# A Task-Centric Cooperative Sensing Scheme for Mobile Crowdsourcing Systems
## 1 Introduction  
With the development of mobile Internet, mobile crowdsourcing systems (MCSs) have become increasingly popular in recent years. The primary notion of a MCS is to select ordinary citizens to collect and share sensory data from their surroundings by using their resource-constrained smart devices for the purpose of task monitoring or addressing a particular problem. While participating in MCS tasks, users consume their own resources such as battery, physical storage and network flow. It is essential to conserve the smartphone's resource in order to increase the sensing efficiency and prolong the lifespan of mobile crowd sensing systems. The long-term MCS resource expenses may have a positive correlation to the number of active participants. Therefore, the MCS should use as few active participants as possible. To achieve this aim, a portion of the requested users could be selected as active participants to carry out the sensing task. The residual users are considered as idle ones with low resource consumption until they are selected in subsequent task cycles. From a data quality perspective, data integrity is likely to be negatively affected when only a part of the users are selected to live-sample the data. Fortunately, when the requested users aredensely distributed in the sensing region, it can be generally assumed that the data measurements at neighboring places tend to be well correlated. Therefore, the missing data samples may be reliably predicted based on the actual sensing data submitted by other participants in neighboring regions. Undoubtedly, such a prediction will result in an approximation error due to imperfect correlations or noise. In this paper, we utilize this approximation error to formulate the data integrity loss caused by particular participant selection schemes. The data integrity is better if the approximation error is less. The first question in this paper can be formulated as follows:  
1. _How to select an optimal number of selected users as active participants while maintaining the overall sensing data integrity above a preset threshold?_  
Previously, the spatial correlation was modeled using the sensing coverage region of each sensor. If user A's coverage region overlaps with the union of the coverage regions of neighboring users, then the user set excluding A can be selected as active participants without affecting the overall sensing coverage, and hence the overall data integrity. As such, participant selection becomes a problem of pruning the minimum number of requested users while maintaining sufficient sensing coverage. The participant selection problem is similar to the sleep schedule methods of sensors in wireless sensor networks (WSNs). Among the full-coverage-based sleep scheduling methods, LDS is proposed for clustered WSNs [1]. The main idea of the LDS algorithm is quite plain: based on the fact that the RF transmission power between an ordinary node and its cluster head node has a positive correlation to the distance between these two nodes, nodes are selected to sleep according to their relative distance to the cluster head. Hence, nodes farther apart from the cluster head are given higher probability to be put into sleep mode. However, static WSNs are obviously distinct from mobile participatory sensing systems because nodes in mobile participatory sensing systems possess incoercible mobility modes and incentive demands. Recently, having noticed the lack of participant selection methods, several participant-selection schemes have been presented by different researchers. [PERSON] _et al._ proposed a coverage maximization algorithm that records participants' tracks and selects participants whose availability matches the campaign coverage constraints [2]. [PERSON] _et al._ exploited the stability of user behaviors and selected participants based on the fitness of their mobility history profiles [3].