# A Task-Centric Cooperative Sensing Scheme for Mobile Crowdsourcing Systems
## 2 Related Works  
Some early mobile participatory sensing models do not support multiple tasks so that the participants are only assigned some special tasks [4; 5]. Thus, it's hard to accomplish massive task allocation for early models because many of them lack proper selection methods and randomly choose participants. Since [PERSON] _et al._ firstly presented a participant selection scheme for multiple tasks [6], the incentive mechanism has been taken into consideration in recent multitask-oriented systems [7; 8]. In some sense, the collaboration of sensors in wireless sensor networks can be regarded as a selection problem. In [9; 10; 11], approaches are designed to determine the time needed for activating sensors and the location to place them. However, the mobility of participants and the special incentive demands lead to huge differences between WSNs and participatory sensing. As for selecting participants, several strategies make good use of participants' traces. [PERSON] _et al._ concentrated on initiating the process of collecting data in the special area called \"bubbles\" [12]. In [2], a coverage maximization algorithm is designed to record the tracks of participants and choose some participants with availability to meet the requirements. Similarly, the traces of participants are assumed to be captured ahead of time or only taken into consideration during the occurrence of the sensing activity [3; 13; 14; 15]. However, these approaches depend too much on the participant trajectories, which may result in the privacy leakage. Collaborative sensing is utilized to reduce energy consumption or substitute low-energy sensors by the high-energy sensors in these schemes [16; 17; 18; 19; 20; 21; 22]. Some mechanisms focus on choosing the most valuable participants in the sensing task. A heuristic greedy algorithm is proposed to choose a minimum of participants to fulfill the assigned tasks after quantifying the process of sensing data collection and estimating the collected data scale. This strategy always selects those whose ratio between data value and the incentive cost is maximum with the iterative approach until the budget is exhausted or all tasks are accomplished [23; 24]. A geocast mechanism is introduced to accomplish effective task assignment. In this scheme, a greedy heuristic algorithm is designed to select the candidates who can produce the maximizing revenue at every round when the fact that participants are willing to complete the nearby task is taken into consideration [25]. The location prediction technique has been used in the process of task allocation. Offline and online algorithms are presented to solve the participant recruitment problem (PRP) that is how to select the least number of participants required in order to fulfill the given task. The historical data is utilized for the estimation because it's hard to determine the time and place of participants in real sensing tasks in advance. The algorithm will greedily choose the participants to make the coverage improvement reach a maximum if the current tasks are not finished [26]. Different from previous methods, some systems pay more attention to providing a certain degree of location privacy for participants. A novel two-stage optimization approach is designed to protect the location privacy in the spatial task assignment via utilizing cloaked locations in the global optimization while the precise locations are adopted in a subsequent local optimization [27]. A three-stage recruitment framework is proposed to reduce the privacy risk by decreasing the exposure of participants' location and context information. In this approach, we focus more on selecting well-suited participants by using geographic and temporal information decided by profiles regarding the transmission and location as well as time [28]. Aiming at identifying suitable tasks to reduce superfluous task expenses, many approaches have been proposed.