POF: Probability-Driven Opportunistic Forwarding for Bike-sharing System

The opportunistic networks and the bike-sharing systems have been attracting much research attention in these years. In this paper, the cycling trips and the buffers of bike stations are utilized to relay the large volume of data. The Markov chain is exploited to formulate and estimate the delivery ratio with the mathematical lattice model. Then, the probability-driven opportunistic forwarding (POF) scheme, which calculates the delivery potentials from the possible delivery paths, is proposed. The relay evaluations, which consider the proper sequence of cycling trips, are made to support the data forwarding decisions. Moreover, the strategies, i.e., the discard notification and the backward elimination, are exploited to drop the redundant replications of data as many as possible. The extensive simulation results show that the proposed scheme outperforms several benchmark algorithms, e.g., prophet, spray & wait, and JDER, in terms of the delivery ratio, the delivery latency, and the expected hop count.