Context-Aware Computing and Self-Managing Systems

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Waltenegus Dargie
CRC Press, Mar 25, 2009 - Mathematics - 405 pages
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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.

 

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Contents

Context and SelfManagement
1
Verifying Nursing Activities Based on Workflow Model
15
A Taxonomy of Service Discovery Systems
43
Managing Distributed and Heterogeneous Context for Ambient Intelligence
79
Dynamic Content Negotiation in Web Environments
129
The Road towards SelfManagement in Communication Networks
177
PolicyBased SelfManagement in Wireless Networks
201
Autonomous Machine Learning Networks
273
Probabilistic Fault Management
309
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