# A Task-Centric Cooperative Sensing Scheme for Mobile Crowdsourcing Systems
## 1 Introduction
Similarly, most of the existing methods focus on utility maximization or coverage maximization with a constraint budget, but in such user selection methods, participation fairness has not been considered, which indicates a fraction of participants that may complete most of the tasks with a lower price. In this paper, we propose an explicitly _task-centric_ correlation-measuring approach for the spatial correlation between the sample data submitted by participants in neighboring regions/grids. We then make use of this measured correlation to obtain participant selection schemes. The proposed approach in this paper is based on the following argument: _a user in a grid is eligible to set to be idle if its measurements can be recovered by the sensing data of the remaining active participants_. The participant selection problem can be rephrased as how to identify this subset of requested users that can be set to idle. In this paper, we consider this participant selection issue as a constrained optimization problem: _optimizing the number of active participants subject to the constraint that the performance loss is bounded by a given threshold_. An exhaustive search for the process of the selection problem may result in non-polynomial time computation complexity and is impractical. Therefore, we propose a novel participant selection scheme algorithm TCPS which possesses a practical polynomial time complexity. The algorithm explicitly measures the approximation error and regards it as a measure to recursively select participants in grids as active ones. To further reduce the resource consumption and prolong the lifespan of smart devices, we consider the sampling rate of a smart device can be adjusted based on the requirements of different tasks. Thus, to reduce resource consumption of MCS the sampling rate can be lowered in some tasks. Data integrity can be guaranteed by predicting the missing data. However, this may bring in some inaccurate data due to the prediction error. In addition, it is possible that some users may be always selected to participate while others may always be idle. Thus, we consider the second research question as below:2. _How to select an optimal number of participants with some inaccurate data so that the fairness of participant selection and resource conservation can be achieved, while maintaining adequate sensing data quality?_  
To maintain the fairness for participants, the MCS organizer will compute the total participation time of all users and the average participation time at the beginning of participant selection, and then queue all users based on the total participation time. Then the organizer will select participants from the front to the end of the queue. If the participation time of selected users exceeds a certain proportion of the average time, they will be put into the end of the queue. In this paper, this set-partitioning problem is regarded as a constrained min-max optimization problem. We propose an improved version of the polynomial time algorithm based on problem (a). In this algorithm, the error caused by the inaccurate data is separated from the total error and then the sensing data quality threshold is adjusted based on it. In this paper, preliminary experiments are conducted to evaluate performance of task-centric participant selection schemes and analyze important elements that affect their performance. The remaining of this paper is organized as follows: in Section 2, we discuss related works on the participant selection problem in MCS. In Section 3, we discuss the spatial correlation of neighboring sensing data and formulate the participant selection problem. In Section 4, the task-centric participant selection algorithms are proposed. We report the experimental results in Section 5, as well as analyze some performance comparisons. Conclusions are presented in Section 6.