Wireless Ad Hoc and Sensor Networks

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Optimized Energy and Delay-Based Routing 365StartCreate an empty MPR set MPR(s)Add the node in N(s) toMPR(s)YIs there a node in N 2 (s)with only one neighborin N(s)?YIs the node in N(s)already in MPR(s)?NNDoes there exist anode in N 2 (s) forwhich MPR isnot selected?NENDYSelect a neighbor from N(s) as MPRwith minimum cost: C MPRs, N(s), N 2 (s)according to equation (8.1)YIs the node inN(s) alreadyin MPR(s)?NAdd the node in N(s) to MPR(s)FIGURE 8.3Flowchart for the MPR selection algorithm.
366 Wireless Ad Hoc and Sensor Networksthe figure. The following steps are followed according to the MPR selectionalgorithm of OEDR:1. Start with an empty MPR set MPR() s of node s : MPR()s = {}2. Nodes p 1 , p 2 , p 4 , and p 5 each have only one neighbor in the one-hopneighbor set Ns (). Add n 1 and n 2 to MPR() s : MPR() s = { n1, n2}3. For each node in N2 () s for which MPR node is not selected ( p 3 ,p 6 , p 7 , and p 8 ), do the following:• For node p 3 , there are two possible paths from s to reach p 3 .MPRFor each of the paths, calculate the MPR costs: C sn , =1,p 34+ 3+ 1 /. 0 5=9; C MPRMPR= 6+ 4+ 1/. 0 2=15. Because C issmaller, select as sn , 2,MPR. p 3sn , 1,p 3n 1 However n 1 is already in the MPRset: MPR() s = { n1, n2}MPR• For node p 6 : CMPRsn , 2,p 6= 6+ 7+ 1/. 02=18; C sn , , p = 5+ 5+ 1/.0 6 =3 6MPR11.67. Because C sn , , p is smaller, select n as MPR and add to3 6 3the MPR set: MPR() s = { n1, n2,n3}MPRMPR• For node p 7 : C sn , 3,p 7 = 5+ 3+ 1/. 0 6=9. 67 ; C sn , , p= 6+ 5+ 1/.0 254 7MPR= 15. Because C sn , , p is smaller, select n as MPR. However,3 7 3 n 3is already in the MPR set: MPR() s = { n1, n2, n3}MPR• For node p 8 : C sn , 4,p 8= 6+ 6+ 1 /. 025 = 16 ; C MPR= 4+ 5+ 1/.0 8 =10.25. Because is smaller, select sn , 5,as p 8MPRC sn , , pn MPR and add to5 85the MPR set: MPR() s = { n , n , n , n }1 2 3 5By following the MPR selection algorithm of the OEDR, the nodesselected as MPRs are n 1 , n 2 , n 3 , and n 5 . In contrast, it can be seen fromFigure 8.1 that the nodes selected as MPRs using the OLSR were n 1 , n 2 ,and n 4 . The following Table 8.1 shows the link costs, calculated accordingto equation (8.1), to reach the two-hop neighbors from s , when the MPRselection is performed, using the OLSR and OEDR protocols:It can be observed from this table that the proposed OEDR protocolsignificantly reduces the cost to reach the two-hop neighbors, when comparedto OLSR. This is achieved by using an efficient MPR selectionalgorithm that considers the energy-delay costs, to select the MPR nodes.TABLE 8.1MPR Selection Algorithm Comparison of OLSR and OEDR ProtocolsCost to Reach the Two-Hop Neighbors (Via the MPR node) According to (1)Protocol p 1 p 2 p 3 p 4 p 5 p 6 p 7 p 8OLSR 9 ( n 1 ) 11 ( n 1 ) 15 ( n 2 ) 16 ( n 2 ) 13 ( n 2 ) 18 ( n 2 ) 15 ( n 4 ) 16 ( n 4 )OEDR 9 ( n 1 ) 11 ( n 1 ) 9 ( n 1 ) 16 ( n 2 ) 13 ( n 2 ) 11.67 ( n 3 ) 9.67 ( n 3 ) 10.25 ( )n 5
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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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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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