Adaptive Mobile Computing

Description

Adaptive Mobile Computing: Advances in Processing Mobile Data Sets explores the latest advancements in producing, processing and securing mobile data sets. The book provides the elements needed to deepen understanding of this trend which, over the last decade, has seen exponential growth in the number and capabilities of mobile devices. The pervasiveness, sensing capabilities and computational power of mobile devices have turned them into a fundamental instrument in everyday life for a large part of the human population. This fact makes mobile devices an incredibly rich source of data about the dynamics of human behavior, a pervasive wireless sensors network with substantial computational power and an extremely appealing target for a new generation of threats.

Offers a coherent and realistic image of today’s architectures, techniques, protocols, components, orchestration, choreography and development related to mobile computing
Explains state-of-the-art technological solutions for the main issues hindering the development of next-generation pervasive systems including: supporting components for collecting data intelligently, handling resource and data management, accounting for fault tolerance, security, monitoring and control, addressing the relation with the Internet of Things and Big Data and depicting applications for pervasive context-aware processing
Presents the benefits of mobile computing and the development process of scientific and commercial applications and platforms to support them
Familiarizes readers with the concepts and technologies that are successfully used in the implementation of pervasive/ubiquitous systems

Front Cover 2
Adaptive Mobile Computing: Advances in Processing Mobile Data Sets 5
Copyright 6
Contents 7
Contributors 13
Acknowledgments 15
Introduction 17
Part I: Generating Mobile Data 21
- Chapter 1: Cloud Services for Smart City Applications 23
  - 1. Introduction 23
  - 2. A Short Overview on Intelligent Transportation Systems 24
  - 3. MobiWay-Middleware Connection Hub for ITS Services 28
  - 4. An Open-Source ITS Mobile App 32
  - 5. The MobiWay Platform 37
  - 6. Web Services for ITS Support 39

Front Cover 2
Adaptive Mobile Computing: Advances in Processing Mobile Data Sets 5
Copyright 6
Contents 7
Contributors 13
Acknowledgments 15
Introduction 17
Part I: Generating Mobile Data 21
- Chapter 1: Cloud Services for Smart City Applications 23
  - 1. Introduction 23
  - 2. A Short Overview on Intelligent Transportation Systems 24
  - 3. MobiWay-Middleware Connection Hub for ITS Services 28
  - 4. An Open-Source ITS Mobile App 32
  - 5. The MobiWay Platform 37
  - 6. Web Services for ITS Support 39
  - 7. Evaluation Results 42
  - References 47
  - 8. Conclusion 47
- Chapter 2: Environment Sensors Measures Processing: Integrating Real-Time and Spreadsheet-Based Data Analysis 49
  - 1. Introduction 49
  - 2. Environment Sensors Measure Processing: Exploiting Data Analytics for Adaptive Real-Time Data Processing 51
    - 2.1. Spreadsheet Data Sharing for Offline Data Analytics 51
  - 3. OSGi Framework Based IoT Engine for Real-Time Data Collection and Data Mashup 53
    - 3.1. Object Virtualization Layer 53
    - 3.3. OSGi Framework Based IoT Engine Implementation 54
    - 3.2. Data Mashup Layer 54
  - 4. Spreadsheet Space Layer: Spreadsheet-Based Data Sharing and Collaborative Analytics 58
    - 4.1. Platform Overview 59
    - 4.2. Data Export and Import Mechanisms 60
    - 4.3. Data Synchronization Mechanism 60
  - 5. A Field Test Experiment: Deploying the Platform in a Real Case Scenario 62
  - 6. Conclusions 64
  - References 65
  - Further Reading 66
- Chapter 3: Fusion of Heterogeneous Mobile Data, Challenges, and Solutions 67
  - 1. Introduction 67
  - 2. Opportunities: Application Examples 68
  - 3. Challenges 69
    - 3.1. Nontechnical Challenges 69
    - 3.2. Technical Challenges 70
  - 4. Solutions 70
    - 4.1. Platform for Sharing and Fusing Heterogeneous Mobile Data 71
      - 4.1.1. System requirements 71
      - 4.1.2. Our approach: SeRAVi 71
        
