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Towards the Performance Analysis of IEEE 802.11 in Multi-hop Ad-Hoc Networks
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Towards the Performance Analysis of IEEE 802.11 in Multi-hop Ad-Hoc Networks

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Towards the Performance Analysis of IEEE 802.11

Towards the Performance Analysis of IEEE 802.11
in Multi-hop Ad-Hoc Networks

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Towards the Performance Analysis of IEEE 802.11 in Multi-hop Ad-Hoc Networks Towards the Performance Analysis of IEEE 802.11 in Multi-hop Ad-Hoc Networks Document Transcript

  • Towards the Performance Analysis of IEEE 802.11 in Multi-hop Ad-Hoc Networks Yawen Barowski and Saˆ d Biaz a Prathima Agrawal Computer Science and Software Eng. Dept. Wireless Engineering Research and Education Center Samuel Ginn College of Engineering Samuel Ginn College of Engineering Auburn University Auburn University Auburn, AL 36849-5347, USA Auburn, AL 36849-5347, USA Email: {dyeaiya,biazsaa}@eng.auburn.edu Email: agrawpr@eng.auburn.edu Abstract— The performance of IEEE 802.11 in multi-hop In all previous work, one or more performance aspects werewireless networks depends on the characteristics of the protocol reported for single hop IEEE 802.11 networks under saturateditself, and on those of the upper layer routing protocol. Extensive traffic conditions. Inspired by Bianchi’s saturated throughputwork has been done to analyze and evaluate the performance ofsingle hop networks under saturated traffic conditions, either model, we propose a model to describe the behavior of IEEEthrough simulations or mathematical modeling. Little work 802.11 under different offered traffic loads. This model showshas been done on the analysis of the performance of IEEE the effect of the offered load on the transmission probability.802.11 protocol under unsaturated traffic conditions that arise in We also propose a three dimensional model to attempt tomulti-hop networks. This paper proposes analytical models and describe the behavior of multi-hop 802.11 networks. The 3Dscenarios to analyze the IEEE 802.11 protocol under unsaturatedtraffic conditions for multi-hop networks. ns-2 simulations with model allows the modeling of not only data sources (as indifferent network configurations validate the proposed models for Bianchi’s model) but also relay stations that forward traffic.performance metrics such as throughput, message delay, average Section II of this paper briefly describes the IEEE 802.11queue length, and energy consumption. Simulation results show DCF scheme, stressing key elements related to this paper. Inthat the proposed models work well. Key to the modeling of the Section III-A, work related to this paper [3] and our modelmulti-hop networks is a treatment of the upper layer routingprotocols that can affect the network performance through the for analyzing the protocol under unsaturated traffic loads isway they forward packets and the impact of that on the traffic discussed. This model is extended in Section IV into a threeload. The model proposed takes into account the impact of the dimensional model that could be used to model IEEE 802.11upper layer routing protocol by introducing a packet acceptance in multi-hop networks.factor with which each relay station accepts packets from thewireless medium before forwarding the same. II. IEEE 802.11 DCF S CHEME IEEE 802.11 is a contention-based MAC protocol. It has I. I NTRODUCTION two working modes. The point coordination function (PCF) IEEE 802.11 [11] medium access control (M AC) protocol mode is a centralized scheme designed for an infrastructureis currently the most popular random access M AC layer network. This mode uses a point coordinator that operates atprotocol used in wireless ad-hoc networks. It uses a distributed the base station to select the next wireless station that willcoordination function (DCF) as the primary mechanism for transmit. The distributed coordination