EVT-based statistical characterization of aggregated Inter-Contact Time in opportunistic networks

The most common approach adopted in the literatures to study the aggregated ICT (Inter-Contact Time) is focused on a certain mobile wireless network, and then characterizing the ICT in this type of network. Tip to now, it still lacks a general approach to model the aggregated ICT effectively in different type of networks. This paper proposes a new approach to characterize the aggregated ICT based on EVT (extreme value theory), which uses the GPD (generalized Pareto distribution) as the unique asymptotic model of the tail distribution. Parameter estimation methods are discussed and applied to real mobility traces collected on different opportunistic networks, such as social pocket switched networks and VANET (vehicular ad hoc network). By performing extensive experiments using EVT-based model, the statistical characteristics of aggregated ICT are found to generally have a power-law behavior in the tail distribution in social opportunistic networks, while an exponential behavior in VANETs. According to the KS (Kolmogorov-Smirnov) test, it illustrates that EVT-based model performs better than other models in characterizing the tail behavior of real mobility traces. Our results thus provide fundamental guidelines on design a new mobility models in opportunistic networks, new routing protocols and their performance analysis.