861

Embedded Systems

Protection Circuit Design of Lithium-Ion Battery
Pack Based on STM32 Processor . . . . . . . . . . . . . . . . . . . . . . . .
Hongtao Zhang, Fen Wu, Hang Zhou, Xiaoli Peng,
Chunhua Xiao, and Hui Xu

871

Analysis of an Intelligent 1553B-Bus Communication
Module Design Based on ARM Platform . . . . . . . . . . . . . . . . . .
Chunlei Song

879

Design of Multichannel Data Real-Time Processing
System Based on Serial Port Communication . . . . . . . . . . . . . . .
Peigang Jia and Sirui He

889

ZigBee-Based Online Dust-Concentration
Monitoring System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Hui Chao and Wang Zhou

897

The Application of WeChat to the University Laboratory
Management Information System . . . . . . . . . . . . . . . . . . . . . . . .
Jiangsheng Zhao and Xi Huang

907


xiv

105

106

107

108

109

110

111

112

Contents

A Short Loop Queue Design for Reduction of Power
Consumption of Instruction-Fetching Based on the Dynamic
Branch Folding Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Wei Li and Jianqing Xiao
Chip Design of a Continuous-Time 5-MHz Low-Pass
Sigma-Delta Modulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Jhin-Fang Huang, Jiun-Yu Wen, and Wei-Chih Chen

925

A Fine-Grained Power Gating Technique for Reducing
the Power Consumption of Embedded Processor . . . . . . . . . . . .
Wei Li and Jianqing Xiao

935

Kinematic Simulation for Series-Parallel Combination
Laser Machine Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Zhiqin Qian, Jiawen Wang, Lizong Lin, and Qun Cao

943

Implementation of a Fine-Grained Parallel Full Pipeline
Schnorr–Euchner Sphere Decoder Algorithm Accelerator
on Field-Programmable Gate Array . . . . . . . . . . . . . . . . . . . . . .
Shijie Li, Lei Guo, Yong Dou, and Jingfei Jiang

953

The Bandpass Sigma-Delta Modulator with Converter
Chip Design for Positron Emission Tomography Front-End
Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Wen-Cheng Lai, Jhin-Fang Huang, Kun-Jie Huang,
and Pi-Gi Yang

963

Simulation Testing Apparatus and Method for Embedded
System Based on Universal Serial Bus Host . . . . . . . . . . . . . . . .
Xin Li, Lingping Chen, Rentai Chen, and Shengwen Jiang

971

Simple Simulation of Abandoned Farmland Based
on Multiagent Modeling Approach . . . . . . . . . . . . . . . . . . . . . . .
Xuehong Bai, Lihu Pan, Huimin Yan, and Heqing Huang

979

113

Parallel Parity Scheme for Reliable Solid-State Disks . . . . . . . . .
Jianbin Liu, Hui Xu, Hongshan Nie, Hongqi Yu, and Zhiwei Li

114

Design of Tibetan Latin Transliteration System
in Unicode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Xiaoying Chen, Jinyong Ai, and Xiaodan Guo

115

917

987

995

Graphical User Interface Reliability Prediction Based
on Architecture and Event Handler Interaction . . . . . . . . . . . . . 1003
Zhifang Yang, Sanxing Yang, Zhongxing Yu,
Beibei Yin, and Chenggang Bai


Contents

xv

116

An Energy-Efficient Dual-Level Cache Architecture

for Chip Multiprocessors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1011
Mian Lou, Longsheng Wu, Senmao Shi, and Pengwei Lu

117

The Performance Optimization of Component-Based
System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1019
Daisen Wei, Xueqing Li, and Longye Tang

118

Microblog Data Parallel Monitoring Algorithm
on Compute Unified Device Architecture . . . . . . . . . . . . . . . . . . 1027
Yunpeng Cao and Haifeng Wang

119

Universal Central Control of Home Appliances as
an Expanding Element of Smart Home Concepts . . . . . . . . . . . . 1035
Jan Dvorak, Ondrej Berger, and Ondrej Krejcar

120

Lightweight Optimization of Android Permission
Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1043
Peixin Que, Xiao Guo, and Zhen Wang

121

Modeling of Virtual Electrical Experiment . . . . . . . . . . . . . . . . . 1051

Yinling Zhang, Deti Ji, and Renyou Zhang

122

Human Intervention Intelligent Flight Path Planning
on Unexpected Threats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1059
Peng Ren, Xiaoguang Gao, and Jun Chen

