  The advancements in peer-to-peer wireless power transfer (P2P-WPT) have
empowered the portable and mobile devices to wirelessly replenish their battery
by directly interacting with other nearby devices. The existing works
unrealistically assume the users to exchange energy with any of the users and
at every such opportunity. However, due to the users' mobility, the inter-node
meetings in such opportunistic mobile networks vary, and P2P energy exchange in
such scenarios remains uncertain. Additionally, the social interests and
interactions of the users influence their mobility as well as the energy
exchange between them. The existing P2P-WPT methods did not consider the joint
problem for energy exchange due to user's inevitable mobility, and the
influence of sociality on the latter. As a result of computing with imprecise
information, the energy balance achieved by these works at a slower rate as
well as impaired by energy loss for the crowd. Motivated by this problem
scenario, in this work, we present a wireless crowd charging method, namely
MoSaBa, which leverages mobility prediction and social information for improved
energy balancing. MoSaBa incorporates two dimensions of social information,
namely social context and social relationships, as additional features for
predicting contact opportunities. In this method, we explore the different
pairs of peers such that the energy balancing is achieved at a faster rate as
well as the energy balance quality improves in terms of maintaining low energy
loss for the crowd. We justify the peer selection method in MoSaBa by detailed
performance evaluation. Compared to the existing state-of-the-art, the proposed
method achieves better performance trade-offs between energy-efficiency, energy
balance quality and convergence time.
