A fault-tolerant routing protocol for dynamic autonomous unmanned vehicular networks

Due to various operational constraints on the unmanned autonomous vehicle (AUxV) networks operating in an adversarial environment, a fault-tolerant routing scheme is an imperative need. Looking at the risk involved in their applications such as search and rescue, threat surveillance, chemical and biohazard sampling, even a fault of minor nature in the system software/hardware may result in destructive consequences. The AUxV network member nodes vary in architecture, capability, application and power of their internal systems. In such a case it is important that the fault-tolerant scheme should take into consideration the kind of heterogeneity involved and should be able to perform in such a scenario as well. Therefore, to address these issues, in this paper we propose a cross-layer and learning automata (LA) based fault-tolerant routing algorithm for AUxVs, named as ULARC (Unmanned Vehicle Network with LA based Routing using Cross Layer Design). We use the theory of LA for the selection of optimal path for routing between source and destination. In this paper, we also focus on making our proposed strategy an energy-efficient one by using a cross-layer architecture for sleep scheduling of nodes. Further, we have devised an α-based scheduling scheme which further adds to the energy efficiency of our protocol by reducing the overhead on the network.