When nodes in a mobile network cluster together or move according to common external factors (e.g., cars that follow the road network), the resulting contact patterns become correlated. In this work we address the question of modelling such correlated mobility movements for the analysis of intermittently connected networks. We propose to use the concept of node colouring time to characterise dynamic node contact patterns. We analyse how this model compares to existing work, and demonstrate how to extract the relevant data from actual trace files. Moreover, we show how this information can be used to derive the latency distribution of DTN routing protocols. Our model achieves a very good fit to simulated results based on real vehicular mobility traces, whereas models which assumes independent contacts do not.
In this paper, we develop a rigorous, unified framework based on ordinary differential equations (ODEs) to study epidemic routing and its variations. These ODEs can be derived as limits of Markovian models under a natural scaling as the number of nodes increases. While an analytical study of Markovian models is quite complex and numerical solution impractical for large networks, the corresponding ODE models yield closed-form expressions for several performance metrics of interest, and a numerical solution complexity that does not increase with the number of nodes. Using this ODE approach, we investigate how resources such as buffer space and the number of copies made for a packet can be traded for faster delivery, illustrating the differences among various forwarding and recovery schemes considered. We perform model validations through simulation studies. Finally we consider the effect of buffer management by complementing the forwarding models with Markovian and fluid buffer models. Communication networks are traditionally assumed to be connected. However, emerging wireless applications such as vehicular networks, pocket-switched networks, etc., coupled with volatile links, node mobility, and power outages, will require the network to operate despite frequent disconnections. To this end, opportunistic routing techniques have been proposed, where a node may store-and-carry a message for some time, until a new forwarding opportunity arises. Although a number of such algorithms exist, most focus on relatively homogeneous settings of nodes. However, in many envisioned applications, participating nodes might include handhelds, vehicles, sensors, etc. These various "classesrdquo have diverse characteristics and mobility patterns, and will contribute quite differently to the routing process. In this paper, we address the problem of routing in intermittently connected wireless networks comprising multiple classes of nodes. We show that proposed solutions, which perform well in homogeneous scenarios, are not as competent in this setting. To this end, we propose a class of routing schemes that can identify the nodes of "highest utilityrdquo for routing, improving the delay and delivery ratio by four to five times. Additionally, we propose an analytical framework based on fluid models that can be used to analyze the performance of various opportunistic routing strategies, in heterogeneous settings. We study fluid approximations for a class of monotone relay policies in delay tolerant ad-hoc networks. This class includes the epidemic routing and the two-hops routing protocols. We enhance relay policies with probabilistic forwarding, i.e., a message is forwarded to a relay with some probability p. We formulate an optimal control problem where a tradeoff between delay and energy consumption is captured and optimized. We compute both the optimal static value of p as well as the optimal time dependent value of p. We show that the time-dependent problem is optimized by threshold type policies, and we compute explicitly the value of the optimal threshold for some special classes of relay policies.