Page 2 Lecture Notes in Computer Science 2865 Edited by G. Goos ...

More documents

Recommendations

Info

On-demand Routing in MANETs:The Impact of a Realistic Physical Layer ModelLiang Qin and Thomas KunzCarleton University, Ottawa, Ont., Canada K1S 5B6{lqin,tkunz}@sce.carleton.caAbstract. Most simulations and performance comparisons of mobile ad hocnetwork routing protocols are based on a simplistic and idealistic physical layermodel. In real applications, there are different kinds of noise or interference thatimpact the signal power. We use a shadowing propagation model in our simulationevaluation of two on-demand routing protocols: AODV and DSR. Becauseof signal power fluctuation, active routes will break, which causes significantthroughput degradation and longer packet delay. In this paper, we analyze theimpact of a shadowing model on the performance of these two routing protocols.Then we set a new signal power threshold during the route discoveryprocess so that only those links with strong enough signal power will be chosen;we also reduce some control messages for DSR. The simulation resultsshow significant increases in packet delivery ratio and decreases in packet latencyfor both protocols.1 IntroductionA mobile ad hoc network (MANET) is an infrastructure-less network. Because theradio transmission range is limited and mobile nodes are free to move randomly, theroutes are subject to frequent failure. Dozens of routing protocols for MANET havebeen proposed, for example, Destination-Sequenced Distance-Vector Routing(DSDV)[1], Temporally-Ordered Routing Algorithm (TORA) [2], Dynamic SourceRouting protocol (DSR)[3], Signal Stability-Based Adaptive Routing (SSA) [4], andAd-hoc On-Demand Distance Vector Routing (AODV)[5]. Performance evaluationsand comparisons between several routing protocols have been published in [6][7]. Butthese evaluations are based on simulations using a two-ray ground propagation model.In real applications, the path between transmitter and receiver can be line-of-sight, orobstructed by physical obstacles between them, thus the signal strength on the receivernot only depends on the distance, but also on the environment. At the timewhen this research began, few simulations tried to use different propagation models toevaluate routing protocol performance, and most routing protocols assume that packetloss over a link indicates a link breakage due to node mobility. Takai [8] presentssimulation results using free space, Rayleigh and SIRCIM (Simulation of IndoorRadio Channel Impulse Response Models with Impulse Noise) propagation models ina 130m by 130m area with 20 mobile nodes. Goff [9] proposes some ways to verifywhether the signal fluctuation is caused by channel fading.This paper concentrates on the impact of a shadowing propagation model on theperformance of two on-demand routing protocols: AODV and DSR. Simulations ofS. Pierre, M. Barbeau, and E. Kranakis (Eds.): ADHOC-NOW 2003, LNCS 2865, pp. 37–48, 2003.© Springer-Verlag Berlin Heidelberg 2003
38 L. Qin and T. Kunzthese two protocols show that the signal strength fluctuations cause routes to be assumedbroken, and in turn the packet delivery ratio significantly decreases and packetdelay increases. We apply a new signal power threshold during the route discoveryprocess, so that only the routes with strong signal strength will be chosen. For DSR,we also reduce some control messages to reduce traffic. Our simulation results showthat this can significantly increase the packet deliver ratio and deceased packet latencyfor both protocols.An overview of free space, two-ray ground and shadowing propagation models areprovided in Section 2. Section 3 analyses the impact of a shadowing model on theperformance of routing protocols, and presents our proposal. Section 4 contains thesimulation results under different mobility patterns and parameters. Section 5 drawsour conclusion and directions for future research. A more detailed introduction toDSR and AODV can be found in [3] [5].2 Propagation ModelsThe free space propagation model is used for the situation when the transmitter andreceiver have a clear line-of-sight path. The received power at receiver P ris given bythe Friis free space equation [10]:2P G G