IEEE TRANS. ON PARALLEL AND DISTRIBUTED SYSTEMS , VOL. X, NO. Y, 201Z 1

Analysis of CSMA/CA mechanism of IEEE802.15.6 under non-saturation regime

Saeed Rashwand, Jelena Misic, Senior Member, IEEE, and Vojislav B. Misic, Senior Member, IEEE

F

Abstract—We have developed an analytical model for a non-saturatedIEEE 802.15.6 wireless body area network (WBAN) operating under anerror-prone channel. The most suitable vehicle for improving networkperformance was found to be the choice of access phase lengths basedon traffic loads for different User Priorities (UPs). It was also found thatthe deployment of exclusive access phase (EAP) is not necessary in atypical WBAN; in fact, short exclusive and random access phases (EAPand RAP, respectively) lead to inefficient use of available bandwidth.We have also found that four user priorities (out of the eight available)typically suffice to achieve even the most stringent requirements forWBAN performance.

Index Terms—wireless body area networks (WBANs); IEEE 802.15.6;probabilistic analysis; access priorities

1 INTRODUCTION

Recent technological advances have led to the development oflow cost, low power sensing devices that allow continuoustracking of patient’s vital health variables through wirelessbody area networks (WBANs) [1], [2]. In this manner, health-care providers can react appropriately when necessary, andalso collect a rich history of patient health data.

WBANs have been built using proprietary MAC and PHYprotocols [3], [4], as well as existing WLAN/WPAN stan-dards such as IEEE 802.11, IEEE 802.15.4, ZigBee, andBluetooth [5], [6]. IEEE has recently published the IEEE802.15.6 standard [7] that combines low power operationand simplified CSMA-CA similar to IEEE 802.15.4, whilstproviding a prioritization mechanism, similar to IEEE 802.11e,which is well suited for transmission of healthcare data.However, in-depth performance analyses of IEEE 802.15.6CSMA/CA mechanism are still scarce. Initial research work inthis direction focused on the network operating in saturationregime which can be analyzed with a discrete-time Markovchain (DTMC) only, without queuing analysis of the nodebuffer (which is always full) and the associated extension to asemi-Markov chain with general distribution of time betweenstate transitions. In [8], [9], a numerical model was developedto evaluate the theoretical throughput and delay limits ofIEEE 802.15.6-based networks, however the model does notconsider user priorities and access phases of the standard, and

• S. Rashwand, J. Misic and V. B. Misic are with the Department of ComputerScience, Ryerson University, Toronto, Ontario.

assumes a collision-free network over an ideal channel. In [10],[11], authors studied the performance of the IEEE 802.15.6MAC under saturation condition for all traffic classes, whilethe impact of access phases lengths was studied in [12].

However, saturation leads to excessive delays, reduction inthroughput, and instability, if the buffers are assumed to be ofinfinite size. Thus, stable operation of a WBAN necessitatesthat its nodes operate well below saturation. In this case,CSMA/CA performance has been investigated in [13], [14] forIEEE 802.11, [15], [16], [17] for IEEE 802.11e, and [18] forIEEE 802.15.4, typically using a combination of queuing andMarkov chain analysis. However, these models are not directlyapplicable to the analysis of IEEE 802.15.6 networks dueto the differences in their respective implementations of theCSMA/CA mechanism. A simulation-only study of 802.15.6networks has been reported in [19], while [21] investigatesthe performance of IEEE 802.15.6-based networks under non-saturation condition, albeit without considering the impact ofqueuing on the departure process, which leads to oversimpli-fication. Our earlier work in [20] focused on the impact offading in physical layer and the interaction between physicaland MAC layer using a simplified MAC model of IEEE802.15.6 CSMA/CA mechanism. Models in [10], [11] do notconsider queuing analysis and probabilistic backoff durationsub-models integrated in a semi-Markov process framework.

In this work, we present a detailed analytical model forinvestigating the performance of the IEEE 802.15.6-basedWBANs. The model includes a Geo/G/1 queuing sub-model ofthe node buffer, and addresses all eight UPs (UPk, k = 0 . .7)and the first exclusive and random access phases (EAP1 andRAP1) under finite load and an error prone channel. Thismodel is solved iteratively to obtain the main performanceindicators of the network, such as mean waiting time andtransmission success probability. We have also developed acomplete simulation model, first of its kind, using OPNETWireless Modeler [23] to validate the analytical model.

The paper is organized as follows: Section 2 briefly intro-duces the IEEE 802.15.6 standard. In Section 3, we describethe major features of the analytical model. In Section 4, we an-alyze the performance of an IEEE 802.15.6-based WBAN forall UPs. Section 5 summarizes our findings and concludes thepaper. An Online Supplement presents the findings regardingthe network that uses RTS/CTS handshake.

IEEE TRANS. ON PARALLEL AND DISTRIBUTED SYSTEMS , VOL. X, NO. Y, 201Z 2

Fig. 1. Layout of access phases in a superframe.

2 IEEE 802.15.6 STANDARD

The IEEE 802.15.6 standard defines no less than eight userpriorities (UPs) which are differentiated based on the minimumand maximum contention windows, CWmin and CWmax,respectively, as shown in Table 1.

TABLE 1BAN User Priority Mapping

UP Traffic designation CWmin CWmax

0 Background (BK) 16 641 Best effort (BE) 16 322 Excellent effort (EE) 8 323 Controlled load (CL) 8 164 Video (VI) 4 165 Voice (VO) 4 86 Media data or network control 2 87 Emergency or medical event report 1 4

The time axis is divided into beacon periods (superframes)by the coordinator of the network. A superframe may includeexclusive access phases (EAP1 and EAP2), random accessphases (RAP1 and RAP2), type-I/II access phases, and con-tention access phase (CAP), in the order shown in Fig. 1; allphases except RAP1 may have a zero length. The EAP periodscan be only accessed for transmitting the UP7 frames whileRAP and CAP periods can be used by all UPs. To improvechannel utilization, a node with UP7 may treat the combinedEAP1 and RAP1 as a single EAP1, and the combined EAP2and RAP2 as a single EAP2. Type-I/II access phases areutilized by the hub for the polling mechanism; other phasesare contention-based access phases. In this work, we focus onEAP1 and RAP1 phases, and set the lengths of all the accessphases to zero.

A detailed description of the pertinent aspects of operationof an IEEE 802.15.6 network can be found in the standard [7].