Part VI

Network Optimization

123

Effectiveness Analysis of Communications Jamming
to Battlefield Ad Hoc Network . . . . . . . . . . . . . . . . . . . . . . . . . . 1071
Sijia Lou, Jun He, and Wei Song

124

Research and Outlook on Wireless Channel Models . . . . . . . . . . 1079
Yuqing Wang, Cuijie Du, Xiujuan Han, Yuxin Qin,
and Hongqi Wang

125

Defending Against Whitewashing Attacks in Peer-to-Peer
File-Sharing Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1087
Weimin Luo, Jingbo Liu, Jiang Xiong, and Ling Wang

126

A New Type of Metropolitan Area Network . . . . . . . . . . . . . . . . 1095
Chuansheng Wu, Yunqiu Shi, and Shicheng Zhao

127

Improved Hierarchical Routing Scheme Based on Game
Theory in Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . 1103
Wan Qiang Han, Hai Bin Wu, and Zhi Jia Lu


xvi

Contents

128

An Item-Based Collaborative Filtering Framework
Based on Preferences of Global Users . . . . . . . . . . . . . . . . . . . . . 1113
Chengchao Li, Pengpeng Zhao, Jian Wu, Jiumei Mao,
and Zhiming Cui

129

Disassortativity of Class Collaboration Networks . . . . . . . . . . . . 1121
Dong Yan, Keyong Wang, and Maolin Yang

130

Online Social Networks Based on Complex Network
Theory and Simulation Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 1129
Xin Jin, Jianyu Li, and Lei Zhang

131

Analysis of Network Accessibility . . . . . . . . . . . . . . . . . . . . . . . . 1139
Shuijian Zhang and Ying Zhang

132

Mobile Botnet Propagation Modeling in Wi-Fi Networks . . . . . . 1147
Na Li, Yanhui Du, and Guangxuan Chen

133

Dynamic Evaluation of Suppliers for Industrial Value
Chain Value-Added Service Platform . . . . . . . . . . . . . . . . . . . . . 1155
Hua Pan and Linfu Sun

134

Energy-Saving Mechanisms for Delay- and
Disruption-Tolerant Networks . . . . . . . . . . . . . . . . . . . . . . . . . . 1165
Yankun Feng and Xiangyu Bai

135

Improved Sensor-MAC Protocol for Wireless
Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1177

Jian Di and Zhijun Ma

136

An Approach of Analyzing Transmission Capacity
of Multi-hop Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . 1185
Shaoqing Wang, Kai Cui, and Ning Zhou

137

A Rapid Payload-Based Approach for Social Network
Traffic Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1195
Yanping Li and Yabin Xu

138

Performance Prediction of WSNs’ Mobile Nodes Based
on GM-Markov Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1207
Xiaohui Cheng, Jinzhou He, and Qiliang Liang

139

Virtual Network Mapping Algorithm Based on Bi-level
Programming Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1215
Mingchun Zheng, Xinxin Ren, Xiao Li, Panpan Zhang,
and Xuan Liu


Contents

xvii

140

Kalman Filter-Based Bandwidth and Round Trip Time
Estimation for Concurrent Multipath Transfer
Performance Optimization in SCTP . . . . . . . . . . . . . . . . . . . . . . 1225
Wen Li, Wenbo Wang, Xiaojun Jing, and Wen Liu

141

Common-Knowledge and Cooperation Management I . . . . . . . . 1235
Takashi Matsuhisa

142

Detection of Topic Communities in Social Networks
Based on Tri-LDA Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1245
Wei Ou, Zanfu Xie, Xiping Jia, and Binbin Xie

143

Communications and Quality Aspects of Smart Grid
Network Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1255
Vladimir Sobeslav and Josef Horalek

144

An Adaptive Greedy Geographical Routing Protocol
for Mobile Multihop Wireless Network . . . . . . . . . . . . . . . . . . . . 1263

Feng Liu, Jianli Li, Gong Qin, and Fanhua Kong

145

A Coverage-Enhancing Algorithm Based on Local Virtual
Force Equilibrium for Wireless Sensor Networks . . . . . . . . . . . . 1273
Yujian Wang and Kaiguo Qian

