book

Pervasive Computing

Name: Pervasive Computing
ISBN: 9781315356457

by Minyi Guo, Jingyu Zhou, Feilong Tang, Yao Shen

August 2016

Intermediate to advanced

206 pages

6h 1m

English

CRC Press

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Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
About the Authors
1 Pervasive Computing Concepts
1.1 Perspectives of Pervasive Computing1.1.1 Technology Trend Overview1.1.2 Pervasive Computing: Concepts1.2 Challenges1.3 Technology1.3.1 Middleware1.3.2 Context Awareness1.3.3 Resource Management1.3.4 Human–Computer Interaction1.3.5 Pervasive Transaction Processing1.3.6 User Preference and RecommendationsReferences
2 The Structure and Elements of Pervasive Computing Systems
2.1 Infrastructure and Devices2.1.1 Wireless Networks2.2 Middleware for Pervasive Computing Systems2.2.1 Resource Management2.2.2 User Tracking2.2.3 Context Management2.2.4 Service Management2.2.5 Data Management2.2.6 Security Management2.3 Pervasive Computing Environments2.3.1 Smart Car Space2.3.2 Intelligent CampusFurther ReadingsReferences
3 Context Collection, User Tracking, and Context Reasoning
3.1 Context Collection and Wireless Sensor Networks3.1.1 Context Category3.1.2 Context Collection Framework3.2 User Tracking3.2.1 Position Identification3.2.2 Mobile Robot Position Identification3.2.3 Intelligent Urban Traffic Management3.3 Context Reasoning3.3.1 Evidence Theory3.3.2 DSCR Model3.3.3 Propagating Evidence in the Sensors Layer3.3.4 Propagating Evidence in the Objects Layer3.3.5 Recognizing User Activity3.3.6 Evidence Selection Strategy3.3.7 PerformanceFurther ReadingsReferences

4 Resource Management in Pervasive Computing
4.1 Efficient Resource Allocation in Pervasive Environments4.1.1 Introduction to PMP Systems4.1.2 Pipeline-Based Resource Allocation4.1.3 Probabilistic Approach-Based Resource Allocation4.1.3.1 Basic Algorithm4.1.3.2 Optimized Algorithm4.1.3.3 Probability Analysis4.2 Transparent Task Migration4.2.1 Introduction to Task Migration4.2.2 Task Migration Model4.2.2.1 HTML/DHTML Viewstate4.2.2.2 OZ Source File Reorganization4.2.2.3 Compiler Support4.2.2.4 Application State Persistence and Recovery4.2.3 Resource Redirection4.2.3.1 Resource Handle Transformation Schema4.2.3.2 Resource Transformation Schema4.2.4 xMozart: A Novel Platform for Intelligent Task Migration4.2.4.1 Migration Management Modules4.2.4.2 Multiple Modalities4.2.4.3 Multimodal Programming in xMozart4.2.5 Implementation and Illustrations4.2.5.1 Prototype for Application Migration4.2.5.2 Prototype for MultimodalitiesFurther ReadingsReferences
5 Human–Computer Interface in Pervasive Environments
5.1 Overview5.2 HCI Service and Interaction Migration5.3 Context-Driven HCI Service Selection5.3.1 Interaction Service Selection Overview5.3.2 User Devices5.3.2.1 Service-Oriented Middleware Support5.3.2.2 User History and Preference5.3.3 Context Manager5.3.4 Local Service Matching5.3.5 Global Combination Selection5.3.5.1 Effective Region5.3.5.2 User Active Scope5.3.5.3 Service Combination Selection Algorithm5.4 Scenario Study: Video Calls at a Smart Office5.4.1 Scenario Description5.4.1.1 The Smart Office Environment5.4.1.2 Scenario Description5.4.2 HCI Migration Request5.4.3 Context Format5.4.4 Device Profile5.4.5 Experiments and Results5.4.5.1 Simulation Result5.4.5.2 Scalability5.5 A Web Service–Based HCI Migration Framework5.5.1 HCI Migration Support Environment5.5.2 Interaction Requirements Description5.5.3 Interaction Devices Manager5.5.4 Interaction Web Service5.5.5 Runtime Migration Demonstration5.6 Summary5.7 Appendices5.7.1 Request Format in XML5.7.2 Context Format in XML5.7.3 Device Profile in XMLFurther ReadingsReferences
6 Pervasive Mobile Transactions
6.1 Introduction to Pervasive Transactions6.2 Mobile Transaction Framework6.2.1 Unavailable Transaction Service6.2.2 Pervasive Transaction Processing Framework6.3 Context-Aware Pervasive Transaction Model6.3.1 Context Model for Pervasive Transaction Processing6.3.2 Context-Aware Pervasive Transaction Model6.3.3 A Case of Pervasive Transactions6.4 Dynamic Transaction Management6.4.1 Context-Aware Transaction Coordination Mechanism6.4.2 Coordination Algorithm for Pervasive Transactions6.4.3 Participant Discovery6.5 Formal Transaction Verification6.5.1 Petri Net with Selective Transition6.5.2 Petri Net–Based Formal Verification6.6 Evaluations6.6.1 Experiment Environment6.6.2 Results and EvaluationFurther ReadingsReferences
7 User Preferences and Recommendations
7.1 Content-Based Recommendation in an RSS Reader7.1.1 Data Collection7.1.2 Recommendation Features7.1.3 Feature Combinations7.1.4 Performance7.2 A Collaborative Filtering-Based Recommendation7.2.1 Background on Collaborative Filtering7.2.2 CFSF Approach7.2.2.1 Overview7.2.2.2 Offline Phase7.2.2.3 Online Phase7.2.3 Performance of CFSF7.2.3.1 Metrics7.2.3.2 Overall Performance7.3 Preference-Based Top-K Recommendation in Social Networks7.3.1 Problem Formulation7.3.2 Computing User Preferences7.3.3 Greedy Algorithm for Mining Top-K Nodes7.3.4 Performance7.3.4.1 Influence Model7.3.4.2 Algorithms and Metrics7.3.4.3 Study on Influence Spread7.3.4.4 ISST versus IS7.3.4.5 Expert Search7.3.4.6 GAUP versus CF7.3.4.7 Topic Drift of HITSFurther ReadingsReferences
8 Case Studies
8.1 iCampus Prototype8.1.1 iShadow: Pervasive Computing Environment8.1.2 Applications in iCampus Prototype8.2 IPSpace: An IPv6-Enabled Intelligent Space8.2.1 IPSpace Architecture Overview8.2.1.1 Device Layer8.2.1.2 Service Layer8.2.1.3 Security Architecture8.2.2 Lightweight Embedded Web Services8.2.2.1 Middleware Layer8.2.2.2 Implementation8.2.3 ExperimentFurther ReadingsReferences
Index

Content preview from Pervasive Computing

Chapter 7

User Preferences and Recommendations

Pervasive applications assist human users to perform various tasks. In particular, these applications should become smart in the sense that even though the users may not explicitly specify their needs, the applications can learn from past interactions with users and adapt their behaviors in the future to provide customized services. In order to achieve this goal, pervasive applications actually learn user preferences from user interactions and make recommendations based on the learned preferences.

A recommendation is a way to help people find information to suit their needs and is widely used by many online services for suggesting products to customers that they might like to buy. For instance, Amazon ...

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Publisher Resources

ISBN: 9781315356457

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

Security

Design

Business

Soft Skills

Pervasive Computing

by Minyi Guo, Jingyu Zhou, Feilong Tang, Yao Shen

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.