When compressive sensing meets mobile crowdsensing. ([2019])

• Record Type: Book
• Further Information: Note: Linghe Kong, Bowen Wang and Guihai Chen.
• Authors: Kong, Linghe; Wang, Bowen; Chen, Guihai
• Contents: Intro; Preface; Acknowledgements; Contents; 1 Introduction; 1.1 Mobile Crowdsensing Overview; 1.2 Compressive Sensing Overview; 1.3 Organization; References; 2 Mobile Crowdsensing; 2.1 Background; 2.2 Data Quality Problem in Mobile Crowdsensing; 2.3 Existing Data Quality Solutions in Mobile Crowdsensing; 2.3.1 Quality-Aware Incentive Mechanisms; 2.3.2 Quality-Driven Participator Selection Mechanisms; 2.3.3 Authentication Mechanisms; 2.3.4 Quality-Driven Task Allocation Mechanisms; 2.3.5 Lightweight Preprocessing Strategies; 2.3.6 Outlier Detection and Data Correction; 2.4 Summary; References 3 Compressive Sensing3.1 Background; 3.2 Conventional Compressive Sensing; 3.2.1 Sparsity and Compressible Signals; 3.2.2 Sampling; 3.2.3 Reconstruction; 3.3 Compressive Sensing for Matrix Completion; 3.3.1 From Vectors to Matrices; 3.3.2 Sparsity in Matrices; 3.3.3 Sampling in Matrices; 3.3.4 Reconstruction in Matrices; 3.4 Summary; References; 4 Basic Compressive Sensing for Data Reconstruction; 4.1 Background; 4.2 Problem Statement; 4.2.1 Missing Data Problem in Mobile Crowdsensing; 4.2.2 Sparse and Uneven Data Distribution; 4.3 Basic Compressive Sensing Algorithm 4.3.1 Revealing Hidden Structure4.3.2 Missing Data Reconstruction; 4.3.3 Design Optimizations; 4.4 Experiments and Analysis; 4.4.1 Methodology and Experimental Setup; 4.4.2 Compared Algorithms; 4.4.3 Results; 4.5 Improvements of Compressive Sensing; 4.6 Summary; References; 5 Iterative Compressive Sensing for Fault Detection; 5.1 Background; 5.2 Problem Statement; 5.3 Iterative Compressive Sensing; 5.3.1 Overview; 5.3.2 Optimized Local Median Method; 5.3.3 Time Series and Compressive Sensing; 5.3.4 Discussion; 5.4 Evaluation; 5.4.1 Evaluation Settings 5.4.2 Performance in Faulty Data Detection5.4.3 Performance in Missing Value Reconstruction; 5.4.4 Impact of Faulty and Missing Data in Velocity; 5.4.5 Convergence; 5.5 Summary; References; 6 Homogeneous Compressive Sensing for Privacy Preservation; 6.1 Background; 6.2 Problem Statement; 6.2.1 Trajectory Recovery Model; 6.2.2 User Models and Adversary Models; 6.2.3 Accuracy and Privacy Problem; 6.3 Homogeneous Compressive Sensing Scheme; 6.3.1 Trace Preparation and Validation; 6.3.2 Overview; 6.3.3 Encrypt the Sensed Trajectories at Individual Users 6.3.4 Recover the Encrypted Trajectories at the Server6.3.5 Decrypting the Recovered Trajectories at Individual Users; 6.4 Theoretical Analysis; 6.4.1 Accuracy Analysis; 6.4.2 Privacy Preservation against Eavesdroppers; 6.4.3 Privacy Preservation Against Stalkers; 6.4.4 Complexity Analysis; 6.4.5 Design Discussion; 6.5 Performance Evaluation; 6.5.1 Simulation Settings; 6.5.2 Performance Analysis; 6.5.3 Illustrative Results; 6.6 Summary; References; 7 Converted Compressive Sensing for Multidimensional Data; 7.1 Background; 7.2 Problem Statement; 7.2.1 Preliminary
• Publisher Details: Singapore : Springer
• Publication Date: 2019
• Copyright Date: 2019
• Extent: 1 online resource
• Subjects: 681.2; Sensor networks; Multisensor data fusion; Data mining; Mobile communication systems; TECHNOLOGY & ENGINEERING / Technical & Manufacturing Industries & Trades; Electronic books; Electronic books
• Languages: English
• ISBNs: 9789811377761; 9811377766
• Related ISBNs: 9789811377754; 9811377758
• Notes: Note: Includes bibliographical references.; Note: Vendor-supplied metadata.
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