146

Network Node Importance Measurement Method
Based on Vulnerability Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 1281
Yahui Li, Hongwa Yang, and Kai Xie

147

Modeling for Information Transmission of Consumer
Products Quality and Safety Based on the Social
Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1291
Yingcheng Xu, Xiaohong Gao, Ming Lei, Huali Cai,
and Yong Su

148

A Multi-classifier-Based Multi-agent Model
for Wi-Fi Positioning System . . . . . . . . . . . . . . . . . . . . . . . . . . . 1299
Shiping Zhu, Kewen Sun, and Yuanfeng Du


Part I

Algorithm Design


Chapter 1

A Spatiotemporal Cluster Method
for Trajectory Data
Yunbo Chen, Hongchu Yu, and Lei Chen
Abstract As positioning and communication technologies become more widespread, the production of large amounts of different types of trajectory data and
the extraction of useful information from mass trajectory data have emerged as hot
issues in data mining. This paper presents a trajectory data processing method
featuring simple operation, high precision, and strong practicability. For
low-precision trajectory data that are discrete but contain time information, a
clustering algorithm is proposed to extract information from such data. The algorithm can detect a point of interest (POI) in trajectory data by setting space and time
thresholds. Trajectory data collected from a taxi using a global positioning system
in Kunming, China, are used as experimental data. We conduct an experiment to
detect a POI in the collected trajectory data and carry out a visual analysis of these
special positions. The experimental results show the effectiveness of the algorithm,
which can in addition compress trajectory data.
Keywords Trajectory data • Data mining • Spatiotemporal cluster

1.1

Introduction

Orientation technology and information communication technology have reached
the stage in their development where they allow for the tracking, in real time, of
dynamic objects, resulting in a huge amount of trajectory data. For example, taxi
companies, bus companies, and government agencies have installed global positioning systems (GPS) on vehicles to monitor and manage them. To track vehicles

continuously, the systems send location information by GPS for continuous acquisition to a vehicle management control center. When a person’s mobile phone
communicates with the mobile communication base station, the mobile communication services will collect data on the user’s location and reconstruct her trajectory
Y. Chen
Kunming Urban Planning and Information Center, 650500 Kunming, China
H. Yu (*) • L. Chen
Faculty of Land and Resource Engineering, Kunming University of Science and Technology,
650500 Kunming, China
e-mail:
© Springer International Publishing Switzerland 2015
W.E. Wong (ed.), Proceedings of the 4th International Conference on Computer
Engineering and Networks, DOI 10.1007/978-3-319-11104-9_1

3


4

Y. Chen et al.

based on the data. In this article, time-continuous position data are called trajectory
data and may be used to record the trajectories of people or objects. Vehicle
management centers and mobile communication service providers generally delete
data regularly or irregularly, and large amounts of trajectory data contain a wealth
of information.
At present, with the increasing application of positioning technology, the accumulation of large amounts of trajectory data provides opportunities for mining
useful information from the data, but the mining of such information presents a
challenge. On the basis of an in-depth analysis of the characteristics of trajectory
data, this paper proposes and implements a processing algorithm for GPS trajectory
data that only requires setting time and space thresholds to detect special positions
in trajectories; in addition, the algorithm can compress raw trajectory data.

Some academics called, for example, trajectory data for location history data [1],
time-stamped position data, and GPS traces [2, 3]. Studies on such data approach
the topic mainly from the following three viewpoints. (1) Data expression:
Hangerstrand first introduced the concept of time trajectory and space–time prism
to analyze a human migration model [4]. With time as the variable axis, people’s
space as the dependent variable axis, using migration records by government
statistics or artificial records, he was able to visualize human migration on a
three-dimensional coordinate axis. Usually, the trajectory is simply expressed as a
series of coordinate pairs: [(x1, y1, t1), . . ., (xn, yn, tn)] , (x1, y1) 2 space plane,
t1 < t2 < Á Á Á < ti Á Á Á < tn, i 2 1 . . . n. (2) Data compression: because of the continuously changing spatial position of a moving object, simply recording and transmitting all the positioning data will reduce the performance of the system, so
researchers have studied trajectory data compression algorithms with the aim of
effectively updating the location of moving objects. (3) Data application: these are
primarily applied in mobile communication network optimization and locationbased services (LBSs) to provide services in connection with mobile communication in moving users when a user’s location needs to be tracked and even to predict
the location of users to optimize service and reduce the network load. The premise
of the LBS is to know the locations of users, so it needs to track them. In 2010, at the
Asian Microsoft Research Institute, Dr. Xie Xin and coworkers launched a research
project called GeoLife [5, 6]. This project initially carries GPS data collected daily
to conduct research from the following three points of view: to understand a user’s
life trajectory by deducing travel modes; to obtain an in-depth understanding of
users by estimating their travel experiences; to understand the location environment
by predicting the user’s points of interest (POIs). The results can be applied to the
following areas: life experience sharing based on GPS trajectories; common
regional travel recommendations, such as, for example, interesting places and travel
experts; personalized locations or recommendations of friends.
Based on the foregoing discussion, on the one hand, most current studies focus
on specific applications to solve specific problems; on the other hand, less research
is being conducted on trajectory data processing methods and existing processing
methods because current multilevel segmentation algorithms have certain limitations with respect to accuracy and practicability. The application analysis of

