TY - GEN
T1 - CWminEstimation and Collision Identification in Wi-Fi Systems
AU - Yazdani-Abyaneh, Amir Hossein
AU - Krunz, Marwan
N1 - Funding Information:
VII. ACKNOWLEDGMENTS This research was supported in part by NSF (grants CNS-1563655, CNS-1731164, and IIP-1822071), and in part by the U.S. Army Small Business Innovation Research Program Office and Army Research Office under Contract No. W911NF-21-C-0016. Any opinions, findings, conclusions, or recommendations expressed in this paper are those of the author(s) and do not necessarily reflect the views of NSF or Army.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
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U2 - 10.1109/MASS52906.2021.00067
DO - 10.1109/MASS52906.2021.00067
M3 - Conference contribution
AN - SCOPUS:85123928430
T3 - Proceedings - 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2021
SP - 490
EP - 498
BT - Proceedings - 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2021
Y2 - 4 October 2021 through 7 October 2021
ER -