Energy harvesting and computing enabled data broadcasting in mobile social networks

With the wide use of mobile phones, data broadcasting in mobile social networks benefits from the cooperative relaying among users. The performance of data broadcasting is often affected by 2 tough challenges, ie, inefficient data broadcasting caused by limited communication ranges of devices and intermittent network connectivity, and uncooperative data delivery as the energy cost for data broadcasting and algorithm computing. This paper explores the collaboration of communication, computing, and energy to improve the performance of data broadcasting and cope with the energy cost for communication and computation. We propose a data broadcasting scheme with the cooperation of communication and computing, which contains the relay selection and data dissemination algorithms. In the relay selection algorithm, social knowledge is exploited to measure user's capacity of data broadcasting, and the relay selection is formulated as a maximum weighted independent set problem. The data dissemination is integrated with algorithm computing by using edge computing among the relay nodes. Moreover, an energy allocation scheme under limited energy constrains is proposed to balance the energy consumed by data broadcasting and algorithm computing. With the aid of base station, we integrate radio frequency-based energy harvesting and beamforming technologies into mobile social networks. A Markov decision process-based energy transmission scheme is designed to supplement devices' energy capacity. Finally, simulation results indicate that the proposed schemes effectively improve the performance of data broadcasting and provide energy supplement for communication and computation.