Wireless Ad Hoc and Sensor Networks
Wireless Ad Hoc and Sensor Networks Wireless Ad Hoc and Sensor Networks
Congestion Control in ATM Networks and the Internet 103Cell loss ratio0.0450.040.0350.030.0250.020.015Buffer length = 250ThresholdAdaptiveOne layerTwo layer w/o trainingTwo layer with training0.010.005020 40 60 80 100 120 140 160 180Time in secFIGURE 3.13Cell loss ratio with congestion.0.0120.01Buffer length = 250AdaptiveOne layerTwo layer w/o trainingTwo layer with training0.008Cell loss ratio0.0060.0040.002020 40 60 80 100Time in sec120 140 160 180FIGURE 3.14Cell loss ratio with congestion.
104 Wireless Ad Hoc and Sensor NetworksMoreover, it is observed that the two-layer NN method performed betterin providing a low CLR during congestion, compared to the adaptiveARMAX, threshold, and one-layer methods. However, as expected, themultilayer NN method takes longer to transfer cells from the source tothe destination compared to the open-loop scenario. In addition, providingoffline training did indeed reduce the cell-transfer delay, whereas theCLR is still near zero. From the figures, it is clear that the CLR resultingfrom the two-layer NN method outperforms the adaptive, thresholding,and one-layer NN methods. Finally, the overall delay observed for twolayerNN was within 1% of the total transmission time of 167 sec, whereasother schemes took longer than 3%.Figure 3.15 and Figure 3.16 present the buffer utilization using adaptiveARMAX, one-layer NN, multilayer NN with and without a priori training.The buffer utilization is very low for the thresholding method because ofa threshold value of 40%, whereas the one-layer NN stores cells in thebuffer frequently resulting in a very high utilization. The multilayer NNwithout a priori training does not utilize the buffer as much as the multilayerNN with training. This is due to the inaccurate knowledge of thetraffic flow with no a priori training. Also, as more buffer space is providedto the multilayer NN with no a priori training, the utilization increaseswith buffer size. However, as pointed out earlier, the overall delay issmaller than other schemes even though the queuing delay is slightlyhigher.100Feedback delay = 0Buffer utilization9080706050ThresholdAdaptiveOne layerTwo layer w/o trainingTwo layer with training4030150 200 250 300 350Buffer length (cells)FIGURE 3.15Cell loss ratio with congestion.
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Congestion Control in ATM <strong>Networks</strong> <strong>and</strong> the Internet 103Cell loss ratio0.0450.040.0350.030.0250.020.015Buffer length = 250Threshold<strong>Ad</strong>aptiveOne layerTwo layer w/o trainingTwo layer with training0.010.005020 40 60 80 100 120 140 160 180Time in secFIGURE 3.13Cell loss ratio with congestion.0.0120.01Buffer length = 250<strong>Ad</strong>aptiveOne layerTwo layer w/o trainingTwo layer with training0.008Cell loss ratio0.0060.0040.002020 40 60 80 100Time in sec120 140 160 180FIGURE 3.14Cell loss ratio with congestion.
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