Part I: How to Collect Crowdsensing Data (Multi-dimensional fundamental issues) 1. User Incentives---Incentivizing Platform-users with win-win effects 1.1 Introduction 1.2 Related Work 1.3 System Model and Problem 1.3.1 System Model 1.3.

2 Example of Personalized Bidding Scenario 1.3.3 Problem Formalization 1.4 Picasso: The Incentive Mechanism 1.4.1 Bid Description in 3-D Space 1.4.2 Construction of Task Dependency Graph 1.

4.3 PB Decomposition for Efficient Task Allocation 1.4.4 PB Recombination for Strategy-proof Payment 1.5 Performance Evaluations 1.6 Conclusions References 2. Data Transmission Empowered by Edge Computing 2.1 Introduction 2.

2 Related Work 2.3 Experimental Explorations 2.3.1 Uncovering Missing Data Issue in Large-Scale ITSs 2.3.2 Experimental Explorations of Spatiotemporal Correlations on Traffic Data 2.4 System Model and Problem 2.4.

1 System Model of Edge Computing 2.4.2 Problem 2.5 GTR: A Large-scale Data Transmission based on Edge Computing 2.5.1 Suboptimal Deployment of Edge Nodes 2.5.2 Accurate Traffic Data Recovery Based on Low-Rank Theory 2.

6 Performance Evaluations 2.7 Conclusions References 3. Data Calibration---Calibrate Without Calibrating 3.1 Introduction 3.2 Related Work 3.3 System Model and Problem 3.4 Auto-calibration Algorithm based on Two-level Iteration 3.4.

1 Algorithm Overview 3.4.2 Outer Loop 3.4.3 Inner Loop 3.4.5 Convergence and Optimality Analysis 3.6 Performance Evaluations 3.

7 Conclusions References Part II: How to Use Crowdsensing Data for Smart Cities (Multi-dimensional applications) 4. Communication Service Application---Wireless Spectrum Map Construction 4.1 Introduction 4.2 Related Work 4.3 Understanding RSS Measurement Error in Smartphone 4.3.1 Experiment Design 4.3.

2 Experiment Observation 4.5 CARM: Crowdsensing Accurate Outdoor RSS Maps with Error-prone Smartphone Measurements 4.5.1 System Overview 4.5.2 Iterative Estimation of Model Parameters 4.5.3 Model-Driven RSS Map Construction 4.

5.4 Algorithm Analysis 4.6 Performance Evaluations 4.7 Conclusions References 5. Environmental Protection Application---Urban Pollution Monitoring 5.1 Introduction 5.2 Related Work 5.3 System Model and Problem 5.

3.1 System model 5.3.2 Problem 5.4 Iterative Truthful-source Identification Algorithm 5.4.1 Algorithm design 5.4.

2 Algorithm description and analysis 5.5 Performance Evaluations 5.6 Conclusions References 6. Urban Traffic Application---Traffic Volume Prediction 6.1 Introduction 6.2 Related Work 6.3 System Overview 6.4 Building-Traffic Correlation Analysis with Multi-Source Datasets 6.

4.1 Correlation Analysis with Building Occupancy Data 6.4.2 Correlation Analysis with Environmental Data 6.5 Accurate Traffic Prediction with Cross-Domain Learning of Building Data 6.5.1 Model and Problem 6.5.

2 Attention Mechanisms-Based Encoder-Decoder RNN 6.6 Performance Evaluations 6.7 Conclusions References Part III: Open Issues and Conclusions 7. Open Issues and Conclusions 7.1 Open Issues 7.1.1 More Crwodsensing Data 7.1.

2 New Urban Applications 7.1.3 Privacy Protection 7.2 Conclusions References.