Selfish Carrier Monitoring in WIFI Using Distributed Sniffers

TL;DR

This paper introduces a passive monitoring tool using distributed sniffers and machine learning to accurately detect interference and selfish carrier-sense behavior in live WiFi networks, reducing router overhead.

Contribution

It presents a novel passive interference estimation method with machine learning for detecting selfish carrier behavior in WiFi networks.

Findings

More accurate interference estimation than heuristics

Effective detection of selfish carrier-sense behavior

Competitive with active measurement approaches

Abstract

This work proposes a tool to estimate the interference between nodes and links in a live wireless network by passive monitoring of wireless traffic. This approach requires deploying multiple sniffers across the network to capture wireless traffic traces. These traces are then analyzed using a machine learning approach to infer the carrier-sense relationship between network nodes. It also demonstrates an important application of this tool-detection of selfish carrier-sense behavior. This is based on identifying any asymmetry in carrier-sense behavior between node pairs and finding multiple witnesses to raise confidence. Simulation results demonstrate that the proposed approach of estimating interference relations is significantly more accurate than simpler heuristics and quite competitive with active measurements. Minimizing router overhead taken as a main goal.