偏好感知的边云协同群智感知参与者选择策略

doi:10.19665/j.issn1001-2400.2022.01.014

Abstract

Abstract:

Crowdsensing relies on the mobility of a large number of users and the sensing ability of intelligent devices to complete data collection,which has become an effective way of sensing data collection.In the existing crowdsensing network,the cloud platform is responsible for the whole process of task distribution and data collection,so it is difficult to effectively process a large amount of real-time data and the sensing cost is high.Different participants have different interests in tasks.Ignoring preference factors will lead to a low efficiency and poor satisfaction of selected participants.Aiming at the problems existing in the above crowdsensing network,a preference aware participant selection strategy under the edge-cloud collaborative architecture is proposed.The participant selection process is performed by the cloud platform and edge nodes in collaboration.The cloud platform distributes tasks to edge nodes based on different locations of tasks,and collects data from edge nodes.The edge node is responsible for the participant selection process,quantifying the user's preference for the task by evaluating the time matching degree,distance matching degree,task type and reward,and quantifying the user's preference for the task by evaluating the user's reputation and sensing cost.Based on the bilateral preference and stable matching theory,the participant selection problem is modeled as a many-to-one stable matching problem between users and tasks,with the stable matching solved to maximize the participant preference.The results show that the participants selected by this strategy have high satisfaction and that the data quality collected by the platform is good.

Key words: crowdsensing, edge cloud collaboration, participant selection, data quality, user preference

CLC Number:

TN929.5

WANG Ruyan,LIU Jia,HE Peng,CUI Yaping. Preference aware participant selection strategy for edge-cloud collaborative crowdsensing[J].Journal of Xidian University, 2022, 49(1): 142-151.

Figures/Tables 7

References 23

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参数名称	参数值	参数名称	参数值
感知区域大小/m²	1 000	用户感知成本	1~3
整个感知周期/min	30	用户历史感知任务次数	5
用户数量	10~300	感知数据质量	0~1
任务数量	10~50	感知任务类型数	3
时间匹配度	0~1	用户感知时间/min	5~15
距离匹配度	0~1	单个任务预算	5~15
任务需求用户数量	1~5

Preference aware participant selection strategy for edge-cloud collaborative crowdsensing

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