3 ANALYTICAL MODEL

In this section, we outline the analytical model for investi-gating the performance of the CSMA mechanism of IEEE802.15.6. The analytical model consists of three inter-relatedsub-models: Markov chain sub-model, sub-model of backoffdurations, and queuing sub-model. The notation for the mostimportant parameters is shown in Table 2.

In all three models, we consider that a node of UPk, has asingle queue of user priority k. (In all subsequent discussions,we will assume that k = 0 . . 7, unless otherwise indicated.)The network, operating in 2.4GHz ISM band, is assumed to besingle hop including a hub as the coordinator and nk nodes ofUPk and therefore we ignore the hidden terminal problem. Weassume that there is only uplink traffic from the nodes to the

hub. Let λk denote the data frame arrival probability duringa CSMA slot for a node of UPk. We assume that during aslot at most one data frame arrives to the queue which is notan unrealistic assumption due to the small length of a CSMAslot. It is clear that the inter-arrival time Ik is geometricallydistributed with the mean of 1

λkslots. The lengths of EAP1 and

RAP1 in slots are denoted with eap and rap, respectively. Weassume that the size of the beacon is small, hence we ignoreit in our analytical model.

The control frames and headers are transmitted at 91.4kbpswhile we assume the payload of the data frames is transmittedat 971.6kbps. The size of the data frames for a node of UPkis denoted by lk (in slots) and lk,b (in bits), while ack andackb denote the size of an ACK frame in slots and in bits,respectively.

We assume an error-prone channel having the Bit Error Rate(BER) of ber, in which case σk = (1− ber)lk,b+ackb denotesthe probability that the data frame and the correspondingacknowledgement are transmitted without getting corrupted bythe noise.

3.1 Markov Chain Sub-model

We observe the system at moments of beginning of EAPs andRAPs, backoff decrements, and packet departures. In thesepoints, referred to as Markov points, the random process understudy has a Markov property. The time interval between twosuccessive Markov points is a random variable which affectsthe steady state probabilities. Therefore, the process underconsideration is a semi-Markov process [24]. For evaluatingthe semi Markov process we need DTMCs and distributionof time between the Markov points. The Markov chain sub-model is composed of a set of eight 3-dimensional DTMCsto compute the access probabilities of the UPs. The DTMCsare developed based on the backoff procedure of the IEEE802.15.6 CSMA mechanism. This sub-model also requires theprobability that the queue is empty after a data frame servicecompletion, which is calculated in the queuing sub-model.

The Markov Chain sub-model actually consists of eight3-dimensional DTMCs, one for each UP, to formulate themedium access probabilities of their respective UPs. A UPkMarkov chain represents the transitions among the backoffstates of a UPk node. All those Markov chains are inter-dependent since each of them must take into account notonly medium access probabilities, idle queue probabilities, andbackoff durations of the corresponding node, but also those ofother nodes in the network.

The access probability of a UPk node, τk, is calculatedby solving the set of DTMCs considering the CSMA slotsin which the medium is not set to be busy due to either a

IEEE TRANS. ON PARALLEL AND DISTRIBUTED SYSTEMS , VOL. X, NO. Y, 201Z 3

TABLE 2Important parameter notation (k denotes user priority for a node).

Parameter Description Parameter Descriptionk Index of UP σk Probability that neither a data frame nor its ACK is

corrupted by noisenk Number of nodes of UPk λk Data frame arrival probability during a CSMA slotτk Medium access probability gk Probability that medium is idle during backoff countdownLk,s Successful data frame transmission time Lk,c Unsuccessful data frame transmission timeLk,so Mean successful data frame transmission time of other

nodesLk,co Mean unsuccessful data frame transmission time of other

nodesηk Probability of successful medium access pk Probability that there is not enough time to complete a

data frame transmission from the current CSMA slot tothe end of the RAP1 period

π′k,0 Probability of an empty queue after serving a data frame pk,Idle Probability of being in the idle state in a CSMA slotpso,k Probability of a successful transmission by the other

nodespco,k Probability of an unsuccessful transmission by the other

nodesΦk(z) PGF for the duration of backoff process Πk(z) PGF of steady state probability distribution of number of

frames in the queue after completing data frame serviceωk Mean waiting time for a data frame ζk Mean response time for a data frameR Maximum retransmission limit

transmission on the medium or being in an inaccessible accessphase. The access probabilities vary in different time periods.We calculate the access probabilities in the CSMA slots inwhich the medium is accessible, using the DTMC for a nodeof UPk shown in Fig. 2. The Markov points are beginning ofthe CSMA slots in which the UPk node is allowed to transmit.Hence, the intervals between two successive Markov pointsin a DTMC may have different lengths. The Markov chainrepresents a random process with stationary distribution bk,i,j ,where k denotes the user priority of the node, i = 0 . . Rdenotes the backoff phase in the backoff procedure invoked bythe node, and j = 0 . . Wk,i denotes the value of the backoffcounter.

After developing all the DTMCs, the Markov chains aresolved as a single system to calculate the medium accessprobabilities.

Let f =∏7i=0 (1− τi)ni denote the probability that a

CSMA slot is idle during a RAP period. Then, the probabilitythat the medium remains idle during the backoff countdown ofa UPk node, k ≤ 6, is fk = f

1−τk . However, UP7 nodes are theonly ones allowed to transmit during EAP1 while all the UPscan access the medium during RAP1, and the probabilities thatthe medium remains idle in a CSMA slot during EAP1 andRAP1 are different. The corresponding probability for a nodeof UP7 is

f7 =XRf

(XE +XR)(1− τ7)+XE(1− τ7)n7−1

XE +XR(1)

where XE and XR denote the mean number of CSMA slotsin EAP1 and RAP1, respectively. Their initial values are setto XE = eap and XR = rap, and then updated in subsequentiterations via

X ′R =rap− L7,s

f +

7∑t=0

ntτtηtLt,s + (1− f −7∑t=0

ntτtηt)Lt,c

X ′E =eap

χ+ n7τ7ψσ7L7,s + (1− χ− n7τ7ψσ7)L7,c(2)

where χ = (1− τ7)n7 and ψ = (1− τ7)n7−1.

Fig. 2. Developed 3-dimensional DTMC for UPk.

