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1 Context and Self-Management |
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1 | (14) |
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1 | (1) |
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1.2 Aspects of Self-Management |
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2 | (1) |
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1.3 Examples of Self-Managing Systems |
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3 | (2) |
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1.3.1 Self-Managing Chaotic Networks |
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3 | (1) |
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1.3.2 Recovery-Oriented Computing |
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4 | (1) |
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1.4 Context-Aware Computing |
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5 | (6) |
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5 | (2) |
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1.4.2 Surrounding Context |
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7 | (1) |
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1.4.3 Activity on a Street |
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8 | (1) |
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1.4.4 User's Attention in a Meeting |
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9 | (1) |
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1.4.5 Activity Context from Multiple Sensors |
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10 | (1) |
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10 | (1) |
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11 | (1) |
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1.5 Context-Aware, Self-Managing Systems |
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11 | (1) |
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1.6 Organization of the Book |
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12 | (1) |
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12 | (3) |
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2 Verifying Nursing Activities Based on Workflow Model |
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15 | (28) |
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16 | (1) |
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17 | (2) |
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2.3 Overview of Research Goals |
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19 | (2) |
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2.4 Case Study of Intravenous Medication Process Performed by Nurses |
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21 | (5) |
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2.4.1 Survey Method and Results |
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22 | (1) |
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2.4.2 Possible Solutions from Ubiquitous Computing Point of View |
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23 | (3) |
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2.5 Prototype of Ubiquitous Sensor Network System |
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26 | (3) |
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2.5.1 Experimental Room Description |
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26 | (1) |
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2.5.2 Location Tracking by IR-ID |
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27 | (1) |
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2.5.3 Activity Data Collection with Bluetooth-Based Wireless Accelerometers |
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27 | (2) |
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2.5.4 Feature Extraction for Activity Recognition |
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29 | (1) |
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2.6 Algorithm for Detecting Errors in Nursing Activities |
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29 | (5) |
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2.6.1 Nursing Workflow Model |
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30 | (1) |
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2.6.2 Error Detection Algorithm |
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31 | (3) |
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2.7 Testing Our Proposed Algorithm |
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34 | (3) |
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2.7.1 Data Correction Method for Recording History of Nursing Activities |
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35 | (1) |
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35 | (2) |
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2.8 Conclusion and Future Works |
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37 | (1) |
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38 | (5) |
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3 A Taxonomy of Service Discovery Systems |
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43 | (36) |
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44 | (3) |
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3.2 Service Discovery: Third Generation Name Discovery |
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47 | (2) |
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3.3 Service Discovery Architecture |
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49 | (5) |
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3.3.1 Logical Topologies (Overlays) |
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50 | (1) |
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3.3.2 Non-Registry Topologies |
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50 | (1) |
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3.3.3 Registry-Based Topologies |
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51 | (3) |
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3.4 Service Discovery Functions |
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54 | (3) |
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3.5 Operational Aspects of Service Discovery |
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57 | (2) |
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59 | (7) |
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61 | (3) |
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64 | (2) |
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3.7 Taxonomy of State of the Art |
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66 | (6) |
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3.7.1 Taxonomy of State of the Art Solutions to Operational Aspects |
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66 | (2) |
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3.7.2 Taxonomy of Service Discovery Functions and Methods |
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68 | (4) |
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72 | (1) |
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73 | (6) |
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4 Managing Distributed and Heterogeneous Context for Ambient Intelligence |
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79 | (50) |
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Amal El Fallah Seghrouchni |
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79 | (4) |
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81 | (2) |
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83 | (1) |
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83 | (3) |
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4.2.1 Ambient Intelligence |
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83 | (1) |
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83 | (1) |
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84 | (1) |
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85 | (1) |
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4.3 Ontological Representation and Reasoning about Context |
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86 | (18) |
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4.3.1 Evaluation Criteria and Taxonomy |
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87 | (1) |
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88 | (2) |
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90 | (2) |
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92 | (1) |
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93 | (2) |
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95 | (2) |
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97 | (2) |
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99 | (2) |
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101 | (1) |
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102 | (2) |
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4.4 Approaches for Ontology Alignment |
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104 | (7) |
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105 | (2) |
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4.4.2 Structural Approaches |
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107 | (1) |
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4.4.3 Instances-Based Approaches |
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107 | (1) |
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4.4.4 Mediated Approaches |
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108 | (1) |
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4.4.5 Alignment Based on Semantic Similarity |
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109 | (2) |
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111 | (1) |
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111 | (9) |
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112 | (1) |
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113 | (1) |
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113 | (3) |
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116 | (4) |
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4.6 Conclusion and Open Problems |
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120 | (2) |
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4.6.1 Discussion and Future Work |
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120 | (2) |
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122 | (7) |
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5 Dynamic Content Negotiation in Web Environments |
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129 | (48) |
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130 | (1) |
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131 | (11) |
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133 | (2) |
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135 | (7) |
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5.3 A Proxy-Based Solution for the Detection of Device Capabilities |
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142 | (12) |