function (DCF) modeaccessing the medium. DCF has two modes: the basic broad- makes use of “carrier sense multiple access with collisioncast mode, and the MACAW [2], [13] based RTS/CTS mode avoidance” (CSMA/CA [13], [2]). This paper focuses on(Request To Send/Clear To Send). The efficiency of the DCF. DCF allows automatic and adaptive medium sharingIEEE 802.11 protocol directly affects utilization of channel between compatible physical layers (PHYs) through the usecapacity and system performance. Performance analysis of of CSM A/CA and a random backoff procedure. CarrierIEEE 802.11 has been done experimentally and analytically: sense is performed both through a mechanism at the physicalsaturated throughput of IEEE 802.11 has been extensively layer and a virtual mechanism at the MAC layer. The virtualinvestigated [3], [5], [7], [16], [19]. Bianchi [3] proposed carrier sense mechanism is achieved by distributing reservationa two-dimensional Markov chain model to analyze the perfor- information announcing the impending use of the medium.mance of the IEEE 802.11 exponential backoff scheme, and The exchange of RTS and CTS frames prior to the actual datato evaluate the saturated throughput. There are inaccuracies frame is one means of distribution of this medium reservationin Bianchi’s model, mentioned in [19] which also proposes information. A wireless station that needs to send a data framemodifications. should invoke the carrier sense mechanism to determine the Other performance metrics such as message delay, data loss, state of the medium. If the medium is idle for a specific lengthpower consumption, and scalability, were investigated in [4], of time, the DCF interframe space (DIF S), the wireless[6], [8], [10], [14], [15], [18]. station should generate a random time period for an additionalIEEE Communications Society / WCNC 2005 100 0-7803-8966-2/05/$20.00 © 2005 IEEE Authorized licensed use limited to: Annamalai University. Downloaded on July 28,2010 at 05:13:53 UTC from IEEE Xplore. Restrictions apply.
  • deferral before transmission. The backoff procedure is invoked of this state can be easily calculated. Figure 1 outlines ourwhen the medium is sensed busy. The MAC sets its backoff model.timer to a random interval using the formula, B. The System Model Count = Random() ∗ σ In order to analyze the protocol under unsaturated traffic, we make the same assumption as is done in [17]. We assume where Random() returns a random integer within that stations are statistically identical, each station has idle[CWmin , CWmax ] where CWmin and CWmax are the min- periods that are exponentially distributed, and packet length isimum and maximum contention window sizes respectively. constant. For a station under unsaturated traffic, the transitionσ is the system time slot set by the physical layer. The from state (b, 0) to state (0, c), which means the station reachesbinary exponential backoff mechanism increases the range of the next transmission cycle after successfully sending out a[CWmin , CWmax ] as contention increases, with the objective packet, is not guaranteed, as it is in Bianchi’s model. This isof staggering the conflicting transmissions. A station perform- only true when the station has at least one buffered packet.ing the backoff procedure uses the carrier sense mechanism to We add an additional state (q = 0) in our model to handledetermine whether there is activity during each backoff slot. If the situation in which the station has no buffered packet. Letno medium activity is detected, the backoff procedure decre- λ be the offered load of each station, and q0 the probabilityments Count by σ. Otherwise, Count is not decremented that a station has no buffered packet. In Figure 1, all statesfor that slot. The minimum time between transmission of and state transitions, except the state (q = 0), are based on theinteractive packets (RTS or CTS) is the short interframe space condition that there is at least one packet to be sent. When a(SIF S). Since DIF S > SIF S, the protocol provides higher