λt t rP ( d)=r 2 2(4π) d Lwhere P tis the transmitted signal power, G tand G rare the antenna gains of the transmitterand the receiver respectively. L (L≥1) is the system loss, and λ is the wavelength.The two-ray ground reflection model considers both the direct path and aground reflection path between the transmitter and receiver. At a long distance, thismodel is more accurate than the free space model. The received signal power at adistance d from the transmitter can be expressed by [10]:P ( d)r=2PG G h ht t r t r4dwhere h rand h tare the heights of the transmit and receive antenna respectively.The free space model and two-ray ground model predict that received power decaysas a function of distance, the radio transmission range is a perfect circle. The shadowingmodel is a statistical model. The mean received power at distance d is computedrelative to P r(d 0), represented by (in dB) [10]:P ( d)[ ]P ( d )d= −10β log( ) X σdr+dBr 00Equation (3) is also called log-normal shadowing model. In this paper, we will simplyrefer to it as shadowing model, and the free space and two-ray ground model as idealmodel or ideal environment. The shadowing model consists of two parts. The first oneis the path loss model, d 0is a reference distance, β is called loss exponent. The secondpart reflects the variation of the received power at certain distance. X σ is a Gaussian2(1)(2)(3)
Page 2 and 3: Lecture Notes in Computer Science 2
Page 4 and 5: Samuel Pierre Michel BarbeauEvangel
Page 6: PrefaceAd Hoc Networks are wireless
Page 9 and 10: Program CommitteeG. Alonso, ETHZ, S
Page 11 and 12: XTable of ContentsResisting Malicio
Page 13 and 14: 2 H. Dubois-Ferrière, M. Grossglau
Page 21 and 22: 10 H. Dubois-Ferrière, M. Grossgla
Page 23 and 24: SAFAR: An Adaptive Bandwidth-Effici
Page 25 and 26: 14 J. Doshi and P. Kilambiour proto
Page 27 and 28: 16 J. Doshi and P. Kilambipacket th
Page 29 and 30: 18 J. Doshi and P. Kilambibandwidth
Page 31 and 32: 20 J. Doshi and P. Kilambi5 Simulat
Page 33 and 34: 22 J. Doshi and P. Kilambiquery its
Page 35 and 36: 24 J. Doshi and P. Kilambi4. David
Page 37 and 38: 26 P. Narayan and V.R. SyrotiukThe
Page 39 and 40: 28 P. Narayan and V.R. Syrotiuk2.2
Page 41 and 42: 30 P. Narayan and V.R. Syrotiukrand
Page 43 and 44: 32 P. Narayan and V.R. Syrotiukenti
Page 45 and 46: 34 P. Narayan and V.R. SyrotiukDSRA
Page 47: 36 P. Narayan and V.R. Syrotiuk7. A
Page 51 and 52: 40 L. Qin and T. Kunzsidered broken
Page 53: 42 L. Qin and T. KunzP = Prdlog( )
Page 57 and 58: 46 L. Qin and T. KunzFig. 5. Averag
Page 59 and 60: 48 L. Qin and T. Kunz6. J. Broch et
Page 61 and 62: 50 M. Diha and S. Pierrethe propose
Page 63 and 64: 52 M. Diha and S. Pierre3. All proc
Page 65 and 66: 54 M. Diha and S. Pierre3.3 Perform
Page 67 and 68: 56 M. Diha and S. PierreCmip/Cprop0
Page 69 and 70: 58 M. Diha and S. PierreThe tunneli
Page 71 and 72: Proactive QoS Routing in Ad Hoc Net
Page 73 and 74: 62 Y. Ge, T. Kunz, and L. LamontFig
Page 75 and 76: 64 Y. Ge, T. Kunz, and L. Lamontits
Page 77 and 78: 66 Y. Ge, T. Kunz, and L. Lamonttha
Page 79 and 80: 68 Y. Ge, T. Kunz, and L. Lamontwhi
Page 81 and 82: 70 Y. Ge, T. Kunz, and L. Lamontver
Page 83 and 84: Delivering Messagesin Disconnected
Page 85 and 86: 74 R. Shah and N.C. Hutchinson3.1 T
Page 87 and 88: 76 R. Shah and N.C. Hutchinsonset c
Page 89 and 90: 78 R. Shah and N.C. HutchinsonTable
Page 91 and 92: 80 R. Shah and N.C. HutchinsonOracl
Page 93 and 94: 82 R. Shah and N.C. Hutchinsonthe r
Page 95 and 96: Extending Seamless IP Multicast Edg
Page 97 and 98: 86 P.M. Ruiz et al.Section 4 presen
Page 99 and 100:
88 P.M. Ruiz et al.Access NetworkCo
Page 101 and 102:
90 P.M. Ruiz et al.Access NetworkR
Page 103 and 104:
92 P.M. Ruiz et al.MIGAccess Networ
Page 105 and 106:
94 P.M. Ruiz et al.10.90.81-hop-750
Page 107 and 108:
A Uniform Continuum Model for Scali
Page 109 and 110:
98 E.W. Grundke and A.N. Zincir-Hey
Page 111 and 112:
100 E.W. Grundke and A.N. Zincir-He
Page 113 and 114:
102 E.W. Grundke and A.N. Zincir-He
Page 115 and 116:
Probabilistic Protocols for Node Di
Page 117 and 118:
106 G. Alonso et al.A node discover
Page 119 and 120:
108 G. Alonso et al.frequency alloc
Page 121 and 122:
110 G. Alonso et al.- D is a diagon
Page 123 and 124:
112 G. Alonso et al.and therefore,
Page 125 and 126:
114 G. Alonso et al.complicated. Fo
Page 127 and 128:
Towards Adaptive WLAN Frequency Man
Page 129 and 130:
118 F. Gamba, J.-F. Wagen, and D. R
Page 131 and 132:
Page 133 and 134:
Page 135 and 136:
Page 137 and 138:
Page 139 and 140:
Analyzing Split Channel Medium Acce
Page 141 and 142:
130 J. Deng, Y.S. Han, and Z.J. Haa
Page 143 and 144:
Page 145 and 146:
Page 147 and 148:
Page 149 and 150:
Page 151 and 152:
Preventing Replay Attacks for Secur
Page 153 and 154:
142 J. Zhen and S. SrinivasTraffic
Page 155 and 156:
144 J. Zhen and S. SrinivasFig. 2.
Page 157 and 158:
146 J. Zhen and S. Srinivasbeginnin
Page 159 and 160:
148 J. Zhen and S. Srinivasthe time
Page 161 and 162:
150 J. Zhen and S. Srinivas16. Y. H
Page 163 and 164:
152 M. Just, E. Kranakis, and T. Wa
Page 165 and 166:
Page 167 and 168:
Page 169 and 170:
Page 171 and 172:
Page 173 and 174:
Page 175 and 176:
A New Framework for Building Secure
Page 177 and 178:
166 H.-P. Bischof, A. Kaminsky, and
Page 179 and 180:
Page 181 and 182:
Page 183 and 184:
Page 185 and 186:
Page 187 and 188:
176 G. Calinescuof the UDG 2-hops a
Page 189 and 190:
178 G. CalinescuSection 3 describe
Page 191 and 192:
180 G. CalinescuWhen receiving a pa
Page 193 and 194:
182 G. CalinescuRyvxFig. 3. The unn
Page 195 and 196:
184 G. Calinescu< nodeID, pieceID,
Page 197 and 198:
186 G. Calinescu7. Heinz Breu and D
Page 199 and 200:
188 S.O. Krumke et al.desirable in
Page 201 and 202:
190 S.O. Krumke et al.2.2 Bicriteri
Page 203 and 204:
192 S.O. Krumke et al.1. From the g
Page 205 and 206:
194 S.O. Krumke et al.The following
Page 207 and 208:
196 S.O. Krumke et al.1. Let α =6l
Page 209 and 210:
198 S.O. Krumke et al.for any given
Page 211 and 212:
200 S. PatilIDEA uses the concept o
Page 213 and 214:
202 S. PatilAfter flooding the quer
Page 215 and 216:
204 S. Patil5 Simulation ModelSourc
Page 217 and 218:
206 S. PatilAvg. Aggregate Energy C
Page 219 and 220:
208 S. PatilTable 1. Probability of
Page 221 and 222:
210 S. Patilergy consumption of the
Page 223 and 224:
212 T. Chu and I. Nikolaidisenergy
Page 225 and 226:
214 T. Chu and I. Nikolaidisaim at
Page 227 and 228:
216 T. Chu and I. Nikolaidis12: end
Page 229 and 230:
218 T. Chu and I. Nikolaidis1.8E+09
Page 231 and 232:
220 T. Chu and I. Nikolaidis2.0E+10
Page 233 and 234:
222 T. Chu and I. Nikolaidisattack
Page 235 and 236:
224 F.J. Molina, J. Barbancho, and
Page 237 and 238:
Page 239 and 240:
Page 241 and 242:
Page 243 and 244:
Page 245 and 246:
Page 247 and 248:
236 G. Calinescu and P.-J. Wan3.5 4
Page 249 and 250:
238 G. Calinescu and P.-J. Wanmum s
Page 251 and 252:
240 G. Calinescu and P.-J. WanFrom
Page 253 and 254:
242 G. Calinescu and P.-J. Wan5 Alg
Page 255 and 256:
244 G. Calinescu and P.-J. WanThe n
Page 257 and 258:
246 G. Calinescu and P.-J. Wan9. A.
Page 259 and 260:
248 C.J. Colbourn, V.R. Syrotiuk, a
Page 261 and 262:
Page 263 and 264:
Page 265 and 266:
Page 267 and 268:
Page 269 and 270:
Page 271 and 272:
260 S. Bhadra and A. Ferreirathe mo
Page 273 and 274:
262 S. Bhadra and A. FerreiraIn thi
Page 275 and 276:
264 S. Bhadra and A. FerreiraDefini
Page 277 and 278:
266 S. Bhadra and A. FerreiraThis s
Page 279 and 280:
268 S. Bhadra and A. FerreiraTheore
Page 281 and 282:
270 S. Bhadra and A. Ferreira4. T.
Page 283 and 284:
272 M.K. Denko and Q.H. MahmoudAn i
Page 285 and 286:
274 M.K. Denko and Q.H. Mahmoud3.1
Page 287 and 288:
276 M.K. Denko and Q.H. Mahmoud5. D
Page 289 and 290:
278 B. Macabéo, S. Pierre, and A.
Page 291 and 292:
280 B. Macabéo, S. Pierre, and A.
Page 293 and 294:
282 A. Benslimane and A. BachirIn [
Page 295 and 296:
284 A. Benslimane and A. BachirFig.
Page 297 and 298:
286 A. Benslimane and A. Bachir5 Co
Page 299 and 300:
288 L. Hughes, K. Shumon, and Y. Zh
Page 301 and 302:
Page 303 and 304:
Page 305 and 306:
3BerlinHeidelbergNew YorkHong KongL
Page 307 and 308:
Series EditorsGerhard Goos, Karlsru
Page 310 and 311:
Organizing CommitteeConference Co-c
Page 312 and 313:
Table of ContentsSpace-Time Routing
Page 314:
Author IndexAlonso, G. 104Bachir, A
show all

Page 2 Lecture Notes in Computer Science 2865 Edited by G. Goos ...

Create successful ePaper yourself

Delete template?

Save as template?