Reputation-based sensing-as-a-service for crowd management over the cloud

Cloud computing model can enable provisioning of sensing services through mobile phones, namely Sensing-as-a-Service (S 2 aaS). In this paper, we study S 2 aaS over social networking services for crowd management problem where malicious users report false sensor readings leading to severe disinformation at the crowd control platform. To this end, we propose Trustworthy Sensing for Crowd Management (TSCM) which is a reputation-based crowd management scheme over the cloud platform where sensing data is collected from smart phones based on an auction mechanism. TSCM periodically runs an auction in order to assign dynamically arriving sensing task requests to the smart phone users forming a crowd connected through a social network. User bids, task values and user reputation values are taken as the inputs whereas the outputs are the utility of the crowd management platform and the average utility per user while reputation of a user is a function of the accuracy of the sensed data. Through simulations, we show that TSCM significantly improves the platform utility while degrading the ratio of the maliciously crowdsourced task by 75%. Furthermore, we also show that under TSCM, reputation of malicious users converge to a low value at the order of 40% following a few auctions.