station gets to state (b, 0), and sends a packet, it will reach stateaccess priority to RTS and CTS frames. (b + 1, c) if the packet collides and needs to be retransmitted. If a transmission succeeds, the wireless station will follow If the packet is successfully sent, it will reach state (q = 0) orthe same procedure for the next transmission. If a transmission state (0, c) depending on whether or not there is any bufferedfails, the DCF procedure will be repeated with an exponential packet. When a station is in state (q = 0), which means itbackoff mechanism. At the first transmission, the range of currently has no packet to send, it will stay there until a packetRandom(0) is from zero to W0 , where W0 is the maximum arrives. Then it will reach one of the (0, c) states and start acontention window at stage 0. At the ith failure, the range of 1 transmission cycle. The average packet arrival interval is λ . SoRandom() is extended from zero to Wi , where Wi = 2i−1 ∗ the transition from state (q = 0) to state (0, c) has transitionW0 . i is called the backoff stage. So, as the stage increases, 1 1 probability W0 and transition duration λ . The state transitionthe range of possible contention window sizes increases. After diagram shown in Figure 1 is governed by the followinga successful transmission, the stage is reset to 0. transition probabilities and durations. III. M ODEL FOR U NSATURATED T RAFFIC 1) The backoff counter decrements, and the station makes a transition from state (b, c) to state (b, c − 1) when theA. Related Work medium is idle Bianchi [3] proposed a two-dimensional Markov chain P {(b, c − 1)|(b, c)} = Pidlemodel to analyze the performance of the IEEE 802.11 protocol t{(b, c − 1)|(b, c)} = σin single hop wireless networks. Two parameters, backoff stage 2) The backoff counter suspends, and the station stays inand backoff counter value, are used to describe the state of an state (b, c) when the medium is busyIEEE 802.11 station. The pair (backoff stage, backoff counter P {(b, c)|(b, c)} = 1 − Pidle P ∗Ts +Pf ∗Tvalue), referred to as (b, c), describes the state of a station, t{(b, c)|(b, c)} = succ 1−Pidleail fwhere backoff stage b varies from 0 to a maximum backoff 3) The station sends a packet and the packet collides, thestage, B and the counter value c takes any value between 0 and station reaches state (b + 1, c)Wb . If a station reaches state (b, 0) (i.e., the backoff counter P {(b + 1, c)|(b, 0)} = Wcoll 0 ≤ b ≤ (B − 1) P b+1value becomes 0), the station will send out a packet. If the P {(B, c)|(B, 0)} = WB Pcollpacket collides (with probability Pcoll ) then the station will t{(b + 1, c)|(b, 0)} = t{(B, c)|(B, 0)} = Tftransit with probability Wcoll to some state (b + 1, c) with a P b+1 4) The station sends a packet successfully, and the stationhigher backoff stage. If the packet does not collide, the station reaches state (0, c) since it has more packets to send.will return to some state (0, c) (recall that in such a state, c P {(0, c)|(b, 0)} = (1−q0 )∗(1−Pcoll ) 0 ≤ c ≤ W0 W0can be any value between 0 and W0 ) with probability 1−Pcoll .W0 t{(0, c)|(b, 0)} = Ts 0 ≤ b ≤ B One difference between Bianchi’s model and ours is the 5) The station sends a packet successfully, and the stationtransition probability from state (b, c) to state (b, c − 1), which reaches state (q = 0) since it has no more packets tois also addressed in [19]. Bianchi’s model assumes that the send.probability of the transition from state (b, c) to state (b,c-1) is P {(q = 0)|(b, 0)} = q0 ∗ (1 − Pcoll ) 0 ≤ b ≤ B1.0. Also, for each transition, transition duration is specified t{(q = 0)|(b, 0)} = Tsalong with transition probability. With this feature, the average 6) The station has an arrival packet, and leaves state (q =time that a station stays in one state and the time proportion 0)IEEE Communications Society / WCNC 2005 101 0-7803-8966-2/05/$20.00 © 2005 IEEE Authorized licensed use limited to: Annamalai University. Downloaded on July 28,2010 at 05:13:53 UTC from IEEE Xplore. Restrictions apply.
