Scalable Wireless Traffic Capture Through Community Detection and Trace Similarity
Existing measurement techniques for IEEE~802.11-based networks assume that the higher the density of monitors in the target area, the higher the quality of the measure. This assumption is, however, too strict if we consider the cost involved in monitor installation and the necessary time to collect and merge all traces. In this paper, we investigate the balance between number of traces and completeness of collected data. We propose a method based on similarity to rank collected traces according to their contribution to the monitoring system. With this method, we are able to select only a subset of traces and still keep the quality of the measure, while improving system scalability. In addition, based on the same rank, we identify monitors that can be relocated to enlarge the monitored area and increase the overall efficiency of the system. Finally, our experimental results show that the proposed solution leads to a better tradeoff in terms of unique captured frames over the number of merge operations.
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