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Ad Hoc Networks xxx (2005) xxx–xxx

www.elsevier.com/locate/adhoc

Performance evaluation of single and multi-channel actorto actor communication for wireless sensor actor networks

Dimitris Vassis a, George Kormentzas a,*, Charalabos Skianis b

a Department of Information and Communication Systems Engineering, University of the Aegean, GR-83200 Karlovassi, Greeceb National Centre for Scientific Research �Demokritos�, Institute of Informatics and Telecommunications,

15310 Aghia Paraskevi Attikis, POB 60228, Athens, Greece

Received 20 July 2004; received in revised form 15 February 2005; accepted 25 February 2005

Abstract

The paper discusses two distributed actor to actor communication schemes (a single channel one and a multi-channelone) for wireless sensor actor networks (WSANs). The performance of these two schemes is evaluated through analysisand simulation. The simulation results show that the whole analysis is fairly accurate. It is further proven that the multi-channel scheme has better performance than the single channel one. The key feature of the multi-channel scheme is theseparation of the single hop from the multi-hop traffic.� 2005 Elsevier B.V. All rights reserved.

Keywords: AAC; Analysis; Multi-channel; Simulation; WSANs

1. Introduction

The low cost deployment of wireless sensor net-works (WSNs) together with the recent advances inwireless technology and electronics brought a sig-nificant increment in the number, the scale andthe complexity of existing WSN settings [1]. Theemerging need for WSNs to obtain a hierarchical

1570-8705/$ - see front matter � 2005 Elsevier B.V. All rights reservdoi:10.1016/j.adhoc.2005.02.001

* Corresponding author. Tel.: +30 2273082235.E-mail addresses: divas@aegean.gr (D. Vassis), gkorm@

aegean.gr (G. Kormentzas), skianis@iit.demokritos.gr (C.Skianis).

structure that could guarantee rapid response tosensors input introduced a new type of WSNs re-ferred as wireless sensor actor (or actuator) net-

works (WSANs) [2]. Apart from sensors that arepassive elements, WSANs also incorporate entitiesreferred as Actors that are active elements withhigher processing and communication capabilitiesand less constrained energy resources than sensors.Based on input by the sensors, actors can takedecisions and perform actions. Indicative examplesof actuation would be re-charging or replacingsmall nodes whose energy has been depleted, imag-ers that can take photos or video when activated

ed.

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by sensors, sprinklers that can sprinkle water inbadly parched areas, among others.Although WSANs is a newly addressed area

with a lot of open issues to be considered, certainprogress is noted in the form of innovative re-search works. In [2] a thorough survey outlines alot of open issues regarding WSANs. In [3] theauthors, using the anycast communication para-digm, exploit the capabilities of actors to reducethe communication burden on sensors. [4] and [7]discuss on algorithms that automatically deter-mine the relative position of sensors and actorsin a WSAN. Work on [5] presents an overloadmanagement policy that enables nodes to opti-mally drop out packets in order to avoid networkcongestion in a WSAN. [6] presents a way to com-bine multiple input/output devices in a distributedarray of audio/video sensors and actors. [8] inturn, provides a survey on smart wireless autono-

mous microsystems (SWAMs) for WSANs, whichare systems that capture data of several sensorsand control actors. The authors of [9] deployagent-based software to manage distributed energyresources in WSANs. Finally, certain implementa-tion aspects are outlined in [10], which presents atestbed for WSANs, while the authors of [11] pres-ent an already deployed WSAN that facilitatesoperations in plant nursery.Alongside the increased research activities in

the area of WSANs, the paper focuses on the actorto actor communication (AAC) concerning the dis-tributed communication between actors of such anetwork. In a distributed infrastructure no centralcoordination exists and all actors exchange infor-mation in a distributed manner performing allthe decision actions themselves. Under the AACsetting, there are a few actor nodes with higherpower capabilities than the sensor ones, resem-bling communication in a wireless ad hoc network[2]. In this line of thought, the paper deals with theemployment of IEEE 802.11-based [12] communi-cation schemes for AAC.Specifically, the paper discusses two IEEE