  • q=0 1/W0 (1−q0)*(1−Pcoll)/W0 q0*(1−Pcoll) 0,0 0,1 0,2 0,W0−2 0,W0−1 P idle 1−Pidle 1−Pidle 1−Pidle (1−q0)*(1−Pcoll) i−1,0 q0*(1−Pcoll) Pcoll/Wi i,0 i,1 i,2 i,Wi−2 i,Wi−1 P idle 1−Pidle 1−Pidle 1−Pidle Pcoll/Wb (1−q0)*(1−Pcoll) B,0 B,1 B,1 B,Wb−2 B,Wb−1 1−Pidle 1−Pidle Fig. 1. Overview of the Station Model 1 P {(0, c)|(q = 0)} = W0 0 ≤ c ≤ W0 C. Solutions and Results 1 t{(0, c)|(q = 0)} = λ With the diagram and conditions mentioned above, we could Let us denote P(b,c) as the probability that the station obtain the stationary probability distribution of the model,reaches state (b, c). From the model, we can compute that except that there is still an unknown, Daccess . Suppose thatunder the condition that there is at least one packet to send, a packet is successfully sent on the first try, and the timethe station has probability τ to send a packet in any time slot. it takes is T S. Otherwise, if it fails on the first try, which τ= B P(b,0) takes time T F , then it will have to wait for the station to b=0 reach the next sending state (b, 0) before it is sent again. InThen the probability that a station will send a packet in any order to derive Daccess , we need to express the average timetime slot is, between two sending states. Let us denote D as the average p = (1 − q0 ) ∗ τ time between two sending states. In practice, D is also the time that a station takes to complete a backoff procedure afterIn a system that consists of n stations, the probability that a a failed transmission.sent packet collides is, Consider that each transmission starts with a backoff pro- Pcoll = 1 − (1 − p)(n−1) cedure. We have, For the whole system, the probabilities of a successful T F = Tf + Dpacket, failed packet and no packet in any time slot are Psucc , T S = Ts + DPf ail and Pidle , respectively, Let Rτ be the set of sending states, i.e., Psucc = n ∗ p ∗ (1 − Pcoll ) Pidle = (1 − p)n Rτ ={(b; c) : c = 0} Pf ail = 1.0 − Psucc − Pidle The probability that a station is in Rτ is τ . Suppose τi ∈ R As for probability q0 , let us denote the average access delay, and τj ∈ R are two consecutive states (in Rτ ) that the stationthe time between when a packet reaches the M AC layer and goes through. Then between two consecutive visits to Rτ (τiwhen it is successfully sent, Daccess . Daccess is also the packet and τj ), the expected number of visits to any state k ∈ Rτ is / Pkservice time if we treat each station as a M/M/1/N queue τ . Assume that the average time a station will stay in statesystem, in which N is the maximum queue length of the k is µk . We can derive the time D between two consecutivequeue. For a M/M/1/N queue, the probability that there is sending states asno packet in the queue is, D= Pk µk k;k∈R,k∈Rτ / τ ρ = λ ∗ Daccess D is also the time between two consecutive transmissions of 1−ρ q0 = 1−ρN +1 any packet. The probability that a packet would be successfullyIEEE Communications Society / WCNC 2005 102 0-7803-8966-2/05/$20.00 © 2005 IEEE Authorized licensed use limited to: Annamalai University. Downloaded on July 28,2010 at 05:13:53 UTC from IEEE Xplore. Restrictions apply.
  • 0.06 0.4 2 n=5 0.35 Transmission Probability n=30 Average Access Delay 0.05 Average Throughput 0.3 n=50 1.5 0.04 0.25 0.03 0.2 1 0.15 0.02 n=5 0.1 0.5 n=5 0.01 n=30 n=30 0.05 n=50 n=50 0 0 0 0.2 0.4 0.6 0.8 1 1.2 0.2 0.4 0.6 0.8 1 1.2 0.2 0.4 0.6 0.8 1 1.2 Offered Traffic Load Offered Traffic Load Offered Traffic Load (a)Transmission Prob. (b)Average Access Delay (c)Average Throughput Fig. 2. Analytic Results from the Model 0.05 0.1 2 2 Simulation Simulation Simulation Simulation Model Model Model ModelAverage Access Delay Average Access Delay Average Throughput Average Throughput 0.04 0.08 1.5 1.5 0.03 0.06 1 1 0.02 0.04 0.5 0.5 0.01 0.02 0 0 0 0 0.2 0.4 0.6 0.8 1 1.2 0.2 0.4 0.6 0.8 1 1.2 0.2 