In the equations above, Lk,x = lk +ack+ sifs denotes thetime needed for a data frame transmission, with subscripts sand c used to distinguish between successful and unsuccessfultransmissions, respectively. Furthermore, Lk,xo = lk,o+ack+sifs denotes the corresponding transmission time of othernodes (denoted with the subscript ‘o’) in the network, wherelk,o is the average size (in slots) of a data frame transmittedby those other nodes for a given UPk node.

Initial values for lk,o are set to

lk,o =

∑7u=0 nulu − lk∑7u=0 nu − 1

(3)

IEEE TRANS. ON PARALLEL AND DISTRIBUTED SYSTEMS , VOL. X, NO. Y, 201Z 4

and then updated in each subsequent iteration as

l′k,o =

∑7u=0 nuτulu − τklk∑7u=0 nuτu − τk

(4)

In the Markov chain shown in Fig. 2, ηk = fkσk representsthe probability of successful access to the medium for a UPknode when its backoff counter reaches zero.

However, if the remaining time during the current accessphase (EAP1 and RAP1 for a UP7 node, and RAP1 otherwise)is not long enough to complete a data frame transmission, thebackoff counter is locked; for a node without UP7 the countermust be kept locked during EAP1 as well.

The backoff counter remains locked until the moment whenthe node is allowed to transmit (RAP1 for UPk, k ≤ 6 or EAP1for UP7). Then, the probability that in a given CSMA slot thenode notices that there is not enough time during the currentaccess period to complete transmission, is

pk =

1

rap−Lk,s−Ck, k ≤ 6

1rap+eap−Lk,s−C7

, k = 7(5)

where Ck =CWk,min+CWk,max

4 approximates the meanbackoff value. The number of accessible CSMA slots in asuperframe for a given UP (the denominator in the equationabove) depends on the length of accessible access phases,frame transmission time, and the backoff counter at whichthe counter is paused.

The probability that the backoff counter of a node with UPkis decremented (i.e., not locked) upon reaching j = 1. .Wk,mk

is

gk,j = fk

(1− pk

1− f jk1− fk

)(6)

The slots in which the backoff counter is locked are stillconsidered as Markov points in order to calculate the accessprobability, which for a UPk node, is τk =

∑Ri=0 bk,i,0.

If the input probability to the zero-th backoff phase is

Yk =τkηk(1− π′

k,0) + pk,Idleβk

1− (1− ηk)R+1(1− π′k,0)

(7)

the solution of the Markov chain will give us

bk,i,j =(1− ηk)iYk(Wk,i − j + 1)

Wk,igk,j

bk,i,0 = (1− ηk)iYk (8)

The probability of being in ”Idle” state is

pk,Idle =τkηkπ

′

k,0

βk(1− (1− ηk)R+1)(9)

The probability that a data frame arrives to the queue duringthe time interval between two successive Markov points is

βk = fkpk(1− (1− λk)Lk) + fk(1− pk)λk

+ (1− fk)

(pk

Lk,so−1∑u=0

(1−pk)u(1−(1−λk)Lk+u+1)(10)

+ (1− pk)Lk,so(1− (1− λk)Lk,so)

)+

1

xk(11)

where xk = XR, k ≤ 6, and x7 = XR + XE . π′

k,0 is theprobability that the queue of the node with UPk is empty whena data frame is either successfully transmitted or dropped dueto an exceeded retry limit, which will be calculated in thequeuing sub-model.

In the last equation, the first component corresponds to thecase where at the first CSMA slot of the interval no node trans-mits on the medium but there is not enough time to complete adata frame transmission; this causes the backoff counter to lockuntil the next superframe. In this case, the length of the intervalis Lk slots. During RAP1 all the nodes in the network have tolock their backoff counters; while during EAP1, UP7 nodesare allowed to transmit or decrease their backoff counters.Consequently, Lk = eap + sifs + lk + ack, k ≤ 6, butL7 = sifs+ lk + ack.

The second component represents the case where the firstCSMA slot remains idle and there is enough time to completea data frame transmission. This results in the interval lengthof one slot.

The third component corresponds to the case where a nodestarts transmitting; 1

xkis the probability that medium access

is not possible in the first CSMA slot.We compute the sum of all stationary distribution bk,i,j as

R∑i=0

Wk,i∑j=0

bk,i,j = Yk

R∑i=0

(1− ηk)i(

1 +

Wk,i∑j=1

Wk,i − j + 1

Wk,igk,j

)(12)

The sum of probabilities must be one, which results in thefollowing set of equations for k = 0 . . 7:

1 = pk,Idle+Yk

R∑i=0

(1− ηk)i(

1 +

Wk,i∑j=1

Wk,i − j + 1

Wk,igk,j

)(13)

Hence, the Markov chain sub-model results in a set of eightequations with unknown τk and π′k,0, k = 0 . . 7, the latter setbeing obtained from the queuing sub-model.

3.2 Sub-model of Backoff DurationThe probability distribution of all the backoff phases and thetotal backoff duration before a successful access to the mediumor a data frame drop for all UPs are formulated by the back-off duration sub-model. The probability generating functions(PGFs) of the backoff durations are computed based on theaccess probabilities of the UPs which are introduced in theMarkov chain sub-model. The computed time distributions arethen substituted in the queueing and the Markov sub-modelsto calculate the transition probabilities in the correspondingsemi-Markov chains.

To calculate the length of backoff durations we extendthe DTMCs described above so that the beginning of everyCSMA slot represents a Markov point; as the result, any twoconsecutive Markov points are separated by a single slot. Tocompose the extended 4-dimensional DTMCs we replace thecomponent shown on top of Fig. 3 with the component shownin the 3-dimensional DTMCs (Fig. 2) for k = 0 . .7, i = 0 . .R,and j = 1 . . Wk,i.

Stationary distributions of bk,i,j,S,t, t = 1 . . Lk,so, andbk,i,j,C,t, t = 1 . . Lk,co, correspond to the time periods

IEEE TRANS. ON PARALLEL AND DISTRIBUTED SYSTEMS , VOL. X, NO. Y, 201Z 5

Fig. 3. Markov chain state extension for UPk

in which the data frame is (not) successfully transmitted,while stationary distribution of bk,i,j,Pz,t for t = 1 . . Lk + jcorresponds to the time period in which the node backoffcounter is locked on account of insufficient time to completea transmission. As before, subscripts s and c and their upper-case counterparts denote successful and unsuccessful mediumaccesses, respectively, while Pz is used to label the pausecaused by inability to access the medium.