802.11-based AAC schemes. Firstly, a single chan-nel communication scheme is outlined and its per-formance is evaluated. Secondly, exploiting thefact that for improving the performance of wirelesssystems, multi-channel schemes have been exten-

sively used in ad hoc networks [13–16] as an alter-native to data rate adaptation schemes [21,24], thepaper also introduces a multi-channel AACscheme. The key feature of the multi-channelscheme is the adoption of two channels that sepa-rate the single hop from the multi-hop traffic. Forthe evaluation of both schemes, appropriate quan-titative analytical metrics are defined and validatedby simulation. Using the analytical metrics, it isshown that the multi-channel scheme with channeldata rates R1 and R2 exhibits better performancethan a single channel with data rate greater thanR1 + R2. Note that as we construct our analyti-cally approximated metrics based on some generaldistributions, these metrics are valid not only forWSANs but also in a more generic context forwireless ad hoc networks. However, for the valida-tion of the analytical metrics and for simplifyingour analysis, we take into account only the specialfeatures of WSANs.The rest of the paper is organized as follows:

The single channel and the multi-channel distrib-uted AAC schemes are introduced in Sections 2and 3, respectively. Moreover, the AAC schemesare modeled and validated through simulation.In all cases the OPNET [20] simulation platformis used for validation purposes. In Section 4 theperformance of the two schemes is evaluated usingcertain indicative metrics. Finally, Section 5 con-cludes the paper.

2. Modeling an IEEE 802.11-based single channel

distributed AAC scheme for a WSAN

Fig. 1 depicts a conventional WSAN setting,that consists of a large number of sensors (denotedby dots) and a smaller number of actors (denotedby squares). Sensors are grouped to clusters with asingle responsible actor. Each actor undertakes thewhole sensors to actor communication, such thateach sensor belongs only to one cluster and com-municates with the responsible actor. For simplic-ity reasons, all network nodes (sensors and actors)are considered static. Moreover, each actor hastwo radio interfaces, one for communicating withsensors of the same cluster and another one forcommunicating with the neighboring actors. While

Fig. 1. A conventional WSAN setting.

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the small circle in the upper left side of Fig. 1 indi-cates the cluster range, also regarded as the trans-mission range of a sensor (and an actor) in thesensor–actor communication, the big circle depictsthe actor transmission range in the actor–actorcommunication.For the performance evaluation of the pre-

sented distributed AAC scheme, we focus mainlyon the indicative metric of packet delay. Particu-larly, two instances of the packet delay metricare used: the media access delay that is the timefrom the beginning of a packet transmission froman actor till its successful reception from the nextactor and the overall system delay that is the ex-pected time that a random packet spends goingfrom the source actor to the destination actor viaa number of intermediate actors. In order to derivethe media access delay metric, initially the hiddenterminal problem is ignored only to be consideredat a later stage in order to improve the estimationof the metric.Let us consider again the WSAN setting of Fig.

1 and concentrate on the actor–actor communica-tion part. The actors are static and uniformlydistributed across the network with density q.Consider the wireless channel data rate, R andthe transmission range of each actor, r. Note that,r stands only for the actor–actor communication,as for the sensor–actor communication the trans-mission range is smaller. Assume that the traffic

sent by each actor to all the other actors is gener-ally distributed with a mean value of k packets persecond, while the packet payload size is also gener-ally distributed with a mean value of P bits. For aparticular actor, the generated traffic is collectedfrom the sensors within his cluster. Note that k re-fers only to the traffic produced by an actor andnot to any traffic relayed on behalf of other actors.Finally, in the multi-hop communication settingwe examine, the number of hops each packet tra-verses before reaching its destination is also de-scribed by a general distribution with h, as themean number of hops.At this point, the generated network traffic

should be estimated considering also the routingand relay overheads. For the routing overhead,the simulation scenarios considered AODV asthe routing protocol, based on its better perfor-mance in an IEEE 802.11 wireless environmentcontext (e.g. [23]). The routing traffic load appearsto be less than 1 Kb/s per node that is negligiblewhen compared to the network capacity and al-lows for the routing overhead to be ignored [16].Note that the routing overhead would still remainsmall when compared to the network capacityeven in cases where other than AODV routingmechanisms were considered. Regarding the relayoverhead, since the actors� density remains thesame in all the network area, it can be argued thatall actors are equiprobable to act as relay nodesand consequently, relay on average the sameamount of traffic. Although this is not generallyvalid for a single packet, it is still applicable whenone considers large scale networks with uniformlydistributed actors across the network area, gener-ating traffic where edge effects can be ignored.