0.4 0.6 0.8 1 1.2 0.2 0.4 0.6 0.8 1 1.2 Offered Traffic Load Offered Traffic Load Offered Traffic Load Offered Traffic Load(a) Average Access Delay n = 5 (b) Average Access Delay n = 10 (c)Average Throughput n = 5 (d)Average Throughput n = 10 Fig. 3. Results from the Simulationsent on the first try is (1 − Pcoll ), on the second try is Pcoll ∗ show that the probability a station sends a packet in any time(1 − Pcoll ), and so on. The probability that a packet would slot when it has packets to send, τ , is not independent of thebe sent successfully on the ith try is Pcoll ∗ (1 − Pcoll ). If a i−1 offered traffic load. As the offered load increases, τ decreases.packet is sent successfully on the first try, it takes T S. If on For network size less than 50, the breaking point is aroundthe second try, it takes (T F + D + Ts ), which is (T F + T S), 0.65. After the overall traffic load exceeds 0.65, the averageand so on. If a packet is sent successfully on the ith try, it access delay increases steeply, and the throughput becomestakes ((i − 1) ∗ T F + T S). The average access delay can be saturated. After the load exceeds 0.8, the average access delayderived as and τ do not change much. Figures 3(a)-(d) plot both ns- N 2 simulation results And analytical results for network size n=1 Pcoll (1 − Pcoll )(T S + (n − 1) ∗ T F ). n−1 5 and 10. The simulation results fit quite well the analytic where N is the number of retransmission times minus one. results. When the network size increases, the ns-2 simulationWhen N goes to infinity, the access delay will be results fit well with the analytic results when the offered load Pcoll ∗T F is less than 0.65 or greater than 0.80. However, they show Daccess = T S + 1−Pcoll . a relatively large deviation from the analytic results when theDaccess can be expressed through Pcoll , and the stationary load is between 0.65 and 0.8. This is because when the offereddistribution can thus be obtained. load is greater than 0.65, the system is close to the saturation The average system throughput should be the sum of condition. The average access delay of each station is closethroughputs of all stations. For a single station, the throughput to or greater than the packet arrival rate. The estimation on ρshould be the throughput it has during the time it has packets to and q0 may not be accurate any more.send averaged over the total time that it has or has no packets In [9], Feeney and Nilsson gave a linear model for Lucentto send. IEEE802.11 2M bps P C cards. According to this model, the energy consumption in IEEE802.11 networks can be Tslot = i Pi µi (1−P(q=0) )∗τ (1−Pcoll )∗n associated with the size of sent packets. T hr = 8 ∗ L ∗ { Tslot } where L is the payload length. E = a ∗ Size + bFrom a single station’s point of view, there are four kinds of a is the energy consumption per byte, and b is the overhead fortime slots. A slot in which there is a successful packet, a slot sending a packet. a and b are different for sending, receiving,in which there is a collided packet, a slot in which the backoff and overhearing conditions. They also depend on whether orcounter decrements, and a slot in which there is no packet to not the station is within the range of the data source andsend. Tslot can be seen as the average slot time. Figures 2(a)- data destination. The idle state energy consumption does not(c) show some analytic results from the model. The x-axis in depend on the packet size. The paper gives an estimationeach figure is the normalized offered traffic load. Figure 2a) of idle state consumption rate e. We borrow this model forIEEE Communications Society / WCNC 2005 103 0-7803-8966-2/05/$20.00 © 2005 IEEE Authorized licensed use limited to: Annamalai University. Downloaded on July 28,2010 at 05:13:53 UTC from IEEE Xplore. Restrictions apply.