We refer to pso,k and pco,k as the probabilities that themedium becomes busy due to a successful transmission byanother node and unsuccessful accesses by the other nodes inthe network, respectively, which are computed as:

pso,k =

7∑i=0

niτiηk1− τi

− τkηk1− τk

pco,k = 1− fk − pso,k(14)

where τiηk1−τi represents the probability of successful transmis-

sion by a UPi node. We note that pso,k + pco,k = 1 − fk isthe probability that the medium is busy due to an ongoingtransmission.

Let us denote the PGFs for the duration of a data frame (suc-cessful or unsuccessful) transmission as Stk(z) = Ctk(z) =zlk+ack+sifs. Every backoff period is composed of up to Rbackoff phases. The backoff phases are made of the timeperiods in which the backoff counter is decremented by oneuntil the counter reaches zero.

Let BS,k,j(z) (BC,k,j(z)) denote the PGFs for the timeinterval during which the backoff counter is locked due to suc-cessful (unsuccessful) medium access by other nodes, whichmay be calculated from the extended DTMCs, as follows:

BP,k,j(z) = zLk+j(fkz + Θk,j(z))

BS,k,j(z) = pkBP,k,j(z)1− (1− pk)Lk,sozLk,so

1− (1− pk)z(15)

+ (1− pk)Lk,sofkzLk,so + (1− pk)Lk,soΘk,j(z)

Also, let BP,k,j(z) denote the PGF for the time interval duringwhich the backoff counter is locked due to insufficient time

to complete a transmission, which may be calculated as

BC,k,j(z) = pkBP,k,j(z)1− (1− pk)Lk,cozLk,co

1− (1− pk)z(16)

+ (1− pk)Lk,cofkzLk,co + (1− pk)Lk,coΘk,j(z)

where Θk,j(z) = pso,kBS,k,j(z)+pco,kBC,k,j(z) is PGF of themean duration of backoff counter lock due to a transmissionon the medium, either successful or unsuccessful.

The values of BS,k,j(z), BC,k,j(z), and BP,k,j(z) can beobtained using the substitution method of solving system ofequations. We now calculate the PGF of the time intervalbetween the moment when the backoff counter of a node ofUPk becomes j and the moment when the backoff counterbecomes j− 1 at the i-th backoff phase. The PGF of the timeto decrease the backoff counter by one, where the counter isj , is given by

Φk,i,j(z) = pkBP,k,j(z) + (1− pk)(fkz + Θk,j(z)) (17)

for k = 0 . . 7, j = 1 . . Wk,i, and i = 0 . . R.Using the above PGFs we can write the PGF of i-th backoff

phase duration as

Φk,i(z) =

Wk,i∑j=1

j∏t=1

Φk,i,t(z)(ηk(Lk,spkz

Lk + 1− Lk,spk)

+ (1− ηk)(Lk,cpkzLk + 1− Lk,cpk)

)/Wk,i (18)

The PGF for the duration of backoff process for a UPk nodeis

Φk(z) =

mk∑i=0

(

i∏u=0

Φk,u(z))(1− ηk)i(zLk,c)iηk (19)

+

R∑i=mk+1

(

mk∏u=0

Φk,u(z))Φi−mk

k,mk(z)(1− ηk)i(zLk,c)iηk

+ (

mk∏u=0

Φk,u(z))ΦR−mk

k,mk(z)(1− ηk)R+1(zLk,c)R+1

From (19), we calculate the mean backoff time before themedium is successfully accessed or the data frame is droppedfor a UPk node as φk = d

dz Φk(z)|z=1.

3.3 Queuing Sub-modelTo calculate durations of idle periods and access probabilitiesof all UPs we need to know the queuing status of each UPin each CSMA slot. The backoff probability distributions ofall UPs, acquired by the backoff duration sub-model, are thenused to arrive at the empty queue probability, duration of anidle period, and the queue size of every UP.

We model the queue of each node in the WBAN using aGeo/G/1 system with vacations [26]. The inter-arrival time tothe queue for all UPs is geometrically distributed with meanof 1

λkslots. A data frame is serviced when it is successfully

transmitted or dropped due to an exceeded retry limit. Thetime interval in which the node performs the backoff processor is idle because of an empty queue is called a vacation. Weassume that upon a successful access to the medium each nodetransmits a single data frame.

IEEE TRANS. ON PARALLEL AND DISTRIBUTED SYSTEMS , VOL. X, NO. Y, 201Z 6

Let Bk(z) = Φk(z)Stk(z) denote the PGF of service time(in slots) of a data frame for a UPk node, which includes thebackoff process and successful transmission time. The meanservice time for a UPk data frame is bk = B

′

k(1).The offered load or traffic intensity of the queue is given

by ρk = λkbk. For stability condition the offered load mustbe less than one, in which case the offered load is equal to thecarried load or the server utilization in the Geo/G/1 system.

We consider a Markov chain Dk,n;n = 0, 1, . . ., whereDk,n is the number of data frames present in the system for aUPk node immediately after the service completion of the n-th data frame. We refer to Ak,n as the number of data framesthat arrive during the service of the n-th data frame of a UPknode. If the number of data frames of UPk that arrive duringan idle period is also denoted by αk, we have

Dk,n+1 =

αk +Ak,n+1 − 1, if Dk,n = 0Dk,n +Ak,n+1 − 1, otherwise (20)

The PGF for the number of data frames that arrive during ser-vice time is Ak(z) = Bk[Λk(z)], where Λk(z) = 1−λk+λkzis the PGF of the number of UPk data frames that arrive duringa single CSMA slot. Likewise, the PGF of the number of dataframes that arrive during idle time is αk(z) = Ik(Λk(z)),where Ik(z) is the PGF of the idle period duration – thedifference between the inter-arrival time and the frame servicetime. Since the UPk data frame arrival rate is λk, the numberof idle slots before an arrival follows a geometric distributionwith the PGF of λkz

1−(1−λk)z. However, the service time of the

last served data frame must be deducted from the inter-arrivaltime, hence

Ik(z) =λkz

Bk(z)(

1− (1− λk)z) (21)

Let the steady state probability of having u ≥ 0 frames inthe UPk queue after service completion be

πk,u = πk,0

u+1∑j=1

αk,jak,u−j+1 +

u+1∑j=1

πk,jak,u−j+1 (22)

where Ak(z) =

∞∑u=0

ak,uzu and αk(z) =

∞∑u=0

αk,uzu. The

PGF of the steady state probability distribution πk,u;u =0, 1, . . . for the queue of a UPk node is given by

