Multi-dimensional Urban Sensing Using Crowdsensing Data

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
2.5.1 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
Baseline Methods and Evaluation Metrics
3.6.2 Experimental Results
Evaluations of Edge Node Deployment
Evaluations of Traffic Recovery
3.7 Discussion and Future Work
3.8 Conclusion
References
Part II: How to Use Crowdsensing Data for Smart Cities (Multi-dimensional Applications)
Chapter 4: Environmental Protection Application: Urban Pollution Monitoring
4.1 Introduction
4.2 Related Work
4.3 System Model and Problem Description
4.3.1 System Model
4.3.2 Problem Description
4.4 Iterative Truthful-Source Identification Algorithm
4.4.1 Algorithm Design
Truthful Probability Estimation
Sensor Efficiency Estimation
4.4.2 Algorithm Description and Analysis
4.5 Experimental Evaluations
4.5.1 Simulation Methodology and Settings
4.5.2 Performance Evaluations
Algorithm Convergence
Algorithm Robustness
Performance Impact of the Number of Users
4.6 Discussion
4.7 Conclusion
References
Chapter 5: Urban Traffic Application: Traffic Volume Prediction
5.1 Introduction
5.2 Related Work
5.2.1 Reusing Building Data
5.2.2 Traffic Volume Prediction
5.2.3 Traffic Prediction Models
5.3 System Overview
5.4 Building-Traffic Correlation Analysis with Multi-Source Datasets
5.4.1 Correlation Analysis with Building Occupancy Data
Correlation Quantification with Metrics
Correlation Verification with Google Maps
5.4.2 Correlation Analysis with Environmental Data
5.5 Accurate Traffic Prediction with Cross-Domain Learning of Building Data
5.5.1 Problem Formulation
5.5.2 Attention Mechanisms-Based Encoder-Decoder Recurrent Neural Network
Encoder with Cross-Domain Attention
Occupancy Component
Environmental Component
Decoder with Temporal Attention
5.6 Performance Evaluation
5.6.1 Experimental Methodology and Settings
Dataset Description
Baseline Methods
Evaluation Metrics and Parameter Settings
5.6.2 Experimental Evaluations
Evaluations on Overall Prediction Results
Comparisons with Baselines
Evaluation of Parameters
Evaluation of Cross-Domain Learning
Evaluation of Attention Weight
Extensive Evaluations of Comparison
Baseline Methods with the Weekday´s Data and the Weekend´s Data
Comparison with State-of-the-Art Methods Based on Different Data Sources
5.7 Discussion
5.7.1 Applicable Conditions of BuildSenSys
5.7.2 Sensing Coverage of BuildSenSys
5.7.3 Extension to Large-Scale Scenarios
5.7.4 Privacy and Security of Building Data
5.8 Conclusion
References
Chapter 6: Airborne Sensing Application: Reusing Delivery Drones
6.1 Introduction
6.2 Related Work
6.2.1 Crowdsensing Based on Other Mobile Devices
6.2.2 Crowdsensing Based on Drones
Summary
6.3 Models and Problem Formulation
6.3.1 Delivering and Sensing Models for Delivery Drones
6.3.2 Energy Consumption Model for Delivery Drones
On-Site Experimental Explorations
Energy Consumption Model
Model Analysis
6.3.3 Problem Formulation
RT Problem
RTW Problem
Objective and Constraints
Challenges
6.4 Solution Design
6.4.1 Overview
6.4.2 Route-Time Joint Allocation Algorithm
Equivalent Objective Function Construction
Approximation Algorithm for Route-Time Joint Allocation
6.4.3 Route-Time-Weight Joint Optimization Algorithm
6.4.4 Theoretical Analysis of Algorithms
Analysis of Objective Function and Constraints
Analysis of Algorithm Performance
6.5 Experimental Evaluations
6.5.1 Traces-Based Simulations
Evaluation Methodology and Settings
Performances of RT-alg Algorithm
Performances of RTW-Algorithm
Case Study of Drone-Based AQI Monitoring
6.5.2 Field Experiments
System Implementation
Experimental Settings and Baselines
Experimental Results
Summary
6.6 Discussions and Future Works
6.6.1 Generalizing Drone Route Model
6.6.2 Incentives Behind Drone Sharing
6.6.3 Impacts of Delivery Delay
6.6.4 Factors in Sensing Utility
6.6.5 Impact of Sensors and Battery Weight
6.7 Conclusion
References
Part III: Open Issues and Conclusions
Chapter 7: Open Issues
7.1 Exploring New Urban Applications
7.2 Utilizing More Crowdsensing Data
7.3 Concerning Privacy Protection
References
Chapter 8: Conclusions