Reliable disjoint path set selection in mobile Ad-hoc Networks using Noisy Hopfield Neural Network

Mobile Ad-hoc NETwork (MANET) is a collection of wireless mobile nodes without any fixed base-station infrastructure and centralized management. Topological changes in MANETs render routing paths unusable. The multipath routing addresses this problem by providing more than one route to a destination node. 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 Noisy Hopfield Neural Network (NHNN) is proposed as a path set selection algorithm in this paper. This algorithm can find either node-disjoint or link-disjoint path set with no extra overhead. This approach is beneficial for mobile ad-hoc networks, since it produces a set of backup paths with high reliability.