Mind the Gap: Autonomous Detection of Partitioned MANET Systems using Opportunistic Aggregation

Abstract : Mobile Ad-hoc Networks (MANETs) use limited-range wireless communications and are thus exposed to partitions when nodes fail or move out of reach of each other. Detecting partitions in MANETs is unfortunately a nontrivial task due to their inherently decentralized design and limited resources such as power or bandwidth. In this paper, we propose a novel and fully decentralized approach to detect partitions (and other large membership changes) in MANETs that is both accurate and resource efficient. We monitor the current composition of a MANET using the lightweight aggregation of compact membership-encoding filters. Changes in these filters allow us to infer the likelihood of a partition with a quantifiable level of confidence. We first present an analysis of our approach, and show that it can detect close to 100% of partitions under realistic settings, while at the same time being robust to false positives due to churn or dropped packets. We perform a series of simulations that compare against alternative approaches and confirm our theoretical results, including above 90% accurate detection even under a 40% message loss rate.
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Simon Bouget, Yérom-David Bromberg, Hugues Mercier, Etienne Rivière, François Taïani. Mind the Gap: Autonomous Detection of Partitioned MANET Systems using Opportunistic Aggregation. SRDS 2018 - 37th IEEE International Symposium on Reliable Distributed Systems, Oct 2018, Salvador, Brazil. pp.143-152, ⟨10.1109/SRDS.2018.00025⟩. ⟨hal-01900360⟩

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