Πk(z) =

∞∑u=0

πk,uzu

=

∞∑u=0

πk,0

u+1∑j=1

αk,jak,u−j+1zu

+

∞∑u=0

zuu+1∑j=1

πk,jak,u−j+1

= πk,0

∞∑j=1

αk,jzj∞∑

u=j−1ak,u−j+1z

u−j+1

+

∞∑j=1

πk,jzj−1

∞∑k=j−1

ak,u−j+1zu−j+1

which ultimately gives

Πk(z) =πk,0(1− Ik(Λk(z)))Bk(Λk(z))

Bk(Λk(z))− z(23)

The probability that the buffer is empty (after a successfuldata frame transmission or a data frame drop due to anexceeded retry limit) is determined from the normalizationcondition as

πk,0 =1− ρkE[αk]

=1− ρk

λkE[Ik(z)](24)

The probability πk,0 depends on the time interval between twosuccessive successful transmissions/data frame drops, whichin turn depends on the background traffic, the contentionamong the nodes, and the data frame arrival rates. The backoffduration of a node is a function of the backoff durations andframe arrival rates of other nodes in the network which mightbe at different backoff phases at different time slots. Hence,πk,0 represents an average probability.

From (13) and (24) we obtain a set of 16 equations whichenables us to calculate the 16 unknown variables of τk andπk,0, for k = 0 . . 7. To solve this model analytically, weemploy an iterative approach. We first solve the model basedon initial values for XE , XR, and f7. The next iterations areperformed using the computed values in equations (2) and (1)based on the data calculated in the previous iteration. Iterationt+1, t ≤ 0 is computed based on the outputs of iteration t andthe computed values in equations (2) and (1). The iterations arerepeated until the value difference between two successive XR

values is less than a predefined value (0.1), up to a maximumof 6 iterations.

The PGF of waiting time for a UPk data frame, defined asthe time interval from the moment when the frame arrives tothe moment when its successful transmission begins, is

Ωk(z) =

Θk(z)(θx+ 1− θ), k ≤ 6Θ7(z), otherwise (25)

where the term

Θk(z) =(1− ρk)(1− Ik(z))(1− Λk[Bk(z)])

E[Ik(z)]λk[1−Bk(z)](Λk[Bk(z)]− z)Φk(z)

(26)represents the PGF of the waiting time of the UPk dataframe until it reaches the top of the queue [26], while

θ =eap2

2(eap+ rap)corresponds to the PGF of the waiting

time from the moment when the data frame reaches to the topof the queue until the moment when its transmission backoffprocess begins; Φk(z) is the PGF of the backoff duration ofthe data frame.

By solving the set of sub-models for all UPs, we are ableto compute the mean waiting time of a UPk data frame as

ωk =

λkb

(2)k − λkbk

2(1− ρk)+E[Ik(z)(Ik(z)− 1)]

2E[Ik(z)]+ θ + φk, k ≤ 6

λkb(2)k − λkbk

2(1− ρk)+E[Ik(z)(Ik(z)− 1)]

2E[Ik(z)]+ φk, k = 7

(27)where b(2)k = bk + d2

dz2 Bk(z)|z=1. Finally, the mean responsetime, defined as the time interval from frame arrival to thetime it leaves the system, is ζk = ωk + (lk + sifs+ ack).

IEEE TRANS. ON PARALLEL AND DISTRIBUTED SYSTEMS , VOL. X, NO. Y, 201Z 7

TABLE 3Experimental setup: traffic parameters.

UP data stream number of nodes traffic load (p/s) packet size (Bytes) sampling rate (Hz) sample size (bits)7 ECG 1 2 150 200 12

EEG 1 2 150 200 126 ECG 2 2 150 200 125 EEG 1 2 150 200 12

Blood Pressure 1 2 150 200 124 Glucose 1 1 50 50 16

Oxygen Saturation 1 1 50 50 16Temperature 1 0.25 20 5 8

Respiration Rate 1 0.25 20 5 83 Physical Activity 4 1 50 50 162 EMG 1 4 500 1000 161 ECG 5 0.8 375 200 120 EEG 8 0.5 600 200 12

4 PERFORMANCE EVALUATION

We have used Maple 13 [27] to solve the analytical modeloutlined above and to calculate the performance descriptors ofthe network. Moreover, we have developed a simulation modelof the IEEE 802.15.6 in OPNET [23]; this model implementsthe complete functionality of IEEE 802.15.6 as stipulated bythe standard.

The wireless healthcare network which is used for per-formance evaluation of the IEEE 802.15.6 standard undernon-saturation regime consists of a hub and 28 nodes. Thenodes include ten channels for Electroencephalogram (EEG),eight channels for Electrocardiogram (ECG), one blood pres-sure sensor, one glucose monitoring node, one blood oxygensaturation monitoring sensor (pulse oximeter), four physicalactivity monitoring sensors, one channel for Electromyogram(EMG), one body temperature sensor, and one respiration ratemonitoring node.

The nodes are categorized into eight UPs, as shown inTable 3 which uses the parameter values from [28]. We assumethat all the nodes in a given UP have equal traffic load andpayload size.

While this setup may be somewhat arbitrary from theperspective of a healthcare worker, it is fairly representativeof a real scenario in which an IEEE 802.15.6 WBAN mightbe employed.

We set the differentiation parameters of CWk,min andCWk,max for all the nodes according to the standard, as shownin Table 1. The retry limit is set to R = 7 for all the UPs.

4.1 Medium access

Fig. 4 show mean waiting time for select UPs in the scenarioRAP1 length varies while the EAP1 length is constant. Asbefore, some UPs are omitted for clarity, and analytical andsimulation results are shown with lines and crosses, respec-tively.

Results for the scenario in which the length of EAP1 variesfrom 0.05 second to 0.12 second while the length of RAP1 isfixed at 0.3 second are shown in Fig. 5 (as before, simulationresults are omitted for clarity). The results confirm that in-creasing the length of EAP1, under non-saturation condition,increases the mean waiting time of the data frames, specifically

UP0

UP2

UP4

UP6

UP7

Fig. 4. Mean waiting time: all UPs, length of EAP1 is 0.05sec.

the data frames that do not belong to the highest priority trafficclass UP7.

