Adaptive Mobile Computing ：Advances in Processing Mobile Data Sets ( Intelligent Data-Centric Systems: Sensor Collected Intelligence )

Publication subTitle ：Advances in Processing Mobile Data Sets

Publication series ：Intelligent Data-Centric Systems: Sensor Collected Intelligence

Author： Migliardi   Mauro;Merlo   Alessio;Al-HajBaddar   Sherenaz  

Publisher： Elsevier Science‎

Publication year： 2017

E-ISBN: 9780128046104

P-ISBN(Paperback): 9780128046036

Subject： TN929.5 mobile communication

Keyword： 计算技术、计算机技术

Language： ENG

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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

Chapter

Front Cover

Adaptive Mobile Computing: Advances in Processing Mobile Data Sets

Copyright

Contents

Contributors

Acknowledgments

Introduction

Part I: Generating Mobile Data

Chapter 1: Cloud Services for Smart City Applications

1. Introduction

2. A Short Overview on Intelligent Transportation Systems

3. MobiWay-Middleware Connection Hub for ITS Services

4. An Open-Source ITS Mobile App

5. The MobiWay Platform

6. Web Services for ITS Support

7. Evaluation Results

8. Conclusion

References

Chapter 2: Environment Sensors Measures Processing: Integrating Real-Time and Spreadsheet-Based Data Analysis

1. Introduction

2. Environment Sensors Measure Processing: Exploiting Data Analytics for Adaptive Real-Time Data Processing

2.1. Spreadsheet Data Sharing for Offline Data Analytics

3. OSGi Framework Based IoT Engine for Real-Time Data Collection and Data Mashup

3.1. Object Virtualization Layer

3.2. Data Mashup Layer

3.3. OSGi Framework Based IoT Engine Implementation

4. Spreadsheet Space Layer: Spreadsheet-Based Data Sharing and Collaborative Analytics

4.1. Platform Overview

4.2. Data Export and Import Mechanisms

4.3. Data Synchronization Mechanism

5. A Field Test Experiment: Deploying the Platform in a Real Case Scenario

6. Conclusions

References

Further Reading

Chapter 3: Fusion of Heterogeneous Mobile Data, Challenges, and Solutions

1. Introduction

2. Opportunities: Application Examples

3. Challenges

3.1. Nontechnical Challenges

3.2. Technical Challenges

4. Solutions

4.1. Platform for Sharing and Fusing Heterogeneous Mobile Data

4.1.1. System requirements

4.1.2. Our approach: SeRAVi

Visualization

Data retrieval

4.1.3. Current status and future direction

4.2. Platform for Sharing and Generating Social Sensor Data

4.2.1. System requirements

4.2.2. Basic idea for sharing program codes

4.2.3. S3 system

SSTD, SSFD, and SSOC

System architecture

4.2.4. Comparison with existing related platforms

4.2.5. Current status and future plan

4.3. Game-Based Approach for Collecting Users High-Level Context

4.3.1. Basic idea

4.3.2. System

System architecture

Initialization

Questions about contexts

App log collection

Game design

4.3.3. Current status and future plan

5. Conclusions

Acknowledgments

References

Chapter 4: Long-Range Passive Doppler-Only Target Tracking by Single-Hydrophone Underwater Sensors with MobilityaaThis wo ...

1. Introduction

2. Background and Problem Definition

2.1. Problem Geometry

2.2. Doppler

2.3. Problem Definition

3. Target-Tracking Approaches

4. Simulation Results

5. Conclusions and Future Works

References

Part II: Processing Mobile Data

Chapter 5: An Online Algorithm for Online Fraud Detection: Definition and Testing

