Proximal and social-aware device-to-device communication via audio detection on cloud

Abstract

Device-to-Device (D2D) communication is a potential strategy to release the mobile network from unnecessary data transfer, accelerate the responsiveness of end-to-end apps, and decentralize the provisioning of traditional services. D2D coordination is a critical challenge, which cannot be overcome without the explicit intervention of the user as D2D communication represents a threat for user's privacy. However, social attributes can be leveraged to equip the devices with trusted mechanisms that can automate D2D communication. In this paper, we build and design a mobile cloud system that relies on audio data obtained from user's environment to determine whether a set of devices are located in proximity. Audio analysis is performed on the cloud using classical machine learning principles, and the cloud instance (server) also informs the devices about the coordination plan to establish D2D communication. The framework is evaluated using a smartphone app for sharing files and the evaluation shows that the approach is feasible in practice.