RoRo-LT: Social Routing with Next-Place Prediction from Self-Assessment of Spatiotemporal Routines

Current trend in store-carry-forward fashioned opportunistic networks is towards utilizing social ties in communities. However, keeping social knowledge/network information up-to-date is a non-trivial task due to ever-changing dynamics such as mobility and other human behavior. Therefore, social-based message forwarding proposals in which network information are initially provided to mobile nodes are subject to lose actuality within time. Motivated with this shortcoming, this study presents a social unicast routing scheme, called RoRo-LT, which is based on self-assessment of people's daily routines. Without requiring any network information, RoRo-LT provides an up-to-date social awareness with long-term spatiotemporal observations. In RoRo-LT, nodes in contact estimate their own future trajectories and decide on forwarding according to their social similarities. In comparison to well-known forwarding schemes, RoRo-LT's performance results indicate a high socio-spatial awareness as well as a reasonable effectiveness for opportunistic routing.