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Machine learning has widely been used in crowdsourcing systems to analyze the behavior of their mobile users. However, it naturally raises privacy concerns, as personal data needs to be collected and analyzed in the cloud, and results need to be sent back to the users to improve their local estimates. In this paper, we focus on the use of a specific type of learning algorithms, called maximum a posteriori...
At events, such as conferences, indoor localization is both contextual and ephemeral, in that localization is only needed within the context of and for the duration of the event. As such, the costs and requirements of providing such services need to be minimal. In this paper, we design, implement, and evaluate Tack, a new mobile application framework that is specifically engineered to support such...
With mobile offloading, computation-intensive tasks can be offloaded from mobile devices to the cloud to conserve energy. In principle, the idea is to trade the relatively low communication energy expense for high computation power consumption. In this paper, we propose that computation-intensive tasks can be distributed among nearby mobile devices, and focus on the case that a group of mobile users...
With an abundance of computing resources, cloud computing systems have been widely used to elastically offload the execution of computation-intensive applications on mobile devices, leading to performance gains and better power efficiency. However, existing works have so far focused on one application only, and multiple applications are not coordinated when sending their offloading requests to the...
Cognitive radio is promising in improving spectrum efficiency by enabling unlicensed users to access to the licensed spectrum. Spectrum auction is perceived as a potential way to realize it. Primary users (PUs) act as sellers selling unused spectrum bands and secondary users (SUs) act as buyers who intend to get spectrum bands from PUs. In situations where multiple PUs and SUs exist, double auction...
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