Performance Characterization of Machine-to-Machine Networks With Energy Harvesting and Social-Aware Relays

In this paper, we propose a large-scale machine-to-machine (M2M) network architecture that incorporates energy harvesting and social-aware relays. The relay is powered by harvesting radio frequency energy and adopts the simultaneous wireless information and power transfer strategy. For the social aspect, a relay is conversant with only some of the communities and will only assist the data transfer of conversant sources. Moreover, for the energy harvesting and social-aware relay that assists the cooperative transmission protocol, we propose two different relay selection strategies, that is, social-aware random relay selection and social-aware best relay selection. The outage probability and network throughput of the proposed protocols are derived in a closed form using the stochastic geometry model, where the multiple M2M transmitter-receiver pairs and relays form independent homogeneous Poisson point processes, respectively. By comparing with the situation without social awareness, we find that social awareness can improve performance in some situations. The theoretical analysis is validated by extensive simulations.