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.
@cite_18 analyse epidemic routing taking into account more factors such as limited buffer space and signalling. Their model is based on differential equations also assuming independent exponentially distributed inter-contact times. A similar technique is used by @cite_17 , and extended to deal with multiple classes of mobility movements by @cite_22 .
@cite_18 @cite_22 @cite_17
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Epidemic routing has been proposed as a robust transmission scheme for sparse mobile ad hoc networks. Under the assumption of no contention, epidemic routing has the minimum end-to-end delay amongst all the routing schemes proposed for such networks. The assumption of no contention was justified by arguing that since the network is sparse, there will be very few simultaneous transmissions. Some recent papers have shown through simulations that this argument is not correct and that contention cannot be ignored while analyzing the performance of routing schemes, even in sparse networks.Incorporating contention in the analysis has always been a hard problem and hence its effect has been studied mostly through simulations only. In this paper, we find analytical expressions for the delay performance of epidemic routing with contention. We include all the three main manifestations of contention, namely (i) the finite bandwidth of the link which limits the number of packets two nodes can exchange, (ii) the scheduling of transmissions between nearby nodes which is needed to avoid excessive interference, and (iii) the interference from transmissions outside the scheduling area. The accuracy of the analysis is verified via simulations. 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. Epidemic routing has been proposed to reduce the data transmission delay in disruption tolerant wireless networks, in which data can be replicated along multiple opportunistic paths as different nodes move within each other's communication range. With the advent of network coding, it is intuitive that data can not only be replicated, but also coded, when the transmission opportunity arises. However, will opportunistic communication with network coding perform any better than simple replications? In this paper, we present a stochastic analytical framework to study the performance of epidemic routing using network coding in opportunistic networks, as compared to the use of replication. We analytically show that network coding is superior when bandwidth and node buffers are limited, reflecting more realistic scenarios. Our analytical study is able to provide further insights towards future designs of efficient data communication protocols using network coding. As an example, we propose a priority based coding protocol, with which the destination can decode a high priority subset of the data much earlier than it can decode any data without the use of priorities. The correctness of our analytical results has also been confirmed by our extensive simulations. Among the realistic ingredients to be considered in the computational modeling of infectious diseases, human mobility represents a crucial challenge both on the theoretical side and in view of the limited availability of empirical data. To study the interplay between short-scale commuting flows and long-range airline traffic in shaping the spatiotemporal pattern of a global epidemic we (i) analyze mobility data from 29 countries around the world and find a gravity model able to provide a global description of commuting patterns up to 300 kms and (ii) integrate in a worldwide-structured metapopulation epidemic model a timescale-separation technique for evaluating the force of infection due to multiscale mobility processes in the disease dynamics. Commuting flows are found, on average, to be one order of magnitude larger than airline flows. However, their introduction into the worldwide model shows that the large-scale pattern of the simulated epidemic exhibits only small variations with respect to the baseline case where only airline traffic is considered. The presence of short-range mobility increases, however, the synchronization of subpopulations in close proximity and affects the epidemic behavior at the periphery of the airline transportation infrastructure. The present approach outlines the possibility for the definition of layered computational approaches where different modeling assumptions and granularities can be used consistently in a unifying multiscale framework.