        Visualization 71
        
        Data retrieval 73
      - 4.1.3. Current status and future direction 73
    - 4.2. Platform for Sharing and Generating Social Sensor Data 73
      - 4.2.1. System requirements 73
      - 4.2.2. Basic idea for sharing program codes 74
      - 4.2.3. S3 system 74
        
        SSTD, SSFD, and SSOC 74
        
        System architecture 75
      - 4.2.4. Comparison with existing related platforms 76
      - 4.2.5. Current status and future plan 77
    - 4.3. Game-Based Approach for Collecting Users High-Level Context 78
      - 4.3.2. System 78
        
        System architecture 79
        
        Initialization 79
        
        Questions about contexts 80
        
        App log collection 80
        
        Game design 81
      - 4.3.1. Basic idea 78
      - 4.3.3. Current status and future plan 81
    - 5. Conclusions 81
    - Acknowledgments 82
    - References 82
  - Chapter 4: Long-Range Passive Doppler-Only Target Tracking by Single-Hydrophone Underwater Sensors with MobilityaaThis wo ... 85
    - 1. Introduction 85
    - 2. Background and Problem Definition 87
      - 2.1. Problem Geometry 87
      - 2.2. Doppler 88
      - 2.3. Problem Definition 89
    - 3. Target-Tracking Approaches 89
    - 4. Simulation Results 95
    - References 98
    - 5. Conclusions and Future Works 98
  - Part II: Processing Mobile Data 101
    - Chapter 5: An Online Algorithm for Online Fraud Detection: Definition and Testing 103
      - Chapter Points 103
      - 1. Introduction 103
      - 2. Fraud Detection Algorithm for Online Banking 104
      - 3. Sample Features and Data Preprocessing 105
      - 4. Classifier Architecture 106
      - 5. Simple Classifiers 107
        
        5.1. SVM Simple Classifiers 107
        
        5.2. Behavioral Simple Classifiers 108
      - 6. Two Layers Assignment 108
      - 8. Empirical Assessment of Metaclassifier Performance 109
        
        8.1. Pure SVM Simple Classifiers 110
        
        8.2. SVMs and Behavioral Simple Classifiers 111
      - 7. Classification Flow 109
      - 9. Cross-Validation 112
      - 10. Sensitivity Analysis 114
      - 11. Further Performance Tests 115
        
        11.1. Dataset Characteristics 115
        
        11.2. Data Preprocessing and Choice of the Classifier 116
        
        11.3. Cost Matrix and Cost-Based Classifier 119
      - 12. Conclusion 123
      - Appendix A. Brief Introduction to Adaboost 123
      - Appendix B. Brief Introduction to SVM 125
      - References 126
    - Chapter 6: Introducing Ubiquity in Noninvasive Measurement Systems for Agile Processes 129
      - 1. Introduction 129
        
        1.1. Problem Statement 129
        
        1.2. Literature Review 130
        
        1.2.1. Agile processes 130
        
        1.2.2. Process modeling 130
        
        1.2.3. Noninvasive measurement tools 134
        
        1.2.4. Ubiquity 136
        
        1.3. System Design 136
        
        1.3.1. iAgile 136
        
        1.3.2. Bayesian model 139
        
        1.3.3. Noninvasive measurement system 140
        
        1.3.4. Ubiquity features 141
        
        2. Results 143
        
        3. Conclusions 144
        
        References 145
        
        Further Reading 146
      - Chapter 7: Constraint-Aware Data Analysis on Mobile Devices: An Application to Human Activity Recognition on Smartphones 147
        