  • 200 20 40Average Energy Per Bit (uw/bit) Average Energy Per Bit (uw/bit) Average Energy Per Bit (uw/bit) 180 n=5 Simulation Simulation n=30 Model 35 Model 160 n=50 15 30 140 120 25 100 10 20 80 15 60 5 10 40 20 5 0 0 0 0.2 0.4 0.6 0.8 1 1.2 0.2 0.4 0.6 0.8 1 1.2 0.2 0.4 0.6 0.8 1 1.2 Offered Traffic Load Offered Traffic Load Offered Traffic Load (a)epb. (b)epb. n = 5 (c)epb. n = 10 Fig. 4. Energy Consumption Analysiscalculating per-packet energy consumption. In addition to other stations’ data. In this case, it is inappropriate to modelthe transition duration for each state, our model gives the every station as a saturated data source at all times.transition energy consumption for each state. For example, We propose a general scenario for the modeling of mutli-in the source station model, the transition from state (b, c) to hop wireless networks. We make the following assumptions.state (b, c−1) will consume e∗σ(w.sec) . The transition from At any given time, statistically, a certain number of stationsstate (b, 0) to state (0, c) will consume a ∗ L + b(w.sec), and within a given station’s transmission range act as data sourcesthe transition from state (b, 0) to state (b + 1, c) will consume that inject data traffic. These are called source stations. Othera ∗ l + b (w.sec), where l is the number of bytes sent during stations act as data relays that forward traffic within thea failed transmission. The average consumption in each state network. These are called relay stations. Source stations doEi can be calculated in the same way as the average state not forward data and relay stations do not generate data.duration, µi . For the source stations, our two dimensional model is The energy consumption of a station during queue empty sufficient to describe the behavior of the IEEE 802.11 MACstate is not very explicit. For source stations, when they are protocol under different traffic loads. For the relay stations,in the empty queue state there are two cases affecting energy the above model fails to correctly describe how packets areconsumption. In the first case, other stations have packets and queued. The above model represents the situation where orig-there is transmission activity on the medium; the station’s inal traffic is generated at the station, which is not true for relayenergy consumption includes the overhearing of packets in the stations. A relay station listens to the medium, gets packetsmedium. In the second case, all stations are empty and there from it and forwards the packets it receives. The numberis no transmission activity in the medium; the station’s energy of packets a relay station receives and accepts to forwardconsumption only includes idle energy consumption. Let ei depends on the upper layer routing protocol. For example, indenote the average energy consumption rate of any state i, the flooding protocol, a station broadcasts all its own packetsand πi denote the time proportion of each state i, then the and forwards packets from/to all its neighbors. A station willaverage energy consumption rate of the station, e, is, accept 100% of the traffic within its range. In the diffusion ei = Ei µi e= i πi ∗ ei routing protocol [12], as well as most routing protocols, a station will forward most of its traffic to the neighbor stationFigure 4.a) shows the analytic results for energy consumption on its estimated shortest path to the destination, and very littleof all source station networks under different traffic loads. traffic to other neighboring stations. So the station on theFigures 4.b) and 4.c) show the simulation results when n is shortest path will accept a packet with a probability that is5 or 10. epb in the figures means energy per bit. epb increases much higher than that of the other stations. Thus, for a multi-as the network size increases, decreases as the traffic load hop data transaction, the upper layer protocol determines theincreases. traffic load on the relay stations that are involved. Due to the different traffic loads that fall on each station, the status of IV. M ULTIHOP W IRELESS N ETWORK M ODEL each station’s link layer queue is different and undetermined. D EVELOPMENT The assumption that at any time there is at least one packetA. Multi-hop Wireless Network Scenario in the queue is not appropriate. In the IEEE 802.11 protocol model for single-hop wireless Since our work focuses on the analysis of the MAC proto-networks, every station is assumed to be equivalent. In most col, we need to find a way to isolate or take into accountprevious work described above, every station is assumed to be the upper layer protocol. That is what we propose in oura data source that sends out saturated traffic. In a multi-hop three dimensional model for the relay stations in the multi-wireless network, each station in the whole network is not hop wireless network. We introduce a probability Pin . Pinnecessarily a data source. A station may act as a data source is the probability that a relay station will accept to forwardfor a period of time when it has original data to send, while at (receive and later forward) a packet under the condition thatother times it may act as a relay station that simply forwards there is a successful packet from other stations in the medium.IEEE Communications Society / WCNC 2005 104 0-7803-8966-2/05/$20.00 © 2005 IEEE Authorized licensed use limited to: Annamalai University. Downloaded on July 28,2010 at 05:13:53 UTC from IEEE Xplore. Restrictions apply.
