Performance Modelling of Opportunistic Forwarding with Imprecise Knowledge

Mobility-assisted networking is becoming very popular as a mean of delivering messages in disconnected or very dynamic networks, such as opportunistic networks. Despite the rapid growth in the number of proposals for routing protocols that exploit the mobility of nodes, there is a lack of general theoretical frameworks to be used for studying analytically their performance under different mobility conditions (e.g., exponential or Pareto inter-meeting times). Moreover, one of the main approaches to forwarding (so-called utility-based forwarding) consists in nodes collecting statistics about their behaviour (e.g., their contact patterns), and using this information to guide the forwarding process. Thus, a general theoretical framework should also be able to model the fact that the statistics collected by nodes and used to make forwarding decisions might suffer from estimation errors. In order to fill these gaps, in this paper we propose an analytical framework for the single-copy forwarding process in a mobility-assisted network that has the following characteristics: (i) it provides a closed form solution for a large class of probability distributions representing inter-meeting times, (ii) it is able to model both randomized and utility-based forwarding protocols, and (iii) it accounts for errors in the estimations of the utility values used by utility-based schemes for making forwarding decisions. We show that the framework is quite accurate and that it can be used to identify the most effective forwarding policies depending on the amount of estimation errors in the forwarding statistics