        1. Introduction 147
        
        Chapter Points 147
        
        2. Constraint-Aware Data Analysis 148
        
        2.1. Training Constraint-Aware Classifiers 149
        
        2.2. SLT for Selecting the Best Classifier 153
        
        3. Application to HAR on Smartphones 156
        
        3.1. Dealing with BAs and PTs 157
        
        3.2. HAR Dataset with BAs and PTs 160
        
        3.3. Algorithm Performance 162
        
        4. Conclusions 164
        
        References 164
      - Part III: Securing Mobile Data 171
        
        Chapter 8: How on Earth Could That Happen? An Analytical Study on Selected Mobile Data Breaches 173
        
        1. Introduction 173
        
        2. Attack Motivations and Incentives 174
        
        3. Breaches by the Numbers 176
        
        4. Recent Mobile Data Breaches 179
        
        4.1. Autonomous Devices 180
        
        4.2. Smart-Home Devices 181
        
        4.3. Medical IoT Devices 183
        
        4.4. Industrial IoT Devices 184
        
        4.5. BYOD 185
        
        4.6. Mobile Malware 186
        
        4.7. Communication Protocols 188
        
        5. Mobile Data Breaches: Insights 189
        
        5.1. Malware is Evasive 190
        
        5.2. Too Many Attack Surfaces 190
        
        5.3. Obstacles 193
        
        5.4. Risks and Patterns 194
        
        6. Recommendations 195
        
        7. Conclusions 197
        
        References 198
        
        Chapter 9: Understanding Information Hiding to Secure Communications and to Prevent Exfiltration of Mobile Data 205
        
        1. Introduction 205
        
        2. Information Hiding and Mobile Devices 206
        
        3. Covert Channels Targeting Mobile Devices: A Review 208
        
        3.3. Network Covert Channels 209
        
        3.1. Local Covert Channels 209
        
        3.2. Air-Gapped Covert Channels 209
        
        3.4. Countermeasures and Mitigation Techniques 210
        
        4. Detecting Covert Channels and Preventing Information Hiding in Mobile Devices 211
        
        4.1. General Reference Architecture 211
        
        4.2. Activity-Based Detection 212
        
        4.3. Energy-Based Detection 213
        
        4.3.1. Regression-based detection 213
        
        4.3.2. Classification-based detection 214
        
        5. Experimental Results 215
        
        5.1. Performance Evaluation of Activity-Based Detection 215
        
        5.2. Performance Evaluation of Energy-Based Detection 216
        
        6. Conclusions 220
        
        References 221
        
        Chapter 10: Exploring Mobile Data Security with Energy Awareness 223
        
        1. Introduction 223
        
        2. Mobile Data Security Threats and Countermeasures 223
        
        3. Mobile Security Management 229
        
        4. Mobile Data Security with Energy Awareness 230
        
        5. Future Trends and Conclusion 232
        
        References 233
        
        Chapter 11: Effective Security Assessment of Mobile Apps with MAVeriC: Design, Implementation, and Integration of a Unifi ... 237
        
        1. Introduction 237
        
        Chapter Points 237
        
        2. Related Work 239
        
        3. Overview of the Architecture 241
        
        4. MAVeriC Integration 244
        
        4.1. Integration with the PI CERT 244
        
        4.2. App Monitoring Methodology 245
        
        5. Case Studies 246
        
        5.1. Analyzing a Known Malware 247
        
        App submission 247
        
        Malware analysis report inspection 247
        
        Permission checking report inspection 248
        
        Reverse engineering report inspection 249
        
        5.2. Finding New Threats 250
        
        Malware analysis report inspection 251
        
        App stimulation report inspection 251
        
        Permission checking report inspection 251
        
        Network analysis report inspection 253
        
        Reverse engineering report inspection 253
        
        Outcome 254
        
        References 254
        
        6. Conclusions 254
        
        Acknowledgments 254
        
        Conclusion 257
        
        Index 259
        
        Back Cover 265

Author's affiliation

Mauro Migliardi: University of Padua and University of Genoa, Italy
Alessio Merlo: University of Genoa, Italy
Sherenaz Al-HajBaddar: University of Jordan, Jordan

Advances in Processing Mobile Data Sets

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