  • Pidle Pidle i+1;0,1 i+1;0,2 i+1;0,0 i+1;0,W0−2 i+1;0,W0−1 (1−Pcoll)/W0 Ps Ps Ps Ps Pidle Pidle (1−Pcoll)/W0 Pidle i;0,0 i;0,1 i;0,2 i;0,W0−2 i;0,W0−1 P3 Ps Ps Ps i+1;j,Wi−1 Ps Ps i;j−1,0 Pcoll/W Pin*Psucc’ Pin*Psucc’ j+1 Pin*Psucc’ i+1;m,Wm−1 P2 i;j,0 i;j,1 i;j,2 i;j,Wj−2 i;j,Wj−1 Pin*Psucc’ Ps Ps Ps Ps Ps i;m,0 i;m,1 i;m,2 i;m,Wm−2 i;m,Wm−1 Ps Ps Ps Pcoll/W m Fig. 5. Overview of the Relay Station ModelPin could be different for different relay stations. The different stays in some state (0, b, c) that has a queue length of zero,routing protocols distribute the traffic load among the stations then the station has no packet to send. Therefore, we assumein different ways. Pin could represent the distribution. that states (0, b, c) must have backoff stage b = 0 and that a For the work that has been done on the performance analysis station in one of these states will not transit to any other stateof the IEEE 802.11 protocol, all stations behave like saturated unless the station gets a successful packet from the medium.data sources. Saturated throughput is of the utmost interest. For This feature of the relay stations is different from that of themulti-hop wireless networks that include both source stations source stations since the source stations have new packets fromand relay stations, the average queue length, average delay, and the application layer.energy consumption at relay stations are of great interest. In With this 3-D model, the average queue length and averageorder to mathematically analyze those performance features, one hop delay can be derived. Please refer to [1] for detailswe add a dimension that takes queue length into account to on this 3-D model.our original model. C. ConclusionB. Model for the Relay Station The performance analysis with saturated traffic does not Figure 5 outlines the model for the relay stations. apply to nodes that do not originate traffic but may forward Let us denote state space R, it in on behalf of others. Sometimes, these relay stations may R = {(q, b, c) : Q ≥ q ≥ 0, B ≥ b ≥ 0, c ≥ 0} not have any packet to forward. Bianchi’s model applies only to saturated traffic. We presented a two-dimensional modelwhere q is the current queue length, Q is the maximum queue to analyze the IEEE802.11 performance under unsaturatedlength, b is the current backoff stage, and c is the current traffic conditions. Another challenge is the fact that not allbackoff counter value. relay stations receive the same amount of data to forward. This The foreground plane in Figure 5 represents the two dimen- amount is determined by the upper layer routing protocol. Wesional Markov model with queue length q = i. The model proposed a three-dimensional model that addresses this issueis extended in depth toward the background with increasing and allows to analyze IEEE802.11 on multi-hop networks inqueue length q. The background plane is the two dimensional which there are source stations and relay stations. SimulationMarkov model with queue length q = i + 1. Within the (b, c) results validate our analytic results.plane with a fixed value i, the model is similar to the twodimensional model. A station in a state on the (b, c) plane R EFERENCESwith queue value i (i.e., in state (i, b, c)) will transit to a state [1] Y. D. Barowski and S. Biaz, “The performance analysis of ieee 802.11on the (b, c) plane with queue length i + 1 if it accepts a under unsaturated traffic conditions,” Tech. Rep. CSSE04-09, Auburnpacket. A station in a state on the (b, c) plane with queue University, Aug. 2004. [2] V. Bharghavan, A. Demers, S. Shenker, and L. Zhang, “MACAW: Avalue i + 1 (i.e., in state (i + 1, b, c)) will transit to some state media access protocol for wireless LANs,” in ACM SIGCOMM, London,(i, 0, c) if it completes a successful transmission. If a station U.K, pp. 212–225, Oct. 1994.IEEE Communications Society / WCNC 2005 105 0-7803-8966-2/05/$20.00 © 2005 IEEE Authorized licensed use limited to: Annamalai University. Downloaded on July 28,2010 at 05:13:53 UTC from IEEE Xplore. Restrictions apply.
