群智感知中面向移动群体的参与者选择优化模型

随着短视频时代的来临,移动群智感知任务的视频化程度越来越高,在传统研究中常利用机会网络和移动网络激励任务的分发和数据的收集,但机会网络中节点移动的不可控性,以及视频任务内容传输的高代价性都使得这些方法的实用性大大降低.针对此问题,利用社会移动群体规律性的自主聚集、活动范围大等特点,提出一种面向社会移动群体的群智感知参与...

Participant selection optimization model for mobile groups in crowdsensing

With the advent of short video era, mobile group awareness tasks are becoming more and more videointensive. Opportunity networks and mobile networks are often used in traditional research to motivate task distribution and data collection. However, the uncontrollability of node movement in the opportunity network and the high cost of video task content transmission make these methods less practical. In order to solve this problem, an optimization model of crowdsensing perception participant selection for social mobile groups is presented, which takes advantage of the regularity of autonomous clustering and large range of activities of social mobile groups. The density clustering algorithm is used to divide the cluster centers according to the locations of similar tasks, so as to divide the metro stations belonging to the task subarea. It includes an optimization algorithm for participants based on user incentive cost and an optimization algorithm for participants based on number of users. The simulation results show that a task distribution scheme with fewer participants can be selected using less system resources than similar algorithms.