Wireless Ad Hoc and Sensor Networks

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9Predictive Congestion Control for WirelessSensor NetworksPrevious chapters have covered congestion and admission controlschemes for wired networks and power control, scheduling, and routingschemes for wireless ad hoc and sensor networks. As indicated in Chapter1, quality of service (QoS) guarantee includes throughput, end-to-enddelay, and packet drop rate even during congestion and in the presenceof unpredictable wireless channel. This chapter will introduce a congestioncontrol scheme for wireless networks that takes into account energyefficiency and fairness. This scheme will be implemented through feedbackobtained from one hop.Available congestion control schemes, for example, the transport controlprotocol (TCP), when applied to wireless networks, result in a large numberof packet drops, unfair scenarios, and low throughputs with a significantamount of wasted energy due to retransmissions. To fully utilize thehop-by-hop feedback information, a decentralized, predictive congestioncontrol method consisting of an adaptive flow and adaptive backoff intervalselection scheme is introduced for wireless sensor networks (WSN)(Zawodniok and Jagannathan 2006) in concert with the distributed powercontrol (DPC) (Zawodniok and Jagannathan 2004). Besides providing anenergy-efficient solution, the embedded channel estimator algorithm inDPC predicts the channel quality in the subsequent time interval. By usingthis information together with queue utilization, the onset of networkcongestion is assessed and a suitable transmission rate is determined byan adaptive flow control scheme. Then, an adaptive backoff interval selectionscheme enables a node to transmit packets at the rate determined bythe flow control scheme through one-hop feedback, thus preventing congestion.Additionally, because of the recursive application of the proposedcongestion control at each node and through piggyback acknowledgments,the onset of congestion is propagated backward toward the sourcenodes, so that they too reduce their transmission rates. An optional adaptivescheduling scheme from Chapter 7 at each node updates the packetweights to guarantee the weighted fairness during congestion. DPC wasdiscussed in Chapter 6.429
430 Wireless Ad Hoc and Sensor NetworksClosed-loop stability of the proposed hop-by-hop congestion control isdemonstrated by using the Lyapunov-based approach. Simulation resultsshow that this scheme results in fewer dropped packets, better fairnessindex, higher network efficiency and aggregate throughput, and smallerend-to-end delays over the other available schemes such as CongestionDetection and Avoidance (CODA) (Wan et al., 2003) and IEEE 802.11protocols.9.1 IntroductionNetwork congestion, which is quite common in wireless networks, occurswhen the offered load exceeds available capacity or the link bandwidthis reduced because of fading channels. Network congestion causes channelquality to degrade and loss rates to rise. It leads to packet drops atthe buffers, increased delays, and wasted energy, and requires retransmissions.Moreover, traffic flow will be unfair for nodes whose data have totraverse a significant number of hops. This considerably reduces the performanceand lifetime of the network. Additionally, WSN have constraintsimposed on energy, memory, and bandwidth. Therefore, energy-efficientdata transmission protocols are required to mitigate congestion resultingfrom fading channels and excess load. In particular, a congestion controlmechanism is needed to balance the load, to prevent packet drops, andto avoid network deadlock.Rigorous work has been done in wired networks on end-to-end congestioncontrol (Jagannathan 2002, Peng et al. 2006). In spite of severaladvantages in end-to-end control schemes, the need to propagate the onsetof congestion between end-systems makes the approach slow. In general,a hop-by-hop congestion control scheme reacts to congestion faster andis normally preferred to minimize packet losses in wireless networks.Therefore, the scheme from Zawodniok and Jagannathan (2005) uses anovel hop-by-hop flow control algorithm that is capable of predicting theonset of congestion and then gradually reducing the incoming traffic bymeans of a backpressure signal.In comparison, the CODA protocol proposed by Wan et al. (2003) usesboth a hop-by-hop and an end-to-end congestion control scheme to reactto an already present congestion by simply dropping packets at the nodepreceding the congestion area. Thus, CODA partially minimizes theeffects of congestion, and as a result retransmissions still occur. Similar toCODA, Fusion (Hull et al., 2004) uses a static threshold value for detectingthe onset of congestion even though it is normally difficult to determinea suitable threshold value that works in dynamic channel environments.
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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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100 Wireless Ad Hoc and Sensor Netw
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4Admission Controller Design for Hi
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Admission Controller Design for Hig
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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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Page 402 and 403: Optimized Energy and Delay-Based Ro
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