Chapter Points

1. Introduction

2. Fraud Detection Algorithm for Online Banking

3. Sample Features and Data Preprocessing

4. Classifier Architecture

5. Simple Classifiers

5.1. SVM Simple Classifiers

5.2. Behavioral Simple Classifiers

6. Two Layers Assignment

7. Classification Flow

8. Empirical Assessment of Metaclassifier Performance

8.1. Pure SVM Simple Classifiers

8.2. SVMs and Behavioral Simple Classifiers

9. Cross-Validation

10. Sensitivity Analysis

11. Further Performance Tests

11.1. Dataset Characteristics

11.2. Data Preprocessing and Choice of the Classifier

11.3. Cost Matrix and Cost-Based Classifier

12. Conclusion

Appendix A. Brief Introduction to Adaboost

Appendix B. Brief Introduction to SVM

References

Chapter 6: Introducing Ubiquity in Noninvasive Measurement Systems for Agile Processes

1. Introduction

1.1. Problem Statement

1.2. Literature Review

1.2.1. Agile processes

1.2.2. Process modeling

1.2.3. Noninvasive measurement tools

1.2.4. Ubiquity

1.3. System Design

1.3.1. iAgile

1.3.2. Bayesian model

1.3.3. Noninvasive measurement system

1.3.4. Ubiquity features

2. Results

3. Conclusions

References

Further Reading

Chapter 7: Constraint-Aware Data Analysis on Mobile Devices: An Application to Human Activity Recognition on Smartphones

Chapter Points

1. Introduction

2. Constraint-Aware Data Analysis

2.1. Training Constraint-Aware Classifiers

2.2. SLT for Selecting the Best Classifier

3. Application to HAR on Smartphones

3.1. Dealing with BAs and PTs

3.2. HAR Dataset with BAs and PTs

3.3. Algorithm Performance

4. Conclusions

References

Part III: Securing Mobile Data

Chapter 8: How on Earth Could That Happen? An Analytical Study on Selected Mobile Data Breaches

1. Introduction

2. Attack Motivations and Incentives

3. Breaches by the Numbers

4. Recent Mobile Data Breaches

4.1. Autonomous Devices

4.2. Smart-Home Devices

4.3. Medical IoT Devices

4.4. Industrial IoT Devices

4.5. BYOD

4.6. Mobile Malware

4.7. Communication Protocols

5. Mobile Data Breaches: Insights

5.1. Malware is Evasive

5.2. Too Many Attack Surfaces

5.3. Obstacles

5.4. Risks and Patterns

6. Recommendations

7. Conclusions

References

Chapter 9: Understanding Information Hiding to Secure Communications and to Prevent Exfiltration of Mobile Data

1. Introduction

2. Information Hiding and Mobile Devices

3. Covert Channels Targeting Mobile Devices: A Review

3.1. Local Covert Channels

3.2. Air-Gapped Covert Channels

3.3. Network Covert Channels

3.4. Countermeasures and Mitigation Techniques

4. Detecting Covert Channels and Preventing Information Hiding in Mobile Devices

4.1. General Reference Architecture

4.2. Activity-Based Detection

4.3. Energy-Based Detection

4.3.1. Regression-based detection

4.3.2. Classification-based detection

5. Experimental Results

5.1. Performance Evaluation of Activity-Based Detection

5.2. Performance Evaluation of Energy-Based Detection

6. Conclusions

References

Chapter 10: Exploring Mobile Data Security with Energy Awareness

1. Introduction

2. Mobile Data Security Threats and Countermeasures

3. Mobile Security Management

4. Mobile Data Security with Energy Awareness

5. Future Trends and Conclusion

References

Chapter 11: Effective Security Assessment of Mobile Apps with MAVeriC: Design, Implementation, and Integration of a Unifi ...

Chapter Points

1. Introduction

2. Related Work

3. Overview of the Architecture

4. MAVeriC Integration

4.1. Integration with the PI CERT

4.2. App Monitoring Methodology

5. Case Studies

5.1. Analyzing a Known Malware

App submission

Malware analysis report inspection

Permission checking report inspection

Reverse engineering report inspection

5.2. Finding New Threats

Malware analysis report inspection

App stimulation report inspection

Permission checking report inspection

Network analysis report inspection

Reverse engineering report inspection

Outcome

6. Conclusions

Acknowledgments

References

Conclusion

Index

Back Cover

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