Wireless Ad Hoc and Sensor Networks

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Congestion Control in ATM Networks and the Internet 137Throughput (Mbps) Throughput (Mbps) Throughput (Mbps)DATA 110TCPTQ50 0 10 20 30 40DATA 254TCPTQ3210 0 10 20 30 40DATA 354TCPTQ3210 0 10 20Time:(s)30 40Throughput (Mbps) Throughput (Mbps) Throughput (Mbps)CBR 110TCPTQ50 0 10 20 30 4010CBR 2TCPTQ50 0 10 20 30 40CBR 354TCPTQ3210 0 10 20Time:(s)30 40FIGURE 3.32Individual throughput with time for fairness policy.guaranteeing the QoS for the inelastic users. The rates are adjusted fromthe theoretical results presented in the previous sections, which guaranteeperformance. Consequently, the TQ scheme displays superior performancein comparison with the TCP. These results are significant as theInternet congestion control is not fair, and therefore a best-effort serviceresults. On the contrary, the proposed end-to-end congestion schemedeveloped using rigorous mathematics guarantees the performance of thescheme, both theoretically and via extensive simulation studies.TABLE 3.7Fairness ComparisonCase IV TQ New-Reno TCPPacket loss ratio 0 1.172%Transmission delay 0.0617 0.0650System power 7.475 7.0917
138 Wireless Ad Hoc and Sensor NetworksTABLE 3.8Transmission TimeTrafficTQ (sec)New-RenoTCP (sec)Data1 31.58 30.05Data2 31.85 30.05VBR 30.07 30.05CBR1 30.04 31.95CBR2 30.05 30.04CBR3 30.04 30.043.6.5 Discussion of ResultsIn terms of PLR, transmission delay, and system power, TQ scheme outperformsthe New-Reno TCP scheme when congestion occurred, in allour test cases. TQ scheme has less PLR, less transmission delay, and highersystem power over New-Reno TCP. The specific performance improvementis shown in Table 3.9.3.7 Summary and ConclusionsThis chapter detailed a multilayer NN-based adaptive traffic-rate controllerfor ATM networks. The ATM switch/buffer dynamics along with thetraffic flow is modeled as a nonlinear dynamical system and an NNcontroller is designed to prevent congestion. This learning-basedapproach does not require accurate information about the network systemdynamics or traffic rate, it estimates the traffic rate at the switch and usesthis estimate to arrive at the controller. No initial offline learning phaseis required for the NN. By providing the NN with offline training usingthe backpropagation algorithm, the QoS improved slightly. However, inTABLE 3.9Performance Improvement for TQ over New-Reno TCPCase PLR (%)TransmissionDelay (%)SystemPower (%)Single source 11 2 2Multiple source 80 32 45Extended topology 100 1 0.4Fairness test 100 0.8 0.4
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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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Background 53with x( 0)being the in
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Background 55with tr(.) the matrix
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Background 57nGiven a function ft (
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Background 592.3.2 Lyapunov Stabili
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Background 61as well as Jagannathan
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Background 63The subsequent example
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Background 65Evaluating this along
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Background 67Now, select the feedba
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Background 69is SISL if and only if
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Background 71L 1 (x)L(x, t)L 0 (x)0
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Background 73for some R > 0 such th
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Background 75Evaluating the first d
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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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