0000000000825597
AUTHOR
Xiaolan Zhang
Performance modeling of epidemic routing
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 tra…
On the Benefits of Random Linear Coding for Unicast Applications in Disruption Tolerant Networks
In this paper, we investigate the benefits of using a form of network coding known as Random Linear Coding (RLC) for unicast communications in a mobile Disruption Tolerant Network (DTN) under epidemic routing. Under RLC, DTN nodes store and then forward random linear combinations of packets as they encounter other DTN nodes. We first consider the case where there is a single block of packets propagating in the network and then consider the case where blocks of K packets arrive according to a Poisson arrival process. Our performance metric of interest is the delay until the last packet in a block is delivered. We show that for the single block case, when bandwidth is constrained, applying RL…
Optimal Delay-Power Tradeoff in Sparse Delay Tolerant Networks: a preliminary study
In this paper we present a first attempt to study analytically the tradeoff between delivery delay and resource consumption for epidemic routing in Delay Tolerant Networks. We assume that the nodes cooperate in order to minimize a common cost equal to a weighted sum of the packet delivery delay and the total number of copies, which is strongly related to the power consumption. In this framework we determine the best policy each node should deploy in a very simple scenario where all the nodes have perfect knowledge of the system status. The result is used as an ideal reference to evaluate the performance of some heuristics proposed, investigating potential performance improvements and config…