2.1. Utilization analysis

Lets now consider the actor A of Fig. 1. ActorA forwards to all other actors on average k packetsper second (traffic produced by sensors within hiscluster). Since each packet needs h hops to reachthe destination actor, the expected traffic relatedto actor A, generated in the network, is on averagek Æh packets per second. Assuming in addition thatthe network is consisted of M actors, the totalexpected traffic generated in the network from all

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actors is M Æk Æh. Out of the total generated traffic,a part equal toMk(h � 1) is relayed from the inter-mediate hops to other actors. Note that, for a largeM, each actor would have approximately the sameprobability to act as a relay node. At the end, anyactor in the network forwards to other actors bothhis own generated traffic and any traffic relayedfrom other actors, which on average is given as

ka ¼ k þ 1

MMkðh� 1Þ ¼ kh: ð1Þ

Consider now a cell that contains all the actorsinside the coverage area of the actor A (see Fig. 1).Profoundly, the total expected traffic in the circu-lar area of the cell is

g ¼ kaqpr2: ð2ÞInitially this cell is considered in isolation and

the hidden terminal problem is ignored, with allactors being in line of sight with each other (at alater stage the above considerations will bedropped). The utilization of the cell is simply [17]:

ULOS ¼S

Bþ I; ð3Þ

where S is the average transmission time of thepacket payload in the wireless medium, B is theaverage busy period of the medium and I is onaverage the idle period of the medium. Let us de-fine as s the duration of an IEEE 802.11 time slot[12]. Moreover, if p is the probability that no pack-et arrives in a time slot, then (1 � p) is the proba-bility that at least one packet arrives in a time slot.Now consider that time is separated in slots of aspecific duration, and therefore we are dealingwith a discrete system. Consequently, packet arriv-als in a time slot can be modeled as Bernoulli trialswith probability of success (1 � p) that is the prob-ability that a packet arrives in a time slot. Accord-ingly, the number of consecutive idle slots before apacket arrival occurs follows the geometric distri-bution (1 � p)pn�1, where n is the number of con-secutive idle slots. Hence, the expected duration ofan idle period is the expected duration before thefirst arrival occurs. This is given as

I ¼ sX1n¼1

nð1� pÞpn�1 ¼ s1� p

: ð4Þ

For the busy period, B, consider the probabilitythat a packet is successfully transmitted. This is theconditional probability ps that a single arrival oc-curs in a time slot, given that at least one arrivaloccurs. Since the probability that no packet arrivesin a time slot is p, the probability that at least onearrival occurs is simply (1 � p). Let us define p1 theprobability that a single arrival occurs in a timeslot. Then the probability ps that a packet is suc-cessfully transmitted is given as:

ps ¼p11� p

: ð5Þ

At this moment lets define tB as the averagenumber of slots for which at least one packet ar-rives, referred as busy slots, given as

tB ¼X1n¼1

npð1� pÞn�1 ¼ 1p: ð6Þ

Note that, there are tBps slots where a successfultransmission occurs with the remaining tB(1 � ps)slots indicating a collision occurrence. Given Tsas the time needed for a successful transmissionand Tc as the time a collision takes, the expectedduration of a busy period is

B ¼ T stBps þ T ctBð1� psÞ ¼T c þ psðT s � T cÞ

p;

ð7Þwhere explicit expressions for Ts and Tc are givenin [18]. Lastly, for a packet of P bits transmittedover a wireless channel supporting data rates ofR bits/s, the transmission time of the packet pay-load is Tp = P/R and the average time for whichpayload information is transmitted in the channelis given by

