Intro
Preface
Acknowledgments
Contents
Part I: How to Collect Crowdsensing Data (Multi-dimensional Fundamental Issues)
Chapter 1: Incentivizing Platform-Users with Win-Win Effects
1.1 Introduction
1.2 Related Work
1.3 System Model and Problem Formulation
1.3.1 System Model
1.3.2 Example of Personalized Bidding Scenario
1.3.3 Problem Formalization
1.3.4 Analysis of Problem Complexity
1.4 Design of Picasso
1.4.1 Bid Description in 3-D Space
PB Description in 3-D Space
Formal Framework of Bid Description Using 3-D Expressive Space

PB Description Method
Theoretical Analysis
1.4.2 Task Allocation Based on Dependency Graph
Construction of Task Dependency Graph
PB Decomposition for Efficient Task Allocation
Problem Transformation by Decomposing the Task Dependency Graph
Task Allocation with Constant-Factor Approximation
Theoretical Analysis
PB Recombination for Strategy-Proof Payment
Truthful Payment Scheme for Non-PB Based on Critical Prices
Truthful Payment Scheme for PB Based on Graph Recombination
Theoretical Analysis
1.5 Performance Evaluation of Picasso
1.5.1 Simulations

Simulation Methodology and Settings
Results
1.5.2 Trace-Driven Case Study of Gigwalk
Evaluation Methodology and Settings
Results
1.6 Discussion and Future Work
1.7 Conclusion
References
Chapter 2: Task Recommendation Based on Big Data Analysis
2.1 Introduction
2.2 Related Work
2.3 Motivation
2.3.1 Crowdsourcing-Based User Studies
2.3.2 Large-Scale Dataset Collection and Analysis
2.4 LSTRec Design for MOVE-CS
2.4.1 Model Design
2.4.2 Research Problem and Challenge Analysis
2.5 Key Algorithm Design for LSTRec

Pick-Up Profit Heatmap Construction
2.5.2 Differentiation-Aware Sensing Reward Design
2.5.3 Submodularity-Based Task Recommendation Algorithm
2.6 Evaluation
2.6.1 Emulation Methodology and Settings
2.6.2 Results of Model Evaluation
2.6.3 Results of Algorithm Evaluation
2.7 Conclusion
References
Chapter 3: Data Transmission Empowered by Edge Computing
3.1 Introduction
3.2 Related Work
3.3 Motivation
3.3.1 Uncovering Missing Data Issue in Large-Scale ITSs
3.3.2 Experimental Explorations of Spatio-Temporal Correlations on Traffic Data

3.3.3 Implementation Dilemmas for Large-Scale Traffic Recovery
3.4 System Model and Problem Formulation
3.4.1 System Model of Edge Computing
3.4.2 Problem Formalization
3.5 System Design
3.5.1 Sub-optimal Deployment of Edge Nodes
Problem Reformulation
Local Search-Based Suboptimal Deployment
3.5.2 Accurate Traffic Data Recovery Based on Low-Rank Theory
Experimental Analysis of Low-Rank
Accurate Traffic Recovery Based on Low-Rank Theory
3.6 Traces-Based Evaluations
3.6.1 Experimental Methodology and Settings
Dataset and Experimental Methodology