1 A Spatiotemporal Cluster Method for Trajectory Data

5

trajectory data is inseparable from the extraction of POIs. Because the coordinates
of POIs produced by user access to the same location change frequently, direct
analysis of such POIs is infeasible; thus, an algorithm needs to cluster POIs
extracted from trajectory data that distribute the adjacent POIs to the same cluster,
which (the cluster)is carried out to replace the POI for analysis. In view of this, this
paper attempts to provide a spatio-temporal clustering processing method for
trajectory data. First, the method makes it possible to extract meaningful positions
or temporal events related to the object of study and to compress vast amounts
of data.
The chapter is organized as follows. Section 2 presents related concepts and
algorithms, Sect. 3 uses the proposed algorithm in an experiment and in the
visualization analysis of GPS vehicle trajectory data, and the last section presents
conclusions and future prospects.

1.2

Principle Explanation

The moving of objects in space is usually considered an function (generally for a
two-dimensional location) which reflects the changes of objects’ spatial position
over time. In a particular plane coordinate system, the spatial location of an object is
represented as a coordinate pair (x, y), and the trajectory of the object can be
expressed as a function f(t) ¼ (x, y),where f is a continuous function and t is time.
In real life, we usually take samples from the coordinates of moving objects in
spatiotemporal coordinates to express the approximate trajectory [7]. The general
sampling methods are as follows:

Time-based sampling: by setting a sampling time interval (e.g., 30 s) in the motion
process, the spatial location of objects are recorded once every 30 s. In vehicle
monitoring, this is the majority of the sampling mode, typically using a GPS for
the position.
Change-based sampling: in motion processing, when changes take place, the
position is recorded, such as the position of an object when the object movement
direction changes.
Location-based sampling: when an object is close to the sensors which were placed
beforehand, the sensors will record the object’s position, such as, for example,
when scanning objects in a specific location and the delivery and transfer of
goods in logistics.
Event-based sampling: the establishment of a position triggered by some event,
such as a phone call, or answering when a mobile communication company
locates a customer in order to establish a communication link.
The experimental data in this paper are GPS vehicle trajectory data. A taxi sends
data to a monitoring center every 15 s to monitor and manage the vehicle. This type
of data acquisition is based on the first method of data sampling, which uses short
sampling intervals, features complete data, is not subject to accidental factors, and


6

Y. Chen et al.

Fig. 1.1 Trajectory sampling of something, points of interest, and interest region sample

makes it easy to analyze people’s patterns of travel in traffic. Regardless of the
sampling method used, it is necessary to obtain a set of discrete points. For the sake
of convenience, discrete points are represented as three-dimensional coordinates
(t, x, y): the spatiotemporal location of the object, which we call the anchor point,

