Picking location clusters
AMet with Pádraig to discuss his ideas about location clusters. The idea is to assign a cell tower to a maximum of three clusters or ‘locations’. We would first need to remove the largest N clusters. The rank of clusters should be defined by the sum of the weight (number of reports of links between towers) of the edges between the node into the cluster, or perhaps the average.

Cell towers are linked to at most their top three communities, ranked on the strength of the ties (weight of edges) to that community.
The first step is to run this community allocation against the whole set, and see what it looks like. As it might be that we can keep the large clusters.
Once we have this done, we can re-rank the locations using a modified version of the orignal algorithm. In the new version, a location has a score, which is the sum of the number of times a cell towers within it has been reported, this means that some cell towers will affect the score of multiple locations. Once we have the new ranked locations, we can then rank each used based on their visiting these locations.
Location Clusters/Communities
After assigning cells to a maximum of three location clusters based on the AVERAGE edge weight and the SUM of weight (i.e. two seperate configurations), it was possible to visualise the assigned clusters.

The top 3 allocated clusters based on MOSES output for MIT-OCT 2004, where the membership is calculated using the average weight of edges in/out of the cluster.
For the initial experiments, the dataset without any communities removed was used as the source for the ranking algorithm. Using only the MIT-OCT dataset as the basis for comparison with other results.

LocationSim results for MIT-OCT using Top 3 Moses Allocated Communities for LBR based on AVERAGE, SUM weight calculation, vs RANDOM ranking allocation.
For comparison, one run with a random allocation of rankings (i.e. ranking was shuffled randomly, but only for one run, not an average) gives a hint about the improvement that ranking gives for LBR. In this case, there no significant improvement, but for more conclusive results, multiple runs of random rankings would need to be tested. It might be interesting to try to find the best possible ranking in order to improve routing, it could be that there is no better ranking that can be achieved than that of LBR. This would explain the poor performance against the Random protocols, who are not limited to a strict hierarchy. To match Random 1.0 and 0.2, LBR may need to be more sophisticated.