Wireless Ad Hoc and Sensor Networks

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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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Wireless Ad Hocand SensorNetworksPr
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18. Chaos in Automatic Control, edi
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CRC PressTaylor & Francis Group6000
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Table of Contents1 Background on Ne
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3 Congestion Control in ATM Network
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5.4.2 Distributed Power Controller
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7.3 Adaptive and Distributed Fair S
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9.2.2 Overview.....................
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the number of connections in each l
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y Maheswaran Thiagarajan, and tirel
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2 Wireless Ad Hoc and Sensor Networ
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10 Wireless Ad Hoc and Sensor Netwo
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2BackgroundIn this chapter, we prov
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Background 51u(k)x n (k + 1)x n (k)
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Page 78 and 79: Background 55with tr(.) the matrix
Page 80 and 81: Background 57nGiven a function ft (
Page 82 and 83: Background 592.3.2 Lyapunov Stabili
Page 84 and 85: Background 61as well as Jagannathan
Page 86 and 87: Background 63The subsequent example
Page 88 and 89: Background 65Evaluating this along
Page 90 and 91: Background 67Now, select the feedba
Page 92 and 93: Background 69is SISL if and only if
Page 94 and 95: Background 71L 1 (x)L(x, t)L 0 (x)0
Page 96 and 97: Background 73for some R > 0 such th
Page 98 and 99: Background 75Evaluating the first d
Page 100: Background 77Section 2.4Problem 2.4
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7Distributed Fair Scheduling in Wir
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Distributed Fair Scheduling in Wire
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8Optimized Energy and Delay-Based R
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Optimized Energy and Delay-Based Ro
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9Predictive Congestion Control for
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Predictive Congestion Control for W
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10Adaptive and Probabilistic Power
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Adaptive and Probabilistic Power Co
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Wireless Ad Hoc and Sensor Networks

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