4.2 Effectiveness of user priorities

We have also investigated the effectiveness of user prioritiesprovided by the IEEE 802.15.6 standard. We consider anetwork with medium load, consisting of a number of nodeswith identical traffic loads of two packets per second with thepacket payload of 150B, and vary the number of nodes whilstkeeping other parameters unchanged.

In the first scenario, we consider a WBAN with four nodesin each of the eight UPs. Fig. 6(a) show the mean waitingtime for select UPS. As can be seen, the difference betweenall categories except UP7 (the highest one) is rather small,despite quite different contention window sizes. As before,lines denote analytical results, while crosses denote simulationresults.

This conclusion is confirmed by the second experiment inwhich the WBAN uses only four UPs consisting of eight nodeseach. Comparing the results in Fig. 6(a) and Fig. 6(b), we may

IEEE TRANS. ON PARALLEL AND DISTRIBUTED SYSTEMS , VOL. X, NO. Y, 201Z 8

UP0

UP2

UP4

UP6

UP7

(a) Mean Waiting Time

UP0

UP4

UP2

UP6

UP7

(b) Successful Transmission Probability

Fig. 5. Network performance: all UPs, length of RAP1 is 0.3 sec.

UP0

UP4

UP2

UP6

UP7

(a) All UPs with four nodes each.

UP0

UP4

UP2

UP6

(b) UPs 0, 2, 4, and 6, with 8 nodes each.

Fig. 6. Mean waiting time; length of RAP1 is 0.3 sec.

conclude that four priorities are more than enough, and thatmerging together priorities 0 and 1, 2 and 3, and 4 and 5,respectively, would not change the results in any noticeableway.

Thus, it seems safe to conclude that having four prioritiesinstead of the original eight would give very similar resultsin terms of performance, while simplifying considerably thedesign of the firmware and software, as well as reducing thecomplexity of the hardware.

We have also analyzed network performance in the casewhere the network has only two UPs with 16 nodes each:one group of nodes with UP0, and another with either UP6 orUP7; the results are shown in Fig. 7. As can be seen, assigning

traffic to the highest priority traffic class in case of non-zeroEAP access phase, leads to better performance results for allnodes.

5 SUMMARY AND CONCLUSION

The results shown above allow the following observations tobe made.

Short EAP and RAP phases lead to inefficient use of WBANbandwidth; in particular, slots at the end of EAP phasesare wasted since there is not enough time for completing aframe transmission. In addition, frame collision probabilitynoticeably increases at the beginning of a RAP phase becausethe nodes at all priorities below UP7 have just unlocked

IEEE TRANS. ON PARALLEL AND DISTRIBUTED SYSTEMS , VOL. X, NO. Y, 201Z 9

UP0

UP6

(a) UPs 0 and 6, 16 nodes each.

UP0

UP7

(b) UPs 0 and 7, 16 nodes each.

Fig. 7. Mean waiting time; length of RAP1 is 0.3 sec.

their backoff counter and are, thus, highly likely to transmita data frame. Since these nodes had to pause their backoffcounter form the previous RAP and phase, and given the smallcontention window sizes of WBAN UPs, the number of suchnodes is high, and degradation of performance is very likely.

In fact, EAP phases are largely unnecessary as they providelittle performance improvement for UP7 nodes, which arealready highly prioritized by the small contention windowsizes, while considerably decreasing the performance for allother UPs. As the result, overall WBAN performance isdegraded unless the network traffic is high – which is nota typical WBAN scenario.

Based on the current contention window sizes for differentpriority classes, deployment of eight UPs is unnecessary. Ourresults indicate that traffic classes 0 and 1, 2 and 3, and 4 and 5,respectively, have almost equal performance measures. There-fore, satisfactory performance could be obtained with only fourpriority classes, namely UP0, UP2, UP4, and UP7 for WBANs,which could reduce the development and manufacturing costsof IEEE 802.15.6-compliant hardware and software.

Finally, the IEEE 802.15.6 CSMA/CA mechanism is notvery efficient in utilizing the medium. In particular, smallcontention window sizes for all UPs lead to early saturationby increasing the collision probability under medium to highnetwork traffic volume. Increasing the CW size would de-crease the collision probability and, thus, improve improvethe WBAN performance at the expense of higher energyconsumption.

Finally, the results of the analysis performed in the net-work that uses RTS/CTS handshake (presented in the OnlineSupplement) indicate that deploying the RTS/CTS mechanismdegrades the network performance, esp. when the WBANoperates well below the saturation regime and data frames ofsmall to medium size are used.

It remains to be seen whether these problem areas will

preclude wider utilization of the IEEE 802.15.6 standard forthe development of WBANs.

REFERENCES[1] M. R. Yuce, “Implementation of wireless body area networks for

healthcare systems,” Sensors and Actuators A: Physical, vol. 162, pp.116–129, Jul. 2010.

[2] H. Chen, W. Wu, and J. Lee, “A WBAN-based real-time electroen-cephalogram monitoring system: Design and implementation,” Journalof Medical Systems, vol. 34, pp. 303–311, 2010.

[3] Y.-S. Seo, D.-Y. Kim, J. Cho, and B. Lee, “OCDP: A WBAN MACprotocol for contention-based medical and CE applications,” in Proc. the4th International Conference on Ubiquitous Information Managementand Communication, Suwon, Korea, Jan. 2010.

[4] K. S. Kwak, M. A. Ameen, D. Kwak, C. Lee, and H. Lee, “Astudy on proposed IEEE 802.15 WBAN MAC protocols,” in Proc.the 9th international conference on Communications and informationtechnologies, Incheon, Korea, Sep. 2009, pp. 834–840.

[5] F. E. H. Tay, D. G. Guoa, L. Xua, M. N. Nyana, and K. L. Yap,“MEMSWear-biomonitoring system for remote vital signs monitoring,”Journal of the Franklin Institute, vol. 346, pp. 531–542, Aug. 2009.

[6] D. Albu, J. Lukkien, and R. Verhoeven, “On-node processing of ECGsignals,” in Proceedings of the 7th IEEE conference on Consumercommunications and networking conference, Las Vegas, Nevada, USA,Jan. 2010, pp. 1165–1169.

[7] Wireless Body Area Networks Standard, IEEE Std. 802.15.6, Feb. 2012.[8] S. Ullah and K. S. Kwak, “Throughput and delay limits of IEEE

802.15.6,” in Proc. IEEE WCNC, Cancun, Mexico, Mar. 2011.[9] S. Ullah, M. Chen, and K. S. Kwak, “Throughput and delay analysis of

IEEE 802.15.6-based CSMA/CA protocol,” Journal of Medical Systems,vol. 36, p. 3875–3891, Jul. 2012.