and (x, y), which refer to the location and time. Thus, the trajectory of object A can
be represented as a collection of c ¼ {(ti, xi, yi)|i 2 N+}, where i represents discrete
points sorted by time sequence number and (ti, xi, yi) are the location of the ith point
of the trajectory. It is worth noting that the location of the anchor point is generally
subject to error because the size is related to the method used to determine the
position.
This paper proposes a method of space–time clustering to extract vehicle POIs,
defined as a moving person or object, at a location for more than a limited time, to
rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
2
À
Á2 
satisfy
xj À xi þ yj À yi
ΔD, tj À ti ! Δt, where ΔD refers to the free
space distance threshold and Δt refers to the time interval threshold. Because of the
multiscale nature of human activities, different spatial distance and time thresholds
are set to detect activity on different scales. In the extraction of residential travel
POIs, the spatial distance threshold is generally 400 or 500 m, while the time
threshold is generally 30 min [8]. Considering the accuracy of GPS positioning
and cluster rationality, this paper adopts time thresholds of 5, 10, and 30 min and
spatial distance thresholds of 200, 400, and 800 m for testing and comparison and,
ultimately, we selected a distance threshold value of 800 m and a time threshold of
30 min. Figure 1.1 shows the sampling of a trajectory generated from a person or an
object moving in space. The arrows indicate the direction of movement, and the
black points denote sampling trajectory points based on people or objects in the
process of moving in an uneven distribution of the location of the space. We believe
that the POI has a special meaning for the object, the red spots in the map are POIs,
With further considering the geographical region around the POIs in a certain
range, draw the dotted line circle as a region of interest.

1 A Spatiotemporal Cluster Method for Trajectory Data

7

Fig. 1.2 Flow chart of POI extraction

For the POIs having a special meaning for the object, we designed an algorithm
to extract POIs from trajectory data. The objective is to cluster the spatiotemporal
data. Here, I is the anchor point number, ti the time attribute of anchor points
labeled I, td the total time difference, s the starting point of clustering, J the
clustering endpoint, and MaxD the maximum distance between the starting point
and the endpoint. Ts is the time threshold, DS the space threshold, N the final
anchor point, and POI the collection of POIs. The algorithm procedure is shown in
Fig. 1.2.

1.3

Experiments

In this paper, the proposed algorithm was implemented in Visual Studio 2010, with
the results visually displayed and analyzed. First, the experimental data are
presented, followed by data preprocessing; then the proposed clustering algorithm
is used to analyze and process the data; finally, the results obtained by the proposed
clustering algorithm are subject to visual analysis in the ArcGIS platform.


8

Y. Chen et al.

Table 1.1 Taxi tracking data example
Stime

Latitude

Longitude

Speed

Orientation

State

2 February 2011 00:00:09
2 February 2011 00:00:24
2 February 2011 00:00:39
2 February 2011 00:00:54
2 February 2011 00:01:10

25040101
25037901
25035863
25034553
25033483

102732921
102732930
102732900

102732860
102732403

51
58
48
10
46

89
90
90
91
114

263
263
263
775
65,799

1.3.1

Introduction of Data

The data in our study were provided by a taxi company in the city of Kunming,
China. The company installed a GPS on a vehicle with the vehicle position data
transmitted to the monitoring center every 15 s. The data included the time,
longitude, latitude, vehicle running speed and angle, and passenger capacity.
Table 1.1 displays the GPS taxi daily travel trajectory data fragments. Of these,

stime is the acquisition of the anchor point time, here Beijing time, accurate to the
second; the format is years–month–day–points–seconds; latitude means latitude,
and longitude means longitude, and latitude and longitude expanded one million
times in the original data; speed is the instantaneous velocity, in kilometers per
hour, of the taxi in operation; orientation is the direction of the taxi runtime in
degrees; and state is the carrying capacity. The GPS positioning precision is 3–
10 m, but the vehicle running speed and angle only have a reference value because
in most of the time, they are not accurate. Experiments were carried out on a total of
9,579 data points during the week of the study.

1.3.2

Preprocessing

Prior to analysis, the preprocessing made original data satisfy the following
processing requirements. (1) Abnormal data elimination: typical noise may shift
positioning data, that is, the positioning data change a great deal in the inner space
in a short period of time. This kind of noise, caused by unstable GPS signals, is
removed as abnormal data prior to analysis. (2) Coordinate conversion: the location
of the original data is expressed in the geographical coordinates of latitude and
longitude, which is not conducive for calculation of the distance or for integration
with the geographic base map, so the analysis should be carried out after the unified
coordinate transformation of the data. (3) Data visualization: a GPS track record is
not appropriate for intuitive analysis and needs to be visualized; for this reason, it is
necessary to connect the track points based on the time sequence so as to reconstruct
the vehicle trajectory, as shown in Fig. 1.3.