Wireless Ad Hoc and Sensor Networks

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Predictive Congestion Control for Wireless Sensor Networks 431In both CODA and Fusion protocols, nodes use a broadcast message toinform their neighboring nodes of the onset of congestion. Although thisis quite interesting, the onset of congestion message occurring inside thenetwork is not guaranteed to reach the sources. Moreover, available protocols(Hull et al. 2004, Wan et al. 2003, Yi and Shakkotai 2004) do notpredict the onset of congestion in dynamic environments, for example,due to fading channel conditions. Finally, very few analytical results arepresented in the literature in terms of guaranteeing the performance ofavailable congestion control protocols. In contrast to these protocols, themethod of Zawodniok and Jagannathan (2005) can predict and mitigatethe onset of congestion by gradually reducing the traffic flow defined byusing the queue availability and channel state.Besides predicting the onset of congestion, this scheme guarantees convergenceto the calculated target outgoing rate by using a novel, adaptivebackoff interval selection algorithm. In CSMA/CA-based wireless networks,a backoff selection mechanism is used to provide simultaneousaccess to a common transmission medium and to vary transmission rates.Many researchers (Vaidya et al. 2000, Wang et al. 2005, Kuo and Kuo 2003)have focused on the performance analysis of backoff selection schemesfor static environments. However, these schemes lack the ability to adaptto a changing channel state, congestion level, and size of the network. Forexample, in (Vaidya et al. 2000) the backoff intervals for the nodes areselected proportional to a flow weight, thus providing weighted fairness.However, the solution assumes a uniform density of transmitting nodesbecause the achieved throughput is determined by both a number ofcompeting nodes and their backoff intervals. Consequently, distributedfair scheduling (DFS) will yield an unfair throughput for nodes withdifferent number of neighbors. In contrast, the proposed algorithmdynamically alters backoff intervals according to current network conditions,for instance, the varying number of neighbor nodes and fadingchannels.Additionally, the protocol (Zawodniok and Jagannathan 2004) usesweights associated with flows to fairly allocate resources during congestion.By adding an optional dynamic weight adaptation algorithm,weighted fairness can be guaranteed in dynamic environments as shownin the proposed work. Finally, using a Lyapunov-based approach, thestability and convergence of the three algorithms, for buffer control, backoffinterval selection and dynamic weight adaptation, are proved.This chapter is organized as follows. Section 9.2 presents an overviewof the proposed methodology of predicting the onset of congestion, mitigation,DPC and weighted fairness. In Section 9.3, the flow control andbackoff interval selection schemes, their performance and weighted fairnessguarantees are presented. Mathematical analysis through theLyapunov method is discussed in this section. Section 9.4 details the
432 Wireless Ad Hoc and Sensor Networkssimulation results of the proposed scheme in comparison with the availablecongestion control scheme for sensor networks such as CODA and802.11. Finally, conclusions are drawn.9.2 Overview of Predictive Congestion ControlThe network congestion, shown in Figure 9.1, occurs when either theincoming traffic (received and generated) exceeds the capacity of theoutgoing link or link bandwidth drops because of channel fading causedby path loss, shadowing, and Rayleigh fading. The latter one is commonto wireless networks. Therefore, the overall objective of this paper is todevelop a novel way of utilizing the channel state in rate adaptation anda new MAC protocol using the mathematical framework, capturing channelstate, backoff intervals, delay, transmitted power, and throughput.Reserving resources for multimedia traffic in wired networks is wellunderstood. On the other hand, the impact of reserving resources inwireless networks on meeting QoS is not clear. Although most of theavailable scheduling schemes (Bennett and Zhang 1996, Vaidya et al. 2000,Wang et al. 2005, Kuo and Kuo 2003) tend to vary the rate based on traffictype, they all assume that the channel is time invariant, which is a strongassumption. In this chapter (Zawodniok and Jagannathan 2006), weconsider dynamic channel and network state. To accommodate thesechanges we will vary the backoff intervals of all the nodes based on thenetwork and channel conditions to transmit data. This idea is utilizedeven during congestion due to channel fading.In the next subsection, the predictive congestion strategy is discussed.Subsequently, an overview of the congestion control scheme is presented.CongestionindicationCongestionindicationSensornodes(sources)ClusterheadRelaynodeFading channelBase station(destination)Congestion due to lower capacityof outgoing link when comparedto incoming trafficCongestion due to reducedcapacity of link to a base station(caused by fading channel)FIGURE 9.1Congestion in wireless sensor networks.
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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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