ActiveCrowd: A Framework for Optimized Multitask Allocation in Mobile Crowdsensing Systems

Abstract

Abstract-Worker selection is a key issue in Mobile Crowd Sensing (MCS). While previous worker selection approaches mainly focus on selecting a proper subset of workers for a single MCS task, multi-task-oriented worker selection is essential and useful for the efficiency of large-scale MCS platforms. This paper proposes ActiveCrowd, a worker selection framework for multi-task MCS environments. We study the problem of multi-task worker selection under two situations: worker selection based on workers' intentional movement for time-sensitive tasks and unintentional movement for delay-tolerant tasks. For time-sensitive tasks, workers are required to move to the task venue intentionally and the goal is to minimize the total distance moved. For delay-tolerant tasks, we select workers whose route is predicted to pass by the task venues and the goal is to minimize the total number of workers. Two Greedy-enhanced Genetic Algorithms are proposed to solve them. Experiments verify that the proposed algorithms outperform baseline methods under different experiment settings (scale of task sets, available workers, varied task distributions, etc.).

Multitask Allocation to Heterogeneous Participants in Mobile Crowd Sensing

“…In [17], the goal is to select a subset of participants to maximize the quality of information under budget constraints. Guo et al [18] proposed a worker selection framework for time-sensitive and delay-tolerant tasks. Time-sensitive tasks are aimed at minimizing the distance traveled by workers.…”

“…Inspired by work [18], we design a greedy strategy to select users. We firstly select the users that preload the sensor that the task required and move to the location of tasks within the shortest possible time.…”

CrowdTravel: scenic spot profiling by using heterogeneous crowdsourced data

“…Recently, the application of crowd intelligence has expanded rapidly Daren et al 2012;Dong et al 2014;Bin et al 2016b;Longbiao et al 2016;Jiangtao et al 2016Jiangtao et al , 2017Huihui et al 2017), including event discovery and characterization, business intelligence, activity recommendation, and so on. Researchers are especially attracted to the domain of tourism where a large number of users contribute rich information via social media.…”

HyTasker: Hybrid Task Allocation in Mobile Crowd Sensing

“…Task allocation or worker selection is one of the major challenges in MCS [12], which has a significant impact on the efficiency and quality of the sensing tasks [15], [18], [19], [20], [26]. Recently, there have been many studies on MCS task allocation, such as [21], [22], [23], [24], [25], [29], [30], [34], [36], [37], which can be divided into two categories based on the workers' movement patterns and participation mechanisms [2], [13], [15], [49].…”