  Wi-Fi networks are susceptible to aggressive behavior caused by selfish or
malicious devices that reduce their minimum contention window size (CWmin) to
below the standard CWmin. In this paper, we propose a scheme called Minimum
Contention Window Estimation (CWE) to detect aggressive stations with low
CWmin's, where the AP estimates the CWmin value of all stations transmitting
uplink by monitoring their backoff values over a period of time and keeping
track of the idle time each station spends during backoff. To correctly
estimate each backoff value, we present a cross-correlation-based technique
that uses the frequency offset between the AP and each station to identify
stations involved in uplink collisions. The AP constructs empirical
distributions for the monitored backoff values and compares them with a set of
nominal PMF's, created via Markov analysis of the DCF protocol to estimate
CWmin of various stations. After detecting the aggressive stations, the AP can
choose to stop serving those stations. Simulation results show that the
accuracy of our collision detection technique is 96%, 94%, and 88% when there
are 3, 6, and 9 stations in the WLAN, respectively. For the former WLAN
settings, the estimation accuracy of CWE scheme is 100%, 98.81%, and 96.3%,
respectively.
