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Context-Aware Computing and Self-Managing Systems [Pehme köide]

  • Formaat: Paperback / softback, 408 pages, kõrgus x laius: 234x156 mm, kaal: 453 g
  • Ilmumisaeg: 02-Oct-2019
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 0367385848
  • ISBN-13: 9780367385842
Teised raamatud teemal:
  • Formaat: Paperback / softback, 408 pages, kõrgus x laius: 234x156 mm, kaal: 453 g
  • Ilmumisaeg: 02-Oct-2019
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 0367385848
  • ISBN-13: 9780367385842
Teised raamatud teemal:

Bringing together an extensively researched area with an emerging research issue, Context-Aware Computing and Self-Managing Systems presents the core contributions of context-aware computing in the development of self-managing systems, including devices, applications, middleware, and networks. The expert contributors reveal the usefulness of context-aware computing in developing autonomous systems that have practical application in the real world.





The first chapter of the book identifies features that are common to both context-aware computing and autonomous computing. It offers a basic definition of context-awareness, covers fundamental aspects of self-managing systems, and provides several examples of context information and self-managing systems. Subsequent chapters on context-awareness demonstrate how a context can be employed to make systems smart, how a context can be captured and represented, and how dynamic binding of context sources can be possible. The chapters on self-management illustrate the need for "implicit knowledge" to develop fault-tolerant and self-protective systems. They also present a higher-level vision of future large-scale networks.





Through various examples, this book shows how context-aware computing can be used in many self-managing systems. It enables researchers of context-aware computing to identify potential applications in the area of autonomous computing. The text also supports researchers of autonomous computing in defining, modeling, and capturing dynamic aspects of self-managing systems.

