Scalable Wireless Traffic Capture Through Community Detection and Trace Similarity

Abstract : Best-performing WLAN monitoring systems must capture as much wireless traffic as possible. To achieve this aim, several monitors are employed to capture wireless exchanges in a target area. Monitors potentially generate large traces that are all merged together to have a more complete, global view of the network behavior. Traces are often more equal than complementary, leading to the underutilization of monitors and to a higher system complexity. In this paper, we propose a methodology to make an efficient use of monitors in order to increase scalability. Such a methodology, based on trace similarity and community detection in graphs, ranks traces to reveal how many and which ones must be merged. Traces at the bottom of the rank, which belong to under-used monitors, are candidates to be relocated somewhere else to extend the target area. We evaluate the proposed methodology in two real-case scenarios. Results show that we can remove up to half of the monitors in our scenarios and still keep the same level of coverage.
Complete list of metadatas

https://hal.sorbonne-universite.fr/hal-01203316
Contributor : Matteo Sammarco <>
Submitted on : Tuesday, September 22, 2015 - 5:04:34 PM
Last modification on : Wednesday, May 15, 2019 - 3:43:55 AM

Identifiers

Citation

Matteo Sammarco, Marcelo Dias de Amorim, Miguel Elias Campista. Scalable Wireless Traffic Capture Through Community Detection and Trace Similarity. IEEE Transactions on Mobile Computing, Institute of Electrical and Electronics Engineers, 2015, 15 (7), pp.1757 - 1769. ⟨10.1109/TMC.2015.2477809⟩. ⟨hal-01203316⟩

Share

Metrics

Record views

261