Privacy-Aware Service Subscription in People-Centric Sensing:A Combinatorial Auction Approach

摘要

With the emergence of ambient sensing technologies which combine mobile crowdsensing and Internet of Things,large amount of people-centric data can be obtained and utilized to build people-centric services.Note that the service quality is highly related to the privacy level of the data.In this paper,we investigate the problem of privacy-aware service subscription in people-centric sensing.An efficient resource allocation framework using a combinatorial auction(CA)model is provided.Specifically,the resource allocation problem that maximizes the social welfare in view of varying requirements of multiple users is formulated,and it is solved by a proposed computationally tractable solution algorithm.Furthermore,the prices of allocated resources that winners need to pay are figured out by a designed scheme.Numerical results demonstrate the effectiveness of the proposed scheme.