Learning from Data Streams (eBook)

Processing Techniques in Sensor Networks

João Gama, Mohamed Medhat Gaber (Herausgeber)

eBook Download: PDF

2007 | 2007
X, 244 Seiten
Springer Berlin (Verlag)
978-3-540-73679-0 (ISBN)

Infos

Processing data streams has raised new research challenges over the last few years. This book provides the reader with a comprehensive overview of stream data processing, including famous prototype implementations like the Nile system and the TinyOS operating system. Applications in security, the natural sciences, and education are presented. The huge bibliography offers an excellent starting point for further reading and future research.

Inhalt

Preface 5
Contents 7
Chapter 1 Introduction 11
1.1 Preamble 11
1.2 Book Overview 12
1.3 Roadmap 13
1.4 Final Remarks 14
References 14
Part I Overview 16
Chapter 2 Sensor Networks: An Overview 17
2.1 Sensing and Communicating 17
2.2 Current Sensor Network Technology 19
2.3 Communication Aspects 21
2.4 Distributed Compression and In-Network Computation 25
2.5 Summary and Concluding Remarks 30
Chapter 3 Data Stream Processing 33
3.1 Introduction 33
3.2 Data Stream Models 34
3.3 Basic Streaming Methods 36
3.4 Basic Streaming Algorithms 41
3.5 Emerging Challenges and Future Issues 45
References 46
Chapter 4 Data Stream Processing in Sensor Networks 48
4.1 Introduction 48
4.2 Classification of Data Stream Processing in Sensor Networks 49
4.3 Research Issues and Challenges 51
4.4 Summary 52
References 53
Part II Data Stream Management Techniques in Sensor Networks 56
Chapter 5 Data Stream Management Systems and Architectures 57
5.1 Introduction 57
5.2 Nile System Design 59
5.3 The Correctness Measure 63
5.4 The Progressive Evaluation of Sliding-Window Queries 64
5.5 Extensions 69
5.6 A Scalable Data Stream Management System 73
5.7 RelatedWork 75
5.8 Summary 76
References 76
Chapter 6 Querying of Sensor Data 78
6.1 Introduction 78
6.2 Types of Queries 79
6.3 Query Dissemination 81
6.4 Result Collection 84
6.5 Data-Centric Storage 88
6.6 Concluding Remarks 89
References 89
Chapter 7 Aggregation and Summarization in Sensor Networks 92
7.1 Introduction 92
7.2 Preliminaries and RelatedWork 95
7.3 Complex Queries in Sensor Networks 98
7.4 Aggregation in Lossy Networks 105
7.5 Conclusion and Future Directions 108
References 108
Chapter 8 Sensory Data Monitoring 111
8.1 Specifying Sensory Data Monitoring Goals 113
8.2 Identifying Significant Data: In-network processing 117
8.3 Accuracy: Identifying Sensing Errors 120
8.4 Summary 123
References 124
Part III Mining Sensor Network Data Streams 127
Chapter 9 Clustering Techniques in Sensor Networks 128
9.1 A Ubiquitous Streaming Setting 128
9.2 The Core of Clustering Procedures 129
9.3 Clustering Streaming Examples 132
9.4 Clustering Multiple Data Streams 135
9.5 Open Issues on Clustering Sensor Data Streams 142
References 143
Chapter 10 Predictive Learning in Sensor Networks 146
10.1 Introduction 146
10.2 General Issues 147
10.3 Centralized Approaches 154
10.4 Distributed Solutions 163
References 166
Chapter 11 Tensor Analysis on Multi-aspect Streams 168
11.1 Introduction 168
11.1 Introduction 168
11.2 Background 172
11.3 Problem Formulation 175
11.4 Window-Based Tensor Analysis 176
11.5 Performance Evaluation 182
11.6 Application and Case Study 183
11.7 RelatedWork 185
11.8 Conclusion 186
Part IV Applications 188
Chapter 12 Knowledge Discovery from Sensor Data for Security Applications 189
12.1 Introduction 189
12.2 Security Challenges 190
12.3 Disparate Data Exploitation 191
12.4 Case Study: Weigh Station Sensors 194
12.5 Closing Remarks 203
Chapter 13 Knowledge Discovery from Sensor Data For Scientific Applications 207
13.1 Introduction 208
13.2 A Broader Knowledge Discovery Framework 209
13.3 Weather, Climate, and Associated Natural Hazards 212
13.4 Challenges of Knowledge Discovery from Sensor Data for Natural Hazards 216
13.5 Knowledge Discovery Approaches forWeather, Climate and Associated Natural Hazards 219
13.6 The Significance of Utilizing Knowledge Discovery Insights for Hazards Mitigation 225
13.7 Closing Remarks 227
References 228
Chapter 14 TinyOS Education with LEGO MINDSTORMS NXT 232
14.1 Introduction 232
14.2 NXT Brick 233
14.3 LEGO Ecosystem 238
14.4 Proposing a TinyOS Educational Platform for NXT 241
14.5 Conclusion 241
Index 243

Produkt-Details

• Erscheint lt. Verlag: 20.9.2007
• Zusatzinfo: X, 244 p. 73 illus.
• Verlagsort: Berlin
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Netzwerke
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Themenwelt: Technik ► Elektrotechnik / Energietechnik
• Themenwelt: Technik ► Nachrichtentechnik
• Schlagworte: Architectures • Clustering • Data Stream Processing • Information • Knowledge Discovery • learning • LEGO MINDSTORMS • Memory • Mindstorms • Monitor • Predictive Learning • Sensor Data • sensor networks • Tensor Analysis • TinyOS • wireless sensor network • wireless sensor networks
• ISBN-10: 3-540-73679-4 / 3540736794
• ISBN-13: 978-3-540-73679-0 / 9783540736790