[10] S. Rashwand, J. Misic, and H. Khazaei, “IEEE 802.15.6 under satura-tion: Some problems to be expected,” Journal of Communications andNetworks, vol. 13, pp. 142–149, 2011.

[11] S. Rashwand, J. Misic, and H. Khazaei, “Performance analysis ofIEEE 802.15.6 under saturation condition and error-prone channel,” inProc. the IEEE Wireless Communications and Networking Conference(WCNC’11), Cancun, Mexico, Mar. 2011, pp. 475–480.

[12] S. Rashwand and J. Misic, “Effects of access phases lengths on perfor-mance of IEEE 802.15.6 CSMA/CA,” Computer Networks, Vol. 56, pp.2832–2846, 2012.

[13] F. Daneshgaran, M. Laddomada, F. Mesiti, and M. Mondin, “Unsaturatedthroughput analysis of IEEE 802.11 in presence of non ideal trans-mission channel and capture effects,” IEEE Transactions on WirelessCommunications, vol. 7, pp. 1276–1286, Apr. 2008.

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[14] C.-S. Hwang and J. M. Cioffi, “Opportunistic CSMA/CA for achievingmulti-user diversity in wireless LAN,” IEEE Transactions on WirelessCommunications, vol. 8, pp. 2972–2982, Jun. 2009.

[15] X. Ling, K. H. Liu, Y. Cheng, X. Shen, and J. W. Mark, “A novelperformance model for distributed prioritized MAC protocols,” in Proc.the IEEE Global Telecommunications Conference (Globecom’07), Wash-ington, DC, US., Nov. 2007, pp. 4692–4696.

[16] O. M. F. Abu-Sharkh and A. H. Tewfik, “Toward accurate modelingof the IEEE 802.11e EDCA under finite load and error-prone channel,”IEEE Transactions on Wireless Communications, vol. 7, pp. 2560–2570,2008.

[17] I. Inan, F. Keceli, and E. Ayanoglu, “Analysis of the 802.11e enhanceddistributed channel access function,” IEEE Transactions on Communi-cations, vol. 57, pp. 1753–1764, Jun. 2009.

[18] J. Misic and V. Misic. Wireless Personal Area Networks Performance,Interconnections and Security with IEEE 802.15.4. Wiley Publishing,Inc., first edition, 2008.

[19] F. Martelli, C. Buratti, and R. Verdone, “On the performance of an IEEE802.15.6 wireless body area network,” in Proc. 11th European WirelessConference, Vienna, Austria, Apr. 2011, pp. 1–6.

[20] S. Rashwand, J. Misic, and V. B. Misic, “MAC performance modeling ofIEEE 802.15.6-based WBANs over rician-faded channels,” in Proc. theIEEE International Conference on Communications (ICC’12), Ottawa,Canada, Jun. 2012, pp. 5462–5467.

[21] B. H. Jung, R. U. Akbar, and D. K. Sung, “Throughput, energyconsumption, and energy efficiency of IEEE 802.15.6 body area net-work (BAN) MAC protocol,” in IEEE 23rd International Symposiumon Personal, Indoor and Mobile Radio Communications - (PIMRC),Sydney, Australia, Sep. 2012, pp. 584–589.

[22] S. Rashwand and J. Misic, “Performance evaluation of IEEE 802.15.6under non-saturation condition,” in Proc. the IEEE Global Telecommu-nications Conference (Globecom’11), Houston, Texas, US., Dec. 2011,pp. 1–6.

[23] Opnet Modeler, Opnet Technologies, Inc. Last accessed on September2012. Bethesda, MD. [Online]. Available: http://www.opnet.com

[24] D. P. Heyman and M. J. Sobel, Stochastic Models in OperationsResearch, Vol. I: Stochastic Processes and Operating Characteristics.New York: Dover Publications, INC., 2005.

[25] H. Takagi, Queueing Analysis; a Foundation of Performance Evaluation- Volume 1: Vacation and Priority Systems. NORTH HOLLAND, 1991.

[26] H. Takagi, Queueing analysis; a foundation of performance evaluation- volume 3: discrete-time systems. NORTH HOLLAND, 1991.

[27] Maple 13, Maplesoft, Inc. Last accessed on September 2012. Waterloo,Canada. [Online]. Available: http://www.maplesoft.com

[28] J. D. Bronzino, The Biomedical Engineering Handbook - Third Edition.Volume 2: Biomedical Engineering Fundamentals. Taylor & FrancisGroup, LLC, 2006.

Saeed Rashwand received his PhD degree in2012 from University of Manitoba, Winnipeg,MB, Canada, and his MSc degree in 2008 fromAmirkabir University of Technology, Iran, bothin Computer Science. His current research in-terests include performance evaluation, design,and implementation of wireless body area net-works, wireless sensor networks, mobile ad-hocnetworks, and wireless local area networks aswell as cloud computing.

Jelena Misic (M91, SM08) Professor of Com-puter Science at Ryerson University in Toronto,Ontario, Canada. She has published over 90papers in archival journals and more than 120papers at international conferences in the areasof wireless networks, in particular wireless per-sonal area network and wireless sensor networkprotocols, performance evaluation, and security.She serves on editorial boards of IEEE Trans-actions on Vehicular Technology, Computer Net-works, Ad hoc Networks, Security and Commu-

nication Networks, Ad Hoc & Sensor Wireless Networks, Int. Journalof Sensor Networks, and Int. Journal of Telemedicine and Applications.She is a Senior Member of IEEE and Member of ACM.

Vojislav B. Misic is Professor of ComputerScience at Ryerson University in Toronto, On-tario, Canada. He received his PhD in Com-puter Science from University of Belgrade, Ser-bia, in 1993. His research interests include per-formance evaluation of wireless networks andsystems and software engineering. He has au-thored or co-authored six books, 20 book chap-ters, and over 200 papers in archival journalsand at prestigious international conferences. Heis a Senior Member of IEEE, and member of

ACM and AIS.

