๐ฎ
๐ฎ
The Ethereal
Modelling, Verification, and Comparative Performance Analysis of the B.A.T.M.A.N. Protocol
March 20, 2017 ยท The Ethereal ยท ๐ MARS
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Kaylash Chaudhary, Ansgar Fehnker, Vinay Mehta
arXiv ID
1703.06570
Category
cs.LO: Logic in CS
Cross-listed
cs.NI,
cs.SE
Citations
10
Venue
MARS
Last Checked
2 months ago
Abstract
This paper considers on a network routing protocol known as Better Approach to Mobile Ad hoc Networks (B.A.T.M.A.N.). The protocol serves two aims: first, to discover all bidirectional links, and second, to identify the best-next-hop for every other node in the network. A key element is that each node will flood the network at regular intervals with so-called originator messages. This paper describes in detail a formalisation of the B.A.T.M.A.N. protocol. This exercise revealed several ambiguities and inconsistencies in the RFC. We developed two models. The first implements, if possible, a literal reading of the RFC, while the second model tries to be closer to the underlying concepts. The alternative model is in some places less restrictive, and rebroadcasts more often when it helps route discovery, and will on the other hand drop more messages that might interfere with the process. We verify for a basic untimed model that both interpretations ensure loop-freedom, bidirectional link discovery, and route-discovery. We use simulation of a timed model to compare the performance and found that both models are comparable when it comes to the time and number of messages needed for discovering routes. However, the alternative model identifies a significantly lower number of suboptimal routes, and thus improves on the literal interpretation of the RFC.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Logic in CS
๐ฎ
๐ฎ
The Ethereal
Safe Reinforcement Learning via Shielding
๐ฎ
๐ฎ
The Ethereal
Formal Verification of Piece-Wise Linear Feed-Forward Neural Networks
๐ฎ
๐ฎ
The Ethereal
Heterogeneous substitution systems revisited
๐ฎ
๐ฎ
The Ethereal
Omega-Regular Objectives in Model-Free Reinforcement Learning
๐ฎ
๐ฎ
The Ethereal