Mobile Context Awareness

Mobile context-awareness is a popular research trend in the field of ubiquitous computing. Advances in mobile device sensory hardware and the rise of 'virtual' sensors such as web application programming interfaces (APIs) mean that the mobile user is exposed to a vast range of data that ca...

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Main Authors Lovett, Tom, O'Neill, Eamonn
Format eBook
LanguageEnglish
Published London Springer Verlag London Limited 2012
Springer London, Limited
Springer London
Springer
Edition1. Aufl.
SeriesHuman/computer interaction
Subjects
Classification
Computer Appl. in Social and Behavioral Sciences
Computer Science
Computers and Society
Context-aware computing
Machine learning
Mobile computing
Personal Computing
User Interfaces and Human Computer Interaction
Computer Betriebssysteme
Online AccessGet full text

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Abstract Mobile context-awareness is a popular research trend in the field of ubiquitous computing. Advances in mobile device sensory hardware and the rise of 'virtual' sensors such as web application programming interfaces (APIs) mean that the mobile user is exposed to a vast range of data that can be used for new advanced applications. Mobile Context Awareness presents work from industrial and academic researchers, focusing on novel methods of context acquisition in the mobile environment - particularly through the use of physical and virtual sensors - along with research into new applications utilising this context. In addition, the book provides insights into the technical and usability challenges involved in mobile context-awareness, as well as observations on current and future trends in the field.
AbstractList This book explores the application of machine learning techiques to problems of classification, showing how they can be decomposed into much simpler sub-problems, and demonstrating how this approach often leads to improved classification performance.
This volume explores the application of machine learning techiques to problems of classification, showing how they can be decomposed into much simpler sub-problems, and demonstrating how this approach often leads to improved classification performance.
Mobile context-awareness is a popular research trend in the field of ubiquitous computing. Advances in mobile device sensory hardware and the rise of 'virtual' sensors such as web application programming interfaces (APIs) mean that the mobile user is exposed to a vast range of data that can be used for new advanced applications. Mobile Context Awareness presents work from industrial and academic researchers, focusing on novel methods of context acquisition in the mobile environment - particularly through the use of physical and virtual sensors - along with research into new applications utilising this context. In addition, the book provides insights into the technical and usability challenges involved in mobile context-awareness, as well as observations on current and future trends in the field.
Author O'Neill, Eamonn
Lovett, Tom
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Snippet Mobile context-awareness is a popular research trend in the field of ubiquitous computing. Advances in mobile device sensory hardware and the rise of 'virtual'...
This book explores the application of machine learning techiques to problems of classification, showing how they can be decomposed into much simpler...
This volume explores the application of machine learning techiques to problems of classification, showing how they can be decomposed into much simpler...
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springer
proquest
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SubjectTerms Classification
Computer Appl. in Social and Behavioral Sciences
Computer Science
Computers and Society
Context-aware computing
Machine learning
Mobile computing
Personal Computing
User Interfaces and Human Computer Interaction
TableOfContents 10.6.4 Adaptive Mobile Communicator (AMC) -- 10.7 Qualitative Evaluation of the Middleware from the Developer's Perspective -- 10.8 Conclusion -- References -- Index
7.3.2 High Level Architecture -- 7.3.3 Plugins -- 7.4 Case Studies -- 7.4.1 iMailbox -- 7.4.2 iRefrigerator -- 7.5 Discussion and Conclusion -- References -- Chapter 8: Mobile Context-Aware Support for Public Transportation Users -- 8.1 Introduction -- 8.2 Related Work -- 8.3 Usage Scenarios -- 8.4 System Overview -- 8.5 User Interaction -- 8.5.1 Location -- 8.5.2 Sensor Data -- 8.5.3 Performance and Resource Consumption -- 8.6 User Feedback -- 8.7 Discussion -- 8.8 Conclusions -- References -- Chapter 9: Quality Sensitive Web Service Pro ling and Discovery: In Support of Mobile and Pervasive Applications -- 9.1 Introduction -- 9.2 Related Work -- 9.3 Proposed System -- 9.3.1 System Architecture -- 9.3.2 Architecture Information Models -- 9.3.2.1 Q-UDDI Information Model -- 9.3.2.2 Q-Pro ler Information Model -- 9.3.3 Architecture Usability -- 9.3.4 UDDI Extensions: Q-UDDI -- 9.3.4.1 save_q_tModel -- 9.3.4.2 save_q_service -- 9.3.4.3 nd_q_service -- 9.3.5 The Pro ling Mechanism -- 9.3.5.1 Measurement and Derivation of QoS Properties -- 9.3.5.2 Usage of Policies in Web Service Pro ling -- 9.3.6 The Discovery Mechanism -- 9.4 Prototype and Experiments -- 9.4.1 Experimentation Environment -- 9.4.2 Experiment 1: Q-UDDI Latency -- 9.4.3 Experiment 2: The Effect of Pro ling on Web Service Latency -- 9.5 Conclusion -- References -- Chapter 10: A Middleware Supporting Adaptive and Context-Aware Mobile Applications -- 10.1 Introduction -- 10.2 Related Work -- 10.3 Proposed Client Architecture -- 10.4 Implemented Middleware Services -- 10.4.1 Kaluana -- 10.4.2 Shared Data Manager -- 10.4.3 MD-ECI -- 10.4.4 Context Management Service -- 10.5 Programming Level Support -- 10.5.1 COP and ContextJ/ContextJ* -- 10.5.2 Situation Evaluation Engine (SEE) -- 10.6 Application Prototypes -- 10.6.1 TrackService -- 10.6.2 ActiveCal -- 10.6.3 Bus4Blinds
4.3 Methods of Acceleration Noise Correction -- 4.3.1 Noise Correction Based on Acceleration Changes -- 4.3.2 Feature Values Used in Noise Correction -- 4.3.2.1 Degree of Direction Change -- 4.3.2.2 Degree of Impact Change -- 4.3.2.3 Walking Pace -- 4.3.3 Noise Correction Based on the OS Events -- 4.4 Evaluation -- 4.4.1 Evaluation on the Effect of Noise Correction Based on the Acceleration Changes -- 4.4.2 Review on the Effects of Noise Correction Based on the OS Events -- 4.5 Related Work -- 4.6 Conclusion -- References -- Chapter 5: Mobile Sensing of User's Motion and Position Context for Automatic Check-in Suggestion and Validation -- 5.1 Introduction and Motivation -- 5.2 System Architecture -- 5.3 Context Collection and Classi cation -- 5.3.1 User's Position -- 5.3.2 User's Motion Situation -- 5.3.2.1 Motion Recognition Method -- Majority Decision -- Time-Weighted Decision -- Score-Weighted Decision -- Joint Score/Time-Weighted Decision -- 5.3.2.2 Method Training -- Dataset Acquisition -- Parameters Setting -- 5.3.2.3 Classi cation Results -- Frame Classi cation Results -- Windowed Decision Results -- 5.3.3 User's Contacts' Proximity -- 5.4 Context-Based Check-in Gateway -- 5.4.1 Check-in Suggestion -- 5.4.2 Check-in Validation -- 5.5 Related Work -- 5.6 Conclusions and Future Work -- References -- Chapter 6: The Case for Context-Aware Resources Management in Mobile Operating Systems -- 6.1 Introduction -- 6.2 Are Users' Interaction with Their Handsets Driven by Context? -- 6.3 Context-Aided Mobile Resource Management -- 6.3.1 Wireless Interfaces -- 6.3.2 Location Sensors -- 6.4 Context-Aware Mobile Operating Systems -- 6.5 Summary -- References -- Chapter 7: A Scalable Sensor Middleware for Social End-User Programming -- 7.1 Introduction -- 7.2 Related Work -- 7.2.1 Sensor Andrew -- 7.2.2 XMPP -- 7.3 System Overview -- 7.3.1 User Interface
Intro -- Mobile Context Awareness -- Foreword -- Preface -- Contents -- Contributors -- Chapter 1: Introduction -- 1.1 Context -- 1.2 Context Awareness -- 1.3 Mobile Context Awareness -- 1.3.1 Mobile Context-Awareness Research -- 1.4 Book Motivation -- 1.5 Book Structure -- References -- Chapter 2: Modeling Success, Failure, and Intent of Multi-Agent Activities Under Severe Noise -- 2.1 Introduction -- 2.2 Capture The Flag Domain -- 2.3 Our Contributions -- 2.4 Background -- 2.4.1 Markov Logic -- 2.4.2 Dynamic Bayesian Networks -- 2.5 Methodology -- 2.5.1 Recognition of Successful Activities -- 2.5.2 Learning Models of Failed Attempts -- 2.5.2.1 The Theory Augmentation Algorithm -- 2.5.2.2 Consistency Check: Finding Incompatible Formulas -- 2.5.3 Extracting the Goal from Success and Failure -- 2.5.4 Identifying Interesting Game Situations -- 2.6 Experiments and Results -- 2.6.1 Recognition of Successful Activities -- 2.6.2 Learned Formulas and Intentions -- 2.6.3 Recognition of Both Successful and Failed Activities -- 2.6.4 The Effect of Modeling Failed Attempts on Recognition of Successful Activities -- 2.6.5 Identifying Interesting Game Situations -- 2.7 Related Work -- 2.8 Conclusions -- 2.9 Future Work -- References -- Chapter 3: Energy-Accuracy Trade-offs of Sensor Sampling in Smart Phone Based Sensing Systems -- 3.1 Introduction -- 3.2 Adaptive Sampling Based on Dynamic Selection of Functions -- 3.3 Evaluation -- 3.3.1 Dataset -- 3.3.2 Results -- 3.4 Related Work -- 3.5 Conclusions -- References -- Chapter 4: Acceleration Noise Correction for Transfer Inference Using Accelerometers on Mobile Devices -- 4.1 Introduction -- 4.2 Acceleration Noises -- 4.2.1 De nition of Acceleration Noises -- 4.2.2 Classi cation of Acceleration Noises -- 4.2.2.1 Taking-out Noise -- 4.2.2.2 Waggling Noise -- 4.2.2.3 In-Operation Noise
Title Mobile Context Awareness
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