BEEINFO: Interest-Based Forwarding Using Artificial Bee Colony for Socially Aware Networking

Abstract

Socially aware networking (SAN) provides a promising paradigm for routing and forwarding data packets by exploiting social properties of involved entities, for example, in vehicular social networks (VSNs). The mobility of individuals often features some regularity in location and time, particularly in vehicular environments. However, individuals' learning capability and awareness to the dynamic environments have not been well explored in the literature. Inspired by the artificial bee colony, we present BEEINFO, which is a set of interest-based forwarding schemes for SAN, which consists of BEEINFO-D, BEEINFO-S, and BEEINFO-D&S. BEEINFO adopts the food foraging behavior of bees to detect the environment information and to optimize the forwarding procedure. BEEINFO takes advantage of individuals' perceiving and learning capability to gather information of density and social ties. BEEINFO-D, BEEINFO-S, and BEEINFO-D&S are distinct from each other according to different utilization of density and social ties. This enhances the adaptability to dynamic environments. Additionally, BEEINFO performs message scheduling and buffer management to improve the forwarding performance. Extensive simulations have been conducted to compare BEEINFO with two representative protocols, i.e., PRoPHET and Epidemic. The results illustrate that BEEINFO outperforms PRoPHET and Epidemic with higher message delivery ratio, less overhead, and fewer hop counts.