S ¼ T ptBps ¼T pp1

pð1� pÞ : ð8Þ

Using Eqs. (4), (7) and (8) into Eq. (3), an expres-sion for the utilization of a cell where all actors arein line of sight with each other can be derived.In the remainder of this section the analysis

considers also the hidden terminal problem. Con-sider a number of identical cells of actor nodesfor which all actors within a cell are in line of sight(e.g. the cell of actor A of Fig. 1). Given as U theutilization of each cell then [19],

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U ¼ ULOS � q; ð9Þwhere ULOS is the utilization of the cell if no hid-den terminals exist (given above by Eq. (3)). More-over, q is the probability that ‘‘no actor outside thecoverage area of actor A transmits during a trans-mission of actor A’’, and ‘‘actor A does not trans-mit during any transmission of an actor outside itscoverage area’’ (see also [19]). Ignoring the propa-gation delay, it can be shown [19] that for theIEEE 802.11 case q is

q ¼ IBþ I

pT s=s� �N�1

; ð10Þ

where I, B, p, Ts, s are the same as in the analysiswith no hidden terminals consideration, N is thequotient of actors that can affect the transmissionof a specific actor to the actors that are in line ofsight with this actor, that is N = qp(2r)2/qpr2 = 4.Combining all the above, the actual utilization

of the cell where the considered actor belongs is

U ¼ S

ðBþ IÞ4I3p3T s=s: ð11Þ

2.2. Delay analysis

The media access delay metric can be easily de-rived from the utilization metric estimated previ-ously. Consider again an actor and a cell thatincludes all actors in the coverage area of the con-sidered actor. Let G be the normalized traffic of acell with respect to the packet transmission time,given as G = g(P/R). Moreover, the mean numberof retransmissions before a packet is sent is G/U[17]. Excluding the last, successful transmission,the actual mean number of retransmissions ism = G/U � 1. Considering a large packet inter ar-rival time compared to the time slot, we can as-sume that the packet does not wait for adistributed inter frame space (DIFS), defined in[12] interval in the first transmission attempt. Theexpected media access delay of a packet transmit-ted from an actor is equal to the duration Tsneeded for the packet to be successfully transmit-ted plus the time taken for all its unsuccessfulretransmissions. The second is equal to the ex-pected duration of each unsuccessful transmission

(backoff time plus the duration Tc of a collision)times the number of unsuccessful transmissions,m. Accordingly, the media access delay is givenas

dm ¼ mðT c þ s � X Þ þ T s; ð12Þwhere X is the expected number of backoff slots ina retransmission. Considering that the backoffslots in each attempt are uniformly distributed be-tween 1 and the contention window, then

X ¼ 1

m

Xm�1j¼0

1

2W 2j ¼ 1

2mW ð2m � 1Þ; ð13Þ

where W is the minimum contention window.Combining Eq. (11)–(13) we have for the media

access delay dm for a packet transmitted from anactor

dm ¼ gPRS

ðBþ IÞ4I3p3T s=s � 1� �

� T c þs � UR

2ðgP � URÞW ð2ðgP=URÞ�1 � 1Þ� �

þ T s:

ð14Þ

The traffic g that a cell of radius r around theactor generates is defined in Eq. (2).Now, if one ignores the processing delay, the

delay a packet faces from the begin of its transmis-sion until its reception from the receiving actor isequal to the media access delay times the numberof hops a packet needs to reach its destination.Considering a large WSAN setting and assumingthat the number of hops a packet needs to reachits destination is generally distributed with proba-bility ph[i], i = 1,2, . . ., the overall system delay dovcan be defined as follows:

dov ¼X1i¼1

ph½i � ði � dmÞ ¼ dmX1i¼1

ph½i � i ¼ dm � h;

ð15Þ

where h is the mean number of hops each packettraverses before reaching its destination. Theabove metric is very useful for the evaluation ofthe overall system performance in respect to boththe generated traffic and the system infrastructure,for any given network with an average hop count,h.

