LAUSR

In mobile crowd sensing, the mobile terminal integrates a variety of widely distributed sensing devices and communication ports. Sensing devices and communication ports can collect and share all kinds of perception data. However, inherent contradictions exist among perceived ability, communication port, and moving rule while collecting real-time and accurate sensing information. This article mainly focused on recruited and selected mobile nodes and assigned sensing tasks to improve the quality of sensing information. The optimization of the implementation stage of the sensing task is beyond the scope of this study. This article proposes a utility-based sensing task decomposition and subcontract algorithm, which is a method of sensing data acquisition that establishes direct collaboration between mobile nodes. A mobility model based on Markov chain is established to forecast the spatial distribution of sensing nodes. A utility function is designed to estimate the sensing task execution capacity of sensing nodes based on spatial distribution and tempo-spatial coverage of the collected data. The sensing tasks are then decomposed and subcontracted to neighboring nodes according to the utilities of the neighboring nodes to the decomposed sensing tasks. This method improves the quality of sensing data in terms of sensing data coverage and finished ratio of sensing task.