Scalable opportunistic forwarding algorithms in delay tolerant networks using similarity hashing

Due to intermittent connectivity and uncertain node mobility, opportunistic message forwarding algorithms have been widely adopted in delay tolerant networks (DTNs). While existing work proposes practical forwarding algorithms in terms of increasing the delivery rate and decreasing data overhead, little attention has been drawn to the control overhead induced by the algorithms. The control overhead could, however, make the forwarding algorithms infeasible when the network size scales. In this paper, we are interested in increasing scalability by reducing control overhead, while retaining the state-of-the-art forwarding performances. The basic idea is to use locality-sensitive hashing to map each node as a hash-code, and use these hash-codes to compute the pair-wise similarity that guides the forwarding decisions. The proposed SOFA algorithms have reduced control overhead and competitive forwarding performance, which are verified by extensive real trace-driven simulations.