Jia Xu

Research on Monetary Incentive Mechanisms for Crowdsensing based on Algorithmic Game Theory, 2019.1-2022.12, NSFC.

Crowdsensing provides a promising paradigm for large scale and highly complex data sensing due to the prominent advantages such as low cost, good scalability, pervasive application scenarios. The incentive mechanisms should not only stimulate the users to participate in the crowdsensing, but also improve the profit of platform, data quality, the ability of privacy protection, and the adaptability for various complex application scenarios. However, existing study of incentive mechanism design for crowdsensing is insufficient. Focusing on and breaking through three key problems in crowdsensing: data quality formalization, privacy protection based incentive mechanism framework, and user cooperative behavior abstraction and compatibility model construction, this project comprehensively utilizes the achievements and powerful tools from auction theory, contract theory, graph theory, algorithm design theory, frugality theory, voting theory, and online social network to studiy the design theory and methods of frugality theory based incentive mechanisms, the quality aware incentive mechanisms, privacy protection based incentive mechanisms, and incentive mechanisms towards complex tasks. The objective of this project is providing the innovative design methods of algorithmic game theory based monetary incentive mechanisms for crowdsensing, improving the business value, data quality, privacy protection ability, and handling ability for complex application scenarios of crowdsensing system. The research results of this project are expected to provide theoretical basis and technical supports for the development and popularization of novel crowdsensing applications.

Research on Opportunistic Data Collection Scheme based on Social Behavior Analysis in Crowd Sensing, 2015.1-2018.12, NSFC.

With the development of society, the desire for thorough sensing is more and more strong. As a new sensing mode, crowd intelligence sensing has been a hot research area because of prominent advantages, such as wide coverage, low cost and strong scalability. As so far, the study of crowd intelligence sensing is still in the initial stage, and the theory and method of closely related research area are inefficient in such scenario. Focusing on three key problems in crowd intelligence sensing: social behavior abstract, participant recruitment and opportunistic data collection, this project research the abstract model and method for user behavior based on mobility profile, maps the actual behavior to calculable and verifiable virtual behavior space; We explore the mechanism of participant recruitment, design the recruitment framework based on social behavior analysis, and propose coverage and reputation based recruitment respectively; Based on the data collection mode which guided by the user behavior characteristics, low load distributed routing protocols and algorithms are proposed. Besides the theoretical proof and simulation, we also develop a social behavior analysis based haze monitoring crowd intelligence sensing system in order to test the efficiency of our study. The project concentrates on reducing data sensing cost, expanding the sensing coverage, increasing the user engagement and improving data quality and data collection performance in crowd intelligence sensing. The results of project will provide theoretical basis and technical support for development and popularization of crowd intelligence sensing applications.

Other Projects

  • Research on Dynamic Distributed Deployment and Cooperation of Mobile infrastructure in WSN, 2012.1-2014.12, NSFC
  • Reseach on Key Technologies and Platform of Emergency Rescue based on Crowd Sensing, 2013.6-2016.6, Scientific and Technological Support Project (Society) of Jiangsu Province
  • Research on Key Technologies of Crowd Sensing based on Social Behavior Analysis, 2014.7-2017.6, Natural Science Foundation of Jiangsu Province
  • Research on Models and Algorithms of Data Dissemination in Delay tolerant Mobile Sensor networks, 2013.8-2015.8, Postdoctoral Special Support of China