  • [3] G. Bianchi, “Performance analysis of the IEEE802.11 distributed co- [12] C. Intanagonwiwat, R. Govindan, and D. Estrin, “Directed diffusion: ordination function,” IEEE Journal in Selected Areas: Communication, a scalable and robust communication paradigm for sensor networks,” vol. 18, pp. 535–547, March 2000. in Sixth Annual International Conference on Mobile Computing and [4] L. Bononi, M. Conti, and L. Donatiello, “A distributed mechanism for Networking, Boston, MA, pp. 56–67, Aug. 2000. power saving in IEEE 802.11 wireless lans,” Mobile Networks and [13] P. Karn, “MACA- a new channel access method for packet radio,” in Applications, vol. 6, no. 3, pp. 211–222, 2001. ARRL/CRRL Amateur Radio 9th Computer Networking, pp. 134–140, [5] F. Cali, M. Conti, and Gregori, “IEEE802.11 wireless lan: Capacity 1990. analysis and protocol enhancement,” in INFOCOM ’98 Seventeenth [14] S. A. Khayam and H. Radha, “Markov-based modeling of wireless Annual Joint Conference of the IEEE Computer and Communications local area networks,” in Proceedings of the 6th international workshop Societies. Proceedings. IEEE, 1998. on Modeling analysis and simulation of wireless and mobile systems, [6] M. M. Carvalho and J. J. Garcia-Luna-Aceves, “Delay analysis of pp. 100–107, ACM Press, 2003. the IEEE802.11 in single-hop networks,” in 11th IEEE International [15] P.Chatzimisios and V.Vitsas, “Throughput and delay analysis of Conference on Network Protocols (ICNP’03), Atlanta, Georgia, USA, IEEE802.11 protocol,” in IEEE International Workshop on Network Nov. 2003. Appliances, (IWNA), Liverpool, U.K, Oct. 2002. [7] H. S. Chhaya and S. Gupta, “Performance modeling of asynchronous [16] J. W. Robinson and T. S. Randhawa, “Saturation throughput analysis of data transfer methods of IEEE802.11 mac protocol,” Wirel. Netw., vol. 3, IEEE802.11e enhanced distributed coordination function,” IEEE Journal no. 3, pp. 217–234, 1997. On Selected Areas In Communications, vol. 22, June 2004. [8] D. S. J. De Couto, D. Aguayo, B. A. Chambers, and R. Morris, “Perfor- [17] H. Takagi and L. Kleinrock, “Throughput analysis for persistent CSMA mance of multihop wireless networks: Shortest path is not enough,” in systems,” IEEE Transactions on Communications, vol. 33, pp. 627–638, Proceedings of the First Workshop on Hot Topics in Networks (HotNets- July 1985. I), (Princeton, New Jersey), ACM SIGCOMM, October 2002. [18] O. Tickoo and B. Sikdar, “Queueing analysis and delay mitigation [9] L. M. Feeney and M. Nilsson, “Investigating the energy consumption in IEEE 802.11 random access mac based wireless networks,” in of a wireless network interface in an ad hoc networking environment,” INFOCOM 2004, HongKong, China, March 2004. in IEEE INFOCOM, Anchorage, AK, USA, 2001. [19] E. Ziouva and T. Antonakopoulos, “CSMA/CA performance under high[10] L. Huang and T.-H. Lai, “On the scalability of IEEE802.11 ad hoc traffic conditions: Throughput and delay analysis,” Computer Commu- networks,” in Proceedings of the 3rd ACM international symposium on nications, pp. 313–321, 2002. Mobile ad hoc networking & computing, pp. 173–182, ACM Press, 2002.[11] IEEE Computer Society LAN MAN Standards Committee, “Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Spec- ifications, IEEE Std 802.11-1997,” 1997.IEEE Communications Society / WCNC 2005 106 0-7803-8966-2/05/$20.00 © 2005 IEEE Authorized licensed use limited to: Annamalai University. Downloaded on July 28,2010 at 05:13:53 UTC from IEEE Xplore. Restrictions apply.