Abstract
As an appealing sensing paradigm, Mobile Crowd Sensing (MCS) which provides a cost-efficient solution for large-scale urban sensing tasks has gained significant attention in recent years. However, in practice, many MCS applications usually suffer from the failure of sensing task execution, ranging from the randomness and autonomous in participant users’ behavior, to lacking of prior experience and monetary reward, etc. To mitigate the impact of these failures, in this paper, we propose and study a novel problem, namely failure-aware mobile crowd sensing. To solve our problem, we devise a two-stages framework, including offline task allocation and online task transfer. Towards enhancing task completion ratio, we propose an indeterminate fitness proportionate based task allocation approach FPSAll , and an utility evaluation-based task transfer approach FTASKTraf , respectively. Through extensive experiments, we demonstrate the efficiency and effectiveness of our proposed approaches on real-world data set.
Highlights
With the dramatic proliferation of sensor-equipped portable mobile devices and wireless communication, a novel sensing paradigm named Mobile Crowd Sensing (MCS) [1]–[3] has become an effective way to sense and collect data about physical environment and human society
Towards enhancing task completion ratio, we propose an indeterminate fitness proportionate based task allocation approach FPSAll, and an utility evaluation-based task transfer approach FTASKTraf, respectively
We propose task allocation approach FPSAll and task transfer approach FTASKTraf in Section IV and V, respectively
Summary
With the dramatic proliferation of sensor-equipped portable mobile devices and wireless communication, a novel sensing paradigm named Mobile Crowd Sensing (MCS) [1]–[3] has become an effective way to sense and collect data about physical environment and human society. According to survey from Amazon MTurk and FigureEight platform, task abandonment is very common, accounting for up to 164% abandoned tasks relative to finished tasks [15] Both them will inevitably cause task failure, and no result will be returned to the requestors. Suppose one failed participant user, we will harness social relationship to transfer the unimplemented tasks to his/her acquaintances. The intuition is that, social relationship could be leveraged to encourage participation in mobile crowd sensing, as well as improve the performance of task allocation and implementation [16]–[19]. We observe that both the initial executors and successors insist that the relative incentive rewards should be divided between them, instead of exclusively initial executors or successors These results validate our assumption, and hereafter, we will follow these observations to devise our transfer approach.
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