B.A.T.Mobile: Leveraging Mobility Control Knowledge for Efficient Routing in Mobile Robotic Networks.
Efficient routing is one of the key challenges of wireless networking forunmanned autonomous vehicles (UAVs) due to dynamically changing channel andnetwork topology characteristics. Various well known mobile-ad-hoc routingprotocols, such as AODV, OLSR and B.A.T.M.A.N. have been proposed to allow forproactive and reactive routing decisions. In this paper, we present a novelapproach which leverages application layer knowledge derived from mobilitycontrol algorithms guiding the behavior of UAVs to fulfill a dedicated task.Thereby a prediction of future trajectories of the UAVs can be integrated withthe routing protocol to avoid unexpected route breaks and packet loss. Theproposed extension of the B.A.T.M.A.N. routing protocol by a mobilityprediction component - called B.A.T.Mobile - has shown to be very effective torealize this concept. The results of in-depth simulation studies show that theproposed protocol reaches a distinct higher availability compared to theestablished approaches and shows robust behavior even in challenging channelconditions.
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