Link State Routing Based on Compressed Sensing

Abstract

In table routing protocols such as link state routing, every node in the network periodically broadcasts its link state and the state of its neighbors. These routing updates result in the transmission of a large number of packets. Some of these packets contain correlated or even redundant data which could be compressed if there is central management in the network. However, in autonomous networks, each node acts as a router, in which case central coordination is not possible. In this paper, compressed sensing is used to reduce routing traffic overhead. This can be done at nodes which have greater processing capabilities and no power consumption limitations such as backbone nodes in wireless mesh networks. A method is proposed to select a subset of nodes and thus a subset of links to probe their state. The sensed states are encoded to generate a low dimension sampled vector. This compressed link state vector is broadcast to the entire network. Nodes can then reconstruct link states from this vector using side information. Performance results are presented which demonstrate accurate anomaly detection while adapting to topology changes. Further, it is shown that a proper choice of weighting coefficients in the sampling process can improve detection performance.