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143 | (8) |
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151 | (1) |
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152 | (2) |
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5.4 Collaborative Optimization, Context Acquisition and Provisioning |
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154 | (16) |
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5.4.1 Application Layer Optimization |
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155 | (2) |
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157 | (3) |
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5.4.3 Header Restoring Policies and Context Provisioning |
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160 | (5) |
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5.4.4 Collaborative Device Capabilities Detection Service |
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165 | (3) |
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5.4.5 Optimization Results |
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168 | (2) |
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170 | (1) |
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171 | (6) |
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6 The Road towards Self-Management in Communication Networks |
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177 | (24) |
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177 | (2) |
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6.2 Self-Management in Networks |
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179 | (5) |
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6.3 Defining Concrete Steps towards the Vision |
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184 | (13) |
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6.3.1 Define Business Objectives in a Business Language |
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184 | (1) |
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6.3.2 Translate the Business Objectives into Technical Terms |
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185 | (3) |
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6.3.3 Derive Rules and Policies for Systems |
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188 | (2) |
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6.3.4 Automatically Breakdown Goals |
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190 | (3) |
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6.3.5 Enable Network Elements to Interpret, Deploy, and Comply with These Goals |
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193 | (4) |
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197 | (2) |
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199 | (2) |
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7 Policy-Based Self-Management in Wireless Networks |
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201 | (72) |
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7.1 Introduction, Background and State-of-the-Art |
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202 | (6) |
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7.1.1 Self-Management Concepts and Challenges |
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202 | (3) |
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7.1.2 Open Issues and Motivation |
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205 | (3) |
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7.2 Policies and Context for Self-Management |
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208 | (15) |
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7.2.1 Policy-Based Management (PBM) Principles |
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208 | (6) |
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7.2.2 Context and Context-Awareness |
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214 | (5) |
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7.2.3 Management of Wireless Ad Hoc Networks and Self-Management Capabilities |
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219 | (4) |
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7.3 A Framework for the Self-Management of Wireless Networks |
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223 | (20) |
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7.3.1 High Level Framework Overview and Design |
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224 | (1) |
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7.3.2 Policy-Based and Context-Aware Organizational Model |
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225 | (4) |
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7.3.3 Policy-Based Design for Autonomic Decision Making |
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229 | (4) |
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7.3.4 Context-Aware Platform for Information Collection and Modeling |
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233 | (3) |
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7.3.5 Distributed Policy and Context Repositories --- The Importance of Knowledge Management |
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236 | (2) |
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7.3.6 Context and Policies Interaction for Closed-Loop Autonomic Management |
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238 | (1) |
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7.3.7 Overview of Applicability and Policy Examples |
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239 | (4) |
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7.4 Implementation and Evaluation of Self-Management Capabilities |
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243 | (15) |
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7.4.1 Self-Configuration and Self-Optimization in Wireless Ad Hoc Networks |
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244 | (9) |
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7.4.2 Self-Configuration of a Distributed Policy Repository |
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253 | (3) |
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7.4.3 Self-Protection of User Privacy and Preferences |
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256 | (2) |
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7.5 Conclusions and the Future of Self-management |
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258 | (4) |
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7.5.1 Summary and Concluding Remarks |
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258 | (2) |
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7.5.2 Future Trends and Challenges |
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260 | (2) |
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262 | (1) |
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262 | (2) |
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264 | (9) |
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8 Autonomous Machine Learning Networks |
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273 | (36) |
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274 | (3) |
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277 | (5) |
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8.2.1 Attack Prediction Problem |
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279 | (1) |
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8.2.2 Attack Class Discovery Problem |
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280 | (2) |
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282 | (4) |
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286 | (2) |
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288 | (3) |
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291 | (10) |
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291 | (1) |
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292 | (3) |
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8.6.3 Initial Arbitrary Network |
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295 | (1) |
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295 | (1) |
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8.6.5 Comparison of Class Prediction |
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295 | (6) |
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8.6.6 Comparison of Cluster Prediction |
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301 | (1) |
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301 | (1) |
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302 | (7) |
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9 Probabilistic Fault Management |
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309 | (40) |
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309 | (3) |
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9.2 Probabilistic Inference in Fault Management |
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312 | (8) |
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9.2.1 The Characteristics of the Faults in Distributed Systems |
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312 | (3) |
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9.2.2 Bayesian Networks for Fault Management |
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315 | (3) |
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9.2.3 Probabilistic Inference for Distributed Fault Management |
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318 | (2) |
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9.3 Prediction Strategies for Fault Management in Dynamic Networks |
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320 | (5) |
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9.3.1 Dynamic Characteristics in Networks |
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320 | (2) |
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9.3.2 Dynamic Bayesian Networks for Fault Management |
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322 | (1) |
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9.3.3 Prediction Strategies for Network Management |
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322 | (3) |
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9.4 Application Investigations for Probabilistic Fault Management |
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325 | (17) |
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9.4.1 Architecture for Network Management |
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325 | (3) |
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9.4.2 The Structure and Function of Fault Diagnosis Agent |
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328 | (12) |
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9.4.3 Discussion of Application Issues |
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340 | (2) |
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342 | (1) |
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342 | (7) |
Index |
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