Hopfield neural network for disjoint path set selection in Mobile Ad-hoc Networks

Topological changes in Mobile Ad-hoc NETwork (MANET) render routing paths unusable. Using multiple redundant paths between the source and the destination is a technique which reduces the affect of this problem. Shared links and nodes between paths present common failure points which can disable many or all of the paths. Disjoint path set requires the multiple paths to be link- or node-disjoint. However, selecting an optimal path set is an NP-complete problem. Neural networks have been proposed as computational tools for solving constrained optimization problems. A Hopfield neural network is proposed as a path set selection algorithm in this paper. This algorithm is beneficial for mobile ad-hoc networks, since it produces a set of backup paths with high reliability. This approach can find either node-disjoint or link-disjoint path set with no extra overhead.