A Time and Location Correlation Incentive Scheme for Deep Data Gathering in Crowdsourcing Networks

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  • Fulong Ma
    School of Information Science and Engineering, Central South University, Changsha 410083, China
  • Xiao Liu
    School of Information Science and Engineering, Central South University, Changsha 410083, China
  • Anfeng Liu
    School of Information Science and Engineering, Central South University, Changsha 410083, China
  • Ming Zhao
    School of Software, Central South University, Changsha 410083, China
  • Changqin Huang
    School of Information Technology in Education, South China Normal University, Guangzhou 510631, China
  • Tian Wang
    College of Computer Science & Technology, Huaqiao University, Xiamen, Fujian Province 361021, China

Abstract

<jats:p>To tackle the issue in deep crowd sensing, a Time and Location Correlation Incentive (TLCI) scheme is proposed for deep data gathering in crowdsourcing networks. In TLCI scheme, a metric named “Quality of Information Satisfaction Degree” (QoISD) is to quantify how much collected sensing data can satisfy the application’s QoI requirements mainly in terms of data quantity and data coverage. Two incentive algorithms are proposed to satisfy QoISD with different view. The first algorithm is to ensure that the application gets the specified sensing data to maximize the QoISD. Thus, in the first incentive algorithm, the reward for data sensing is to maximize the QoISD. The second algorithm is to minimize the cost of the system while meeting the sensing data requirement and maximizing the QoISD. Thus, in the second incentive algorithm, the reward for data sensing is to maximize the QoISD per unit of reward. Finally, we compare our proposed scheme with existing schemes via extensive simulations. Extensive simulation results well justify the effectiveness of our scheme. The QoISD can be optimized by 81.92%, and the total cost can be reduced by 31.38%.</jats:p>

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