Table 1Model parameters—single channel AAC

Parameter Value

Time slot duration (s) 20 lsPropagation delay (d) �1 lsSIFS: short inter frame space [12] 10 lsDIFS: distributed inter frame space[12]

50 ls

Minimum contention window (W) 31 slotsPHY header 192 bitsMAC header 272 bitsACK 112 bits + PHYRTS 160 bits + PHYCTS 112 bits + PHY

Channel data rate (R) 6 Mb/sRTS threshold 256 BytesPacket payload (P) 1024 BytesActor transmission range (r) �400 mCluster radius 100 mActor density (q) 3.18 · 10�5 actors/m2

ph[1] 0.8

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2.3. Validation tests of the analytically

approximated metrics

In order to validate the accuracy of the analyticapproximation for the introduced metrics derivedin the previous subsections, several simulation sce-narios were considered in the OPNET advancedsimulation platform [20]. For the deployment ofthe single channel distributed AAC, the IEEE802.11g standard [22] is chosen that supports threedifferent channels and a variety of data rates. Fi-nally, AODV is chosen as the routing protocol.Regarding the distribution of the generated ac-

tor traffic k, this depends on various different char-acteristics specific to each network, such as sensortraffic characteristics, the communication protocolin the sensor–actor communication and the char-acteristics of the traffic processing procedure. Traf-fic processing is the procedure where an actorinitially collects the traffic from sensors, then pro-cesses it, adds its own control traffic and finallyforwards the aggregated traffic to other actors.The Poisson distribution can be adopted to effi-ciently characterize the traffic produced by an ac-tor with mean value k packets/s. In a similarmanner, the total generated traffic inside the cellfollows the Poisson distribution with parameter gpackets/s. Consequently, p and p1 discussed in Sec-tion 2.1 can be easily derived as follows:

p ¼ ðgsÞ0

0!e�gs ¼ e�gs;

p1 ¼ðgsÞ1

1!e�gs ¼ gse�gs:

ð16Þ

In addition, the packet payload size follows atruncated exponential distribution with a mean va-lue of P. The exponential distribution is provenadequate to describe the packet size distributionin IEEE 802.11 networks (e.g. [18]). The truncatedexponential distribution is chosen against the sim-ple one in order to better describe the fact that theIEEE 802.11 protocol allows a maximum packetpayload size of 32,768 bits.Lastly, the distribution that describes the num-

ber of intermediate hops a packet traverses to reachits destination can be easily characterized by thegeometric distribution, a sequence of Bernoulli tri-

als until a destination is reached. This also capturesthe fact that in aWSAN setting, it is more probablefor an actor to communicate with its adjacent ac-tors rather than with remote actors. This stemsout of the fact that any information from the sen-sors is usually location dependent and coordina-tion actions between actors also depend oncriteria and information based on a specific geo-graphic area. For example, if one considers aWSAN used to sense fire in a forest, most of thesensor information is exchanged between neighbor-ing actors responsible for a specific location andnot between all actors in the whole forest. In otherwords, the probability that an actor communicateswith another actor i hops away decreases as thenumber of hops increases and follows the geomet-ric distribution with parameter ph[1]. The expectednumber of hops h is now simply 1/ph[1].Table 1 summarizes the input parameters used

during the validation process.Table 2 summarizes the results of the compari-

son between the analytical media access delay met-ric estimate and the OPNET output. Note that,actor traffic refers only to the traffic that the actorgenerates himself, excluding any additional relayedtraffic. In all cases the results show that the modelis fairly accurate.

Table 2Media access delay metric validation—single channel AAC

Actor traffic (Kb/s)(1 packet = 8 P bits)

Media access delay (ms)(Analytical metric)

Media access delay (ms)(OPNET)

Difference(%)

10 2.39 2.62 920 4.04 4.36 830 19.63 21.20 840 283.45 303.29 7

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3. Modeling an IEEE 802.11-based multi-channel

distributed AAC scheme for a WSAN

As noted in Section 2, in a WSAN setting, it ismore probable for an actor to communicate withits adjacent actors rather than with remote actors.Apparently, the network performance could besignificantly improved provided that a specialweight would be given in the communication be-tween adjacent actors. This could be achieved byutilizing the multi-channel availability of the IEEE802.11 physical layer.The key feature is the usage of two independent

radio interfaces in order to separate the single hopAAC from the multi-hop AAC. Particularly, asdepicted in Fig. 2, two different channels are as-signed to each actor, namely Cs for the communi-cation between adjacent actors and Cm for thecommunication between remote actors. In this