IEEE TRANS. ON PARALLEL AND DISTRIBUTED SYSTEMS , VOL. X, NO. Y, 201Z 1

Analysis of CSMA/CA mechanism of IEEE802.15.6 under non-saturation regime:

the Impact of RTS/CTS HandshakeSaeed Rashwand, Jelena Misic, Senior Member, IEEE, and Vojislav B. Misic, Senior Member, IEEE

F

The IEEE 802.15.6 standard does not mention the use ofRTS/CTS handshake, but neither does it prohibit it. Therefore,it may be of interest to see whether its use would giveany benefits. This Online supplement presents the resultsof performance evaluation of our model in case RTS/CTShandshake is used. We first present the differences in theanalytical model, and then show the results of performanceevaluation.

1 ANALYTICAL MODEL: CHANGES NEEDED TOACCOMMODATE RTS/CTS HANDSHAKEIn this section, we will briefly outline the changes that needto be introduced in the analytical model to accommodate theRTS/CTS handshake. Let rts and cts represent the size of RTSand CTS (in slots) and rtsb and ctsb (in bits), respectively, andlet δ = (1 − ber)rtsb+ctsb denote the probability that neitherRTS nor CTS is corrupted by noise. All other variables arethe same as in the main paper.

In Equation (2), the mean number of CSMA slots in theEAP1 period is updated via

X ′E =

eap

χ+ n7τ7ψδL7,s + (1 − χ− n7τ7ψδ)L7,c(1)

where the transmission time for a UPk data frame in slots isLk,s = rts+cts+lk+ack+3sifs and Lk,c = rts+cts+sifsfor successful and unsucccesful transmission, respectively. Thecorresponding transmission times of data frames sent by othernodes are Lk,so = rts+ cts+ lk,o +ack+ 3sifs and Lk,co =rts+ cts+ sifs, respectively.

In the Markov chain in Fig. 2 in the main file, ηk = fkδrepresents the probability of successful access to the mediumfor a UPk node when its backoff counter reaches zero.

The probability that the queue of the node with UPk isempty when a data frame is either successfully transmitted ordropped due to an exceeded retry limit is πk,0 = π

′

k,0σk; it iscalculated in the queuing sub-model.

The PGFs for the duration of a successful and unsuc-cessful data frame transmission, failure being due to a RTS

• S. Rashwand, J. Misic and V. B. Misic are with the Department of ComputerScience, Ryerson University, Toronto, Ontario.

UP0

UP2

UP4

UP6

UP7

Fig. 1. Mean waiting time in the network with all UPs;length of EAP1 is 0.05 sec.

collision, by Stk(z) = zrts+cts+lk+ack+3sifs and Ctk(z) =zrts+cts+sifs, respectively.

The PGF for the service time of a data frame is given by

Bk(z) =σkΦk(z)Stk(z)

1 − (1 − σk)Φk(z)Stk(z).

The PGF of waiting time for a UPk data frame, as perequation (25), is modified by multiplying Θk(z) from equa-tion (26) by (rts + 2sifs + cts)z + 1 − rts + 2sifs + cts,which corresponds to the PGF for the duration of the RTS/CTShandshake.

2 PERFORMANCE OF MEDIUM ACCESS WITHRTS/CTSOnce those changes are incorporated in the analytical model,it is solved in the same manner as the one described in themain paper. The simulation and analytical results for the meanwaiting time of data frames for all user priorities where theRAP1 length varies while the length of EAP1 is constant areshown in Fig. 1. As before, we have not shown the resultsfor all user priorities in order to improve clarity; the priorities

IEEE TRANS. ON PARALLEL AND DISTRIBUTED SYSTEMS , VOL. X, NO. Y, 201Z 2

UP0

UP2

UP4

UP6

UP7

(a) Mean waiting time.

UP0

UP4

UP2

UP6

UP7

(b) Probability of successful transmission.

Fig. 2. Performance of the network with all UPs; length of RAP1 is 0.3 sec.

shown are labeled in the diagrams. Lines denote analyticalresults, while crosses denote simulation results.

As can be seen, increasing the length of RAP1 while EAP1is kept constant improves (i.e., reduces) the frame delay for allUPs, since longer RAP periods lead to decrease of collisionprobability after an EAP period. In addition, having short EAPand RAP periods increases the number of CSMA slots inwhich the medium cannot be accessed by the nodes becausethere is not enough time to complete a data frame transactionduring the current access phase.

In the second scenario, the length of EAP1 varies from 0.05second to 0.12 second while the length of RAP1 is set to 0.3second; the results are shown in Fig. 2, again with lines andcrosses denoting analytical and simulation results, respectively.

Comparing the graphs in Figs. 1 and 2 above with theircounterparts, Figs. 4 and 5 in the main paper, we see thatthe behavior of the network does not depend on the useof RTS/CTS – in qualitative terms, that is. With or withoutRTS/CTS, mean waiting time and transmission success prob-ability of data frames increase for all UPs when the length ofEAP1 increases. Namely, having a long EAP period essentiallywastes network resources because the traffic load of the highestpriority traffic category, UP7, is low. However, during the RAPperiods the traffic congestion increases because other nodeshave shorter time periods for transmission. The conclusion isthat judicious choice of RAP and EAP periods according to thetraffic loads of the UPs should lead to substantial performanceimprovements.

However, the difference in quantitative terms is quite notice-able, as the performance of the WBAN improves dramaticallywhen the RTS/CTS mechanism is not used, esp. in termsof waiting time which is about 50% lower when RTS/CTSis not used. The probability of successful transmission isonly slightly higher when RTS/CTS is used, probably becausethe original values were high enough, so there’s not muchimprovement there to justify the use of RTS/CTS handshake.

We may conclude that the use of RTS/CTS is counterpro-ductive in the scenario shown, where small to moderate dataframe sizes of up to the maximum payload size of 600 Bare used. This setup was selected for use in an environmentswhere signal to noise ratio (SNR) is low, in which case largerpayloads are more likely to result in frequent frame errors andretransmissions.

However, for a WBAN in an environment with sufficientlyhigh SNR, larger frame sizes may be used. In this case, usingthe RTS/CTS mechanism improves the performance of thenetwork because the RTS/CTS avoids many of the errors,and its transmission time is considerably smaller than thetransmission time of the data frame itself.

3 SUMMARY

Our results indicate that, for an unsaturated WBAN with asmall to moderate data frame sizes, deploying the RTS/CTSmechanism degrades the network performance compared to thecase where the nodes immediately transmit their data framesupon a successful medium access. Since the control frames arenot much smaller in size compared to the data frames, thereseems to be no benefit in using RTS/CTS; on the contrary, itactually degrades network performance.