1 Context and Self-Management
1(14)
Waltenegus Dargie
1.1 Introduction
1(1)
1.2 Aspects of Self-Management
2(1)
1.3 Examples of Self-Managing Systems
3(2)
1.3.1 Self-Managing Chaotic Networks
3(1)
1.3.2 Recovery-Oriented Computing
4(1)
1.4 Context-Aware Computing
5(6)
1.4.1 Context-Awareness
5(2)
1.4.2 Surrounding Context
7(1)
1.4.3 Activity on a Street
8(1)
1.4.4 User's Attention in a Meeting
9(1)
1.4.5 Activity Context from Multiple Sensors
10(1)
1.4.6 IBadge
10(1)
1.4.7 Mediacup
11(1)
1.5 Context-Aware, Self-Managing Systems
11(1)
1.6 Organization of the Book
12(1)
References
12(3)
2 Verifying Nursing Activities Based on Workflow Model
15(28)
Noriaki Kuwahara
Naoki Ohboshi
Hiromi Itoh Ozaku
Futoshi Naya
Akinori Abe
Kiyoshi Kogure
2.1 Introduction
16(1)
2.2 Related Works
17(2)
2.3 Overview of Research Goals
19(2)
2.4 Case Study of Intravenous Medication Process Performed by Nurses
21(5)
2.4.1 Survey Method and Results
22(1)
2.4.2 Possible Solutions from Ubiquitous Computing Point of View
23(3)
2.5 Prototype of Ubiquitous Sensor Network System
26(3)
2.5.1 Experimental Room Description
26(1)
2.5.2 Location Tracking by IR-ID
27(1)
2.5.3 Activity Data Collection with Bluetooth-Based Wireless Accelerometers
27(2)
2.5.4 Feature Extraction for Activity Recognition
29(1)
2.6 Algorithm for Detecting Errors in Nursing Activities
29(5)
2.6.1 Nursing Workflow Model
30(1)
2.6.2 Error Detection Algorithm
31(3)
2.7 Testing Our Proposed Algorithm
34(3)
2.7.1 Data Correction Method for Recording History of Nursing Activities
35(1)
2.7.2 Test Results
35(2)
2.8 Conclusion and Future Works
37(1)
References
38(5)
3 A Taxonomy of Service Discovery Systems
43(36)
Vasughi Sundrarnoorthy
Pieter Hartel
Hans Scholten
3.1 Introduction
44(3)
3.2 Service Discovery: Third Generation Name Discovery
47(2)
3.3 Service Discovery Architecture
49(5)
3.3.1 Logical Topologies (Overlays)
50(1)
3.3.2 Non-Registry Topologies
50(1)
3.3.3 Registry-Based Topologies
51(3)
3.4 Service Discovery Functions
54(3)
3.5 Operational Aspects of Service Discovery
57(2)
3.6 State of the Art
59(7)
3.6.1 Small Systems
61(3)
3.6.2 Large Systems
64(2)
3.7 Taxonomy of State of the Art
66(6)
3.7.1 Taxonomy of State of the Art Solutions to Operational Aspects
66(2)
3.7.2 Taxonomy of Service Discovery Functions and Methods
68(4)
3.8 Conclusion
72(1)
References
73(6)
4 Managing Distributed and Heterogeneous Context for Ambient Intelligence
79(50)
Jose Viterbo
Markus Endler
Karin Breitman
Laurent Mazuel
Yasmine Charif
Nicolas Sabouret
Amal El Fallah Seghrouchni
Jean-Pierre Briot
4.1 Introduction
79(4)
4.1.1 Scenario
81(2)
4.1.2 Outline
83(1)
4.2 Fundamental Concepts
83(3)
4.2.1 Ambient Intelligence
83(1)
4.2.2 Context Awareness
83(1)
4.2.3 Ontology
84(1)
4.2.4 Context Reasoning
85(1)
4.3 Ontological Representation and Reasoning about Context
86(18)
4.3.1 Evaluation Criteria and Taxonomy
87(1)
4.3.2 Gaia
88(2)
4.3.3 CoBrA
90(2)
4.3.4 Semantic Space
92(1)
4.3.5 CHIL
93(2)
4.3.6 SAMOA
95(2)
4.3.7 CAMUS
97(2)
4.3.8 OWL-SF
99(2)
4.3.9 DRAGO
101(1)
4.3.10 Conclusion
102(2)
4.4 Approaches for Ontology Alignment
104(7)
4.4.1 Lexical Alignment
105(2)
4.4.2 Structural Approaches
107(1)
4.4.3 Instances-Based Approaches
107(1)
4.4.4 Mediated Approaches
108(1)
4.4.5 Alignment Based on Semantic Similarity
109(2)
4.4.6 Conclusion
111(1)
4.5 The Campus Approach
111(9)
4.5.1 Context Types
112(1)
4.5.2 Ontologies
113(1)
4.5.3 Reasoning
113(3)
4.5.4 Ontology Alignment
116(4)
4.6 Conclusion and Open Problems
120(2)
4.6.1 Discussion and Future Work
120(2)
References
122(7)
5 Dynamic Content Negotiation in Web Environments
129(48)
Xavier Sanchez-Loro
Jordi Casademont
Jose Luis Ferrer
Victoria Beltran
Marisa Catalan
Josep Paradells
5.1 Introduction
130(1)
5.2 Ubiquitous Web
131(11)
5.2.1 Related Concepts
133(2)
5.2.2 Protocols Overview
135(7)
5.3 A Proxy-Based Solution for the Detection of Device Capabilities
142(12)
5.3.1 System Description
143(8)
5.3.2 System Deployment
151(1)
