Forecasting DTN performance under heterogeneous mobility: The case of limited replication

Opportunistic or Delay Tolerant Networks (DTNs) may be used to enable communication in case of failure or lack of infrastructure (disaster, censorship, remote areas) and to complement existing wireless technologies (cellular, WiFi). Wireless peers communicate when in contact, forming an impromptu network, whose connectivity graph is highly dynamic and only partly connected. In this harsh environment, communication algorithms are mostly greedy, choosing the best solution among the locally available ones. Furthermore, they are routinely evaluated through simulations only, as they are hard to model analytically. Even when more insight is sought from models, they usually assume homogeneous node meeting rates, thereby ignoring the attested heterogeneity and non-trivial structure of (human) mobility. We propose DTN-Meteo: a new unified analytical model that maps an important class of DTN optimization problems and the respective (greedy) algorithms into a Markov chain traversal over the relevant solution space. Fully heterogeneous node contact patterns and a range of algorithmic actions jointly (but separably) define transition probabilities. Thus, we provide closed-form solutions for crucial performance metrics under generic settings. While DTN-Meteo has wider applicability, in this paper, we focus on algorithms with explicitly controlled replication. We apply our model to two problems: routing and content placement. We predict the performance of state of the art algorithms (SimBet, BubbleRap) in various real and synthetic mobility scenarios and show that surprising precision can be achieved against simulations, despite the complexity of the problems and diversity of settings. To our best knowledge, this is the first analytical work that can accurately predict performance for utility-based algorithms and heterogeneous node contact rates.