B.A.T.Mobile: Leveraging Mobility Control Knowledge for Efficient Routing in Mobile Robotic Networks

Abstract

Efficient routing is one of the key challenges of wireless networking for unmanned autonomous vehicles (UAVs) due to dynamically changing channel and network topology characteristics. Various well known mobile-ad-hoc routing protocols, such as AODV, OLSR and B.A.T.M.A.N. have been proposed to allow for proactive and reactive routing decisions. In this paper, we present a novel approach which leverages application layer knowledge derived from mobility control algorithms guiding the behavior of UAVs to fulfill a dedicated task. Thereby a prediction of future trajectories of the UAVs can be integrated with the routing protocol to avoid unexpected route breaks and packet loss. The proposed extension of the B.A.T.M.A.N. routing protocol by a mobility prediction component - called B.A.T.Mobile - has shown to be very effective to realize this concept. The results of in-depth simulation studies show that the proposed protocol reaches a distinct higher availability compared to the established approaches and shows robust behavior even in challenging channel conditions.

References in corpus (1)

• An OMNeT++ based Framework for Mobility-aware Routing in Mobile Robotic Networks

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• Empirical Analysis of Client-based Network Quality Prediction in Vehicular Multi-MNO Networks
• System-of-Systems Modeling, Analysis and Optimization of Hybrid Vehicular Traffic
• Lightweight Simulation of Hybrid Aerial- and Ground-based Vehicular Communication Networks
• Lightweight Joint Simulation of Vehicular Mobility and Communication with LIMoSim
• Towards Data-driven Simulation of End-to-end Network Performance Indicators
• A Simple Scheme for Distributed Passive Load Balancing in Mobile Ad-hoc Networks