Fig. 2. Multi-channel distributed AAC scheme analysis.

way, for a specific actor of a WSAN, a Cs channelcarries only traffic generated by this actor to beforwarded to an adjacent actor. (As it is depictedin Fig. 2, the actor A communicates directly withits adjacent actor B through Cs as long as itsends information destined for actor B.) Regard-ing relayed multi-hop traffic, actor D in Fig. 2 isa remote actor in relation to actor A (out of thetransmission range of the Cs channel) and there-fore actor A uses the Cm channel to communicatewith an intermediate actor C, which subsequentlyrelays the traffic to actor D through the Cm chan-nel. Note that, although actor C and actor D areadjacent actors, the communication between themis performed through the Cm channel, since thetraffic refers to remote actor A. This is because ifCs was used instead, firstly the relaying of trafficfrom Cs to Cm would add a processing overheadto the system and secondly the already loadedfrom local traffic Cs channel would exhibit lowperformance as more traffic would be added.Discussing implementation issues, the emerging

IEEE 802.11g standard [22] provides three avail-able channels with a variety of different data ratesand thus could be employed for the implementa-tion of the channels Cs and Cm.

3.1. Utilization and delay analysis

We proceed now to estimate the utilization anddelay metrics for both the Cs and the Cm channels.Following similar arguments to the single channelscheme one could assume that the number h ofhops a packet needs to reach the destination actorfollows a geometric distribution with parameterph[1] and consequently the probability that an ac-tor sends a packet to an adjacent actor is simplyph[1]. As a result, an actor would send on averageka Æph[1] data traffic to adjacent actors via the Cs

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channel and ka (1 � ph[1]) data traffic to remote ac-tors via the Cm channel (ka is the actual traffic bothactor generated and relayed).ConsiderRs, Rm and rs, rm the data rates and the

transmission ranges respectively of the channels Csand Cm. As a result, based on the transmissionranges of the channels Cs and Cm, two differentcells are now considered for each actor. The totalexpected traffic generated inside the cell of the Cschannel is

gs ¼ kaph½1qpr2s ; ð17Þ

while the corresponding traffic inside the cell of theCm channel is

gm ¼ kað1� ph½1Þqpr2m: ð18Þ

We can now easily derive the utilization andmedia access delay for each channel Cs or Cmindependently by using Eqs. (11) and (14) fromthe single channel analysis of Section 2 once we re-place g with gs or gm, R with Rs or Rm, and r withrs or rm.As previously discussed, since actor traffic is

location dependent, it is anticipated that a Cmchannel will carry less traffic than a Cs channel,so the data rate of the Cm channel can be lowerthan this of the Cs channel. The data rate decreaseof the Cm channel gives the opportunity to

Table 3Media access delay metric validation for the Cs channel

Actor traffic (Kb/s)(1 packet = 8 P bits)

Media access delay (ms)(Analytical metric)

10 2.0620 2.2330 2.5840 3.05

Table 4Media access delay metric validation for the Cm channel

Actor traffic (Kb/s)(1 packet = 8 P bits)

Media access delay (ms)(Analytical metric)

10 7.9520 15.4230 34.7240 82.48

decrease the number of hops required for a mul-ti-hop AAC since the transmission range rm isincreased as the data rate Rm is decreased.At this moment, note that the number of hops a

packet needs to reach the destination actor is pro-portional to the actor transmission range. Appar-ently, in the multi-channel AAC scheme, theexpected number of hops that a random packetneeds to reach the destination actor is approxi-mately rs

rmtimes less than this of the single channel

AAC scheme. Considering dsm and dmm the mediaaccess delay in the Cs and the Cmchannel respec-tively and ph[i], i = 1,2, . . . the probability that apacket needs i hops to reach the destination actor,the overall system delay in the multi-channel AACscheme is now:

dov ¼ ph½1dsm þX1i¼2

ph½i irsrm

� �dmm

¼ ph½1dsm þ 1� ph½12

ph½1dmm

rsrm

: ð19Þ

3.2. Validation tests of the analytically

approximated metrics

In order to validate the accuracy of the mediaaccess delay for the multi-channel AAC, the net-work was simulated using the OPNET advanced

