TY - GEN
T1 - Modeling WiFi Traffic for White Space Prediction in Wireless Sensor Networks
AU - Dhanapala, Indika Sanjeewa Abeywickrama
AU - Marfievici, Ramona
AU - Palipana, Sameera
AU - Agrawal, Piyush
AU - Pesch, Dirk
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/14
Y1 - 2017/11/14
N2 - Cross Technology Interference (CTI) is a prevalent phenomenon in the 2.4 GHz unlicensed spectrum causing packet losses and increased channel contention. In particular, WiFi interference is a severe problem for low-power wireless networks causing a significant degradation of the overall performance. We propose here a proactive approach based on WiFi interference modeling for accurately predicting transmission opportunities for low-power wireless networks. We leverage statistical analysis of real-world WiFi traces to learn aggregated traffic characteristics in terms of Inter-Arrival Time (IAT) that, once captured into a specific 2nd order Markov Modulated Poisson Process (MMPP(2)) model, enable accurate estimation of interference. We further use a hidden Markov model (HMM) for channeloccupancy prediction. We evaluated the performance of: i) the MMPP(2) traffic model w. r. t. real-world traces and an existing Pareto model for accurately characterizing the WiFi traffic and, ii) compared the HMM based white space prediction to random channel access. We report encouraging results for using interference modeling for white space prediction.
AB - Cross Technology Interference (CTI) is a prevalent phenomenon in the 2.4 GHz unlicensed spectrum causing packet losses and increased channel contention. In particular, WiFi interference is a severe problem for low-power wireless networks causing a significant degradation of the overall performance. We propose here a proactive approach based on WiFi interference modeling for accurately predicting transmission opportunities for low-power wireless networks. We leverage statistical analysis of real-world WiFi traces to learn aggregated traffic characteristics in terms of Inter-Arrival Time (IAT) that, once captured into a specific 2nd order Markov Modulated Poisson Process (MMPP(2)) model, enable accurate estimation of interference. We further use a hidden Markov model (HMM) for channeloccupancy prediction. We evaluated the performance of: i) the MMPP(2) traffic model w. r. t. real-world traces and an existing Pareto model for accurately characterizing the WiFi traffic and, ii) compared the HMM based white space prediction to random channel access. We report encouraging results for using interference modeling for white space prediction.
KW - Hidden Markov Model
KW - Interference Prediction
KW - Markov Modulated Poisson Process
KW - WiFi Traffic Modelling
KW - Wireless Sensor Networks
UR - https://www.scopus.com/pages/publications/85040637954
U2 - 10.1109/LCN.2017.30
DO - 10.1109/LCN.2017.30
M3 - Conference proceeding
AN - SCOPUS:85040637954
T3 - Proceedings - Conference on Local Computer Networks, LCN
SP - 551
EP - 554
BT - Proceedings - 2017 IEEE 42nd Conference on Local Computer Networks, LCN 2017
PB - IEEE Computer Society
T2 - 42nd IEEE Conference on Local Computer Networks, LCN 2017
Y2 - 9 October 2017 through 12 October 2017
ER -