5.3.3 Vocabulary
152(2)
5.4 Collaborative Optimization, Context Acquisition and Provisioning
154(16)
5.4.1 Application Layer Optimization
155(2)
5.4.2 System Description
157(3)
5.4.3 Header Restoring Policies and Context Provisioning
160(5)
5.4.4 Collaborative Device Capabilities Detection Service
165(3)
5.4.5 Optimization Results
168(2)
5.5 Conclusion
170(1)
References
171(6)
6 The Road towards Self-Management in Communication Networks
177(24)
Ralf Wolter
Bruno Klauser
6.1 Introduction
177(2)
6.2 Self-Management in Networks
179(5)
6.3 Defining Concrete Steps towards the Vision
184(13)
6.3.1 Define Business Objectives in a Business Language
184(1)
6.3.2 Translate the Business Objectives into Technical Terms
185(3)
6.3.3 Derive Rules and Policies for Systems
188(2)
6.3.4 Automatically Breakdown Goals
190(3)
6.3.5 Enable Network Elements to Interpret, Deploy, and Comply with These Goals
193(4)
6.4 Research Outlook
197(2)
References
199(2)
7 Policy-Based Self-Management in Wireless Networks
201(72)
Antonis M. Hadjiantonis
George Pavlou
7.1 Introduction, Background and State-of-the-Art
202(6)
7.1.1 Self-Management Concepts and Challenges
202(3)
7.1.2 Open Issues and Motivation
205(3)
7.2 Policies and Context for Self-Management
208(15)
7.2.1 Policy-Based Management (PBM) Principles
208(6)
7.2.2 Context and Context-Awareness
214(5)
7.2.3 Management of Wireless Ad Hoc Networks and Self-Management Capabilities
219(4)
7.3 A Framework for the Self-Management of Wireless Networks
223(20)
7.3.1 High Level Framework Overview and Design
224(1)
7.3.2 Policy-Based and Context-Aware Organizational Model
225(4)
7.3.3 Policy-Based Design for Autonomic Decision Making
229(4)
7.3.4 Context-Aware Platform for Information Collection and Modeling
233(3)
7.3.5 Distributed Policy and Context Repositories --- The Importance of Knowledge Management
236(2)
7.3.6 Context and Policies Interaction for Closed-Loop Autonomic Management
238(1)
7.3.7 Overview of Applicability and Policy Examples
239(4)
7.4 Implementation and Evaluation of Self-Management Capabilities
243(15)
7.4.1 Self-Configuration and Self-Optimization in Wireless Ad Hoc Networks
244(9)
7.4.2 Self-Configuration of a Distributed Policy Repository
253(3)
7.4.3 Self-Protection of User Privacy and Preferences
256(2)
7.5 Conclusions and the Future of Self-management
258(4)
7.5.1 Summary and Concluding Remarks
258(2)
7.5.2 Future Trends and Challenges
260(2)
7.6 Acknowledgments
262(1)
7.7 Abbreviations
262(2)
References
264(9)
8 Autonomous Machine Learning Networks
273(36)
Lei Liu
8.1 Introduction
274(3)
8.2 Problem Formulation
277(5)
8.2.1 Attack Prediction Problem
279(1)
8.2.2 Attack Class Discovery Problem
280(2)
8.3 Related Work
282(4)
8.4 Methodology
286(2)
8.5 Evaluation
288(3)
8.6 Experiment
291(10)
8.6.1 Data Samples
291(1)
8.6.2 Sample Reduction
292(3)
8.6.3 Initial Arbitrary Network
295(1)
8.6.4 Tuning
295(1)
8.6.5 Comparison of Class Prediction
295(6)
8.6.6 Comparison of Cluster Prediction
301(1)
8.7 Conclusions
301(1)
References
302(7)
9 Probabilistic Fault Management
309(40)
Jianguo Ding
Pascal Bouvry
Bernd J. Kramer
Haibing Guan
Alei Liang
Franco Davoli
9.1 Introduction
309(3)
9.2 Probabilistic Inference in Fault Management
312(8)
9.2.1 The Characteristics of the Faults in Distributed Systems
312(3)
9.2.2 Bayesian Networks for Fault Management
315(3)
9.2.3 Probabilistic Inference for Distributed Fault Management
318(2)
9.3 Prediction Strategies for Fault Management in Dynamic Networks
320(5)
9.3.1 Dynamic Characteristics in Networks
320(2)
9.3.2 Dynamic Bayesian Networks for Fault Management
322(1)
9.3.3 Prediction Strategies for Network Management
322(3)
9.4 Application Investigations for Probabilistic Fault Management
325(17)
9.4.1 Architecture for Network Management
325(3)
9.4.2 The Structure and Function of Fault Diagnosis Agent
328(12)
9.4.3 Discussion of Application Issues
340(2)
9.5 Conclusions
342(1)
References
342(7)
Index 349
Waltenegus Dargie