GSoC 2019 – Log Monitoring on LibreMesh

Log analysis is the way to find and recover the problems (known or not) of hardware, software or “rare” traffic on the network. In addition to the technical problem involved in unifying the logs of various routers and analyzing them, in community networks we frequently encounter the following additional problems:

  • Temporary disconnections. Due to geographical and / or atmospheric conditions, some equipment is temporarily disconnected from the network, so we have to design a system that allows us to “transfer” those logs.
  • Several of the routers used in community networks have limited resources to store the logs.
  • Several community networks do not have a sys admin within the network or they may not have Internet access (total or only temporary) to receive external help.

The idea of this GSoC is to develop a system that allows us to unify the logs of the routers of the network, filter them to stay only with the relevant ones to analyze and generate automatic analyzes (for example analysis of correlation of logs) to find possible problems and report them to the community.
Also, we want to develop a general dashboard of the state of the community network.

About me

I’m Franco Bellomo, a student of Exact Sciences at the University of Buenos Aires. My area of study is mathematical analysis and computational modeling. My previous free soft projects are related to academic problems so I am very happy with this new challenge.

I am an activist for free knowledge and I am very motivated to contribute to community networks.


  • Normalize and decrease the size of the logs. For this I want to perform a test between developing and training an own model (starting from a Huffman tree) in comparison with using (liblognorm) [].
  • Centralize Within the network there will be an RPi which will help us to join the standardized logs. In this step you will not consider the teams that are temporarily out.
  • General Dashboard. Visualization of the topology of the network and the status of each team.
  • Analysis of traffic and outlier in the network.
  • The records within the log are labeled (debug, info, warning, etc). We want to generate a system of auto tageo of groups of registries, that is to say that combinations of logs are potentially dangerous. For this we are going to use algotirmos of classification.
  • Extraction of features of the logs to make an unsupervised model of anomalies detection.
  • Obtain the log of the connected routers. For this we are going to use the community telephones as bridges.
  • Generate a good documentation!

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