RoCNet: Spatial mobile data offload with user-behavior prediction through delay tolerant networks

Abstract

We present a robust cellular network (RoCNet) that combines a cellular and an opportunistic networks for spatial uplink mobile data offloading, which focuses on the spatial difference of the traffic load among areas (e.g., business district and residential area in the daytime). RoCNet realizes the spatial data offload by leveraging the store-carry-forward routing mechanism. In the area where traffic load is high, delay-tolerant data originated from a mobile terminal is directly forwarded to a nearby terminal using Bluetooth or wireless LAN instead of being transmitted to a congested cellular base station. When the data is carried by the nearby terminal to other area where the traffic load is low, the data is forwarded to a cellular base station. To enhance the offload effect, it is necessary for data to be forwarded to a terminal that moves to a low traffic load area. In this paper, we use the particle filter to predict user behavior. Before forwarding data between mobile terminals, the terminals exchange prediction results and decide whether or not the data should be forwarded. We conducted a computer simulation whose result shows RoCNet can spatially offload uplink traffic in a traffic concentration area to non-congested areas. As a result, RoCNet can suppress peak traffic by about 20 percent in a traffic-congested base station by distributing traffic to vicinity base stations.