Performance modelling of opportunistic forwarding under heterogenous mobility

Abstract The delay tolerant networking paradigm aims to enable communications in disconnected environments where traditional protocols would fail. Opportunistic networks are delay tolerant networks whose nodes are typically the users’ personal mobile devices. Communications in an opportunistic network rely on the mobility of users: each message is forwarded from node to node, according to a hop-by-hop decision process that selects the node that is better suited for bringing the message closer to its destination. Despite the variety of forwarding protocols that have been proposed in the recent years, there is no reference framework for the performance modelling of opportunistic forwarding. In this paper we start to fill this gap by proposing an analytical model for the first two moments of the delay and the number of hops experienced by messages when delivered in an opportunistic fashion. This model seamlessly integrates both social-aware and social-oblivious single-copy forwarding protocols, as well as different hypotheses for user contact dynamics. More specifically, the parameters of model can be solved in a closed form in the case of exponential and Pareto inter-meeting times, two popular cases emerged from the literature on human mobility analysis. In order to exemplify how the proposed framework can be used, we discuss its application to two case studies with different mobility settings. Then, we discuss how the framework can be also extended to accommodate inter-meeting times following a hyper-exponential distribution. This case is particularly relevant as hyper-exponential distributions are able to approximate the large class of high-variance distributions (distributions with coefficient of variation greater than one), which are those more challenging, e.g., from the delay standpoint. Finally, we provide a validation for the framework with both ideal contacts (i.e., exactly following a given distribution) and contacts extracted from a real mobility trace. This evaluation highlights the strength of the framework in terms of its ability both to provide very accurate predictions under ideal mobility and to effectively approximate the behaviour of the delay moments under real mobility.