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.
Closest to our work in this paper is that of Resta and Santi @cite_10 , where the authors present an analytical framework for predicting routing performance in delay-tolerant networks. The authors analyse epidemic and two-hops routing using a colouring process under similar assumptions as in our paper. The main difference is that our work considers heterogeneous node mobility (including correlated inter-contact times), whereas the work by Resta and Santi assumes independent exponential inter-contact times.
@cite_10
2101946268 2031857436 2145594425 1991690766
In this paper, we present a framework for analyzing routing performance in delay tolerant networks (DTNs). Differently from previous work, our framework is aimed at characterizing the exact distribution of relevant performance metrics, which is a substantial improvement over existing studies characterizing either the expected value of the metric, or an asymptotic approximation of the actual distribution. In particular, the considered performance metrics are packet delivery delay, and communication cost, expressed as number of copies of a packet circulating in the network at the time of delivery. Our proposed framework is based on a characterization of the routing process as a stochastic coloring process and can be applied to model performance of most stateless delay tolerant routing protocols, such as epidemic, two-hops, and spray and wait. After introducing the framework, we present examples of its application to derive the packet delivery delay and communication cost distribution of two such protocols, namely epidemic and two-hops routing. Characterizing packet delivery delay and communication cost distribution is important to investigate fundamental properties of delay tolerant networks. As an example, we show how packet delivery delay distribution can be used to estimate how epidemic routing performance changes in presence of different degrees of node cooperation within the network. More specifically, we consider fully cooperative, noncooperative, and probabilistic cooperative scenarios, and derive nearly exact expressions of the packet delivery rate (PDR) under these scenarios based on our proposed framework. The comparison of the obtained packet delivery rate estimation in the various cooperation scenarios suggests that even a modest level of node cooperation (probabilistic cooperation with a low probability of cooperation) is sufficient to achieve 2-fold performance improvement with respect to the most pessimistic scenario in which all potential forwarders drop packets. In this paper, we introduce a distributed dynamic routing algorithm in multi-hop cognitive radio (CR) networks, in which secondary users (SUs) want to minimize their interference to the primary users (PUs) while keeping the delay along the route low. We employ a cognitive pilot channel (CPC) for SUs to be able to access the information about PUs, including PUs' locations and channel conditions. Medial axis with a relaxation factor is used as a reference path for the routing, along which we develop a hierarchical structure for multiple sources to reach their destinations. We introduce a temporal and spatial dynamic non-cooperative game to model the interactions among the SUs as well as their influences on the PUs, and obtain by backward induction a set of mixed (behavioral) Nash equilibrium strategies. We also employ a multi-stage fictitious play learning algorithm for distributed routing, which minimizes the overall interference from the SUs to the PUs, as well as the average packet delay along the route from the SU nodes to their destinations. Simulation results show that our proposed algorithm can avoid congestion in the CR network and minimize delay while keeping the interference level low. Considered is a mobile ad hoc network consisting of three types of nodes (source, destination and relay nodes) and using the two-hop relay routing protocol. Packets at relay nodes are assumed to have a limited lifetime in the network. All nodes are moving inside a bounded region according to some random mobility model. Both closed-form expressions, and asymptotic results when the number of nodes is large, are provided for the packet delivery delay and the energy needed to transmit a packet from the source to its destination. We also introduce and evaluate a variant of the two-hop relay protocol that limits the number of generated copies in the network. Our model is validated through simulations for two mobility models (random waypoint and random direction mobility models), numerical results for the two-hop relay protocols are reported, and the performance of the two-hop routing and of the epidemic routing protocols are compared. In this paper we focus on how the heterogeneous contact dynamics of mobile nodes impact the performance of forwarding routing algorithms in delay disruption-tolerant networks (DTNs). To this end, we consider two representative heterogeneous network models, each of which captures heterogeneity among node pairs (individual) and heterogeneity in underlying environment (spatial), respectively, and examine the full extent of difference in delay performances they cause on forwarding routing algorithms through formal stochastic comparisons. We first show that these heterogeneous models correctly capture non-Poisson contact dynamics observed in real traces. Then, we consider direct forwarding and multicopy two-hop relay protocol and rigorously establish stochastic convex ordering relationships on their delay performances under these heterogeneous models and the corresponding homogeneous model, all of which have the same average inter-contact time over all node pairs. We show that heterogeneous models predict an entirely opposite ordering relationship in the delay performances depending on which of the two heterogeneities is captured. This suggests that merely capturing non-Poisson contact dynamics - even if the entire distribution of aggregated inter-contact time is precisely matched, is not enough and that one should carefully evaluate the performance of forwarding routing algorithms under a properly chosen heterogeneous network setting. Our results will also be useful in correctly exploiting the underlying heterogeneity structure so as to achieve better performance in DTNs.