Media access delay (ms)(OPNET)

Difference(%)

2.27 102.43 92.76 73.26 7

Media access delay (ms)(OPNET)

Difference(%)

8.57 816.70 837.15 787.43 6

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simulation platform in a manner similar to Section2.3. An additional channel Cm was introduced forthe multi-hop communication with data rates of2 Mb/s and a transmission range of 500 m.Tables 3 and 4 summarize the output of the

analytical approximation and the simulationregarding the media access delay metric for bothchannels. The results are also fairly accurate in amanner similar to the single channel scheme inSection 2.

4. Performance evaluation of the single and multi-channel AAC schemes

In the present section we use the models de-scribed previously in order to evaluate the perfor-mance of the proposed AAC schemes. Startingfrom the performance evaluation of the singlechannel AAC scheme, Fig. 3 depicts the overallsystem delay versus the actor traffic k for differentvalues of ph[1] that is the probability of the geo-metric distribution that an actor needs one hopto reach the destination actor. The network con-sidered is identical to this of Section 2.3 (see Table1 for the values of the related parameters).It rightly appears that the higher ph[1] is, the

more probable it gets for traffic to be topical andtherefore the less the overall system delay it gets.In that manner, the single channel AAC schemeexhibits low performance that is mainly due tothe additional traffic that each node is obligatedto relay.

1

10

100

1000

10000

100000

5 10 15 20 25 30 35 40Actor Traffic (Kb/s)

Ove

rall

Syst

em D

elay

(ms)

Ph[1]=0.9Ph[1]=0.8Ph[1]=0.7

Fig. 3. Overall system delay versus actor traffic—single channelAAC.

Let us now reconsider the network of Section2.3 by assuming that the traffic is highly �localized�with ph[1] equal to 0.9. Since traffic is highly local-ized the channel for the single hop communication(Cs) can assume a data rate equal to 6 Mb/s whilethe multi-hop communication (Cm) is given a low-er data rate equal to 2 Mb/s. In order to make afair comparison of the single and multi-channelschemes, when it comes to the single channel, thedata rate is equal to 9 Mb/s that is more thanthe sum of the data rates of Cs and Cm channels.Table 5 summarizes the input parameters for theperformance study.Figs. 4 and 5 depict the performance of the two

discussed AAC schemes in terms of the discussedmetrics. Fig. 4 depicts the media access delay forboth the single channel and the multi-channelAAC schemes. As it is shown, the multi-channelAAC scheme performs generally better than thesingle channel one. For the single hop communica-tion, the 6 Mb/s data rate in the multi-channelscheme assures a low media access delay comparedto the single channel scheme in all cases. For themulti-channel communication, the single channelscheme performs better when network traffic islow, mainly due to the fact that 2 Mb/s is not en-ough for the multi-hop traffic. As the network traf-fic increases, the multi-channel scheme performsbetter, since the increase in the single hop traffic af-fects also the multi-hop communication in the sin-gle channel scheme, which does not happen in themulti-channel scheme.Fig. 5 depicts the overall system delay for both

the single channel and the multi-channel AACschemes. It is proven that as the actor traffic in-creases, the multi-channel scheme guarantees asmaller overall system delay than the single chan-nel scheme.In the second scenario the traffic is now less

�localized� so the ph[1] parameter is equal to 0.7.In a manner similar to the first scenario, Cs chan-nel supports data rates of 6 Mb/s but now since anincrease is expected in multi-hop traffic, Cm chan-nel is given higher than before data rate equal to6 Mb/s. In the second scenario, for the study ofthe single channel scheme, data rates of 12 Mb/sare assumed. The input parameters for the secondscenario are presented in Table 5.

Table 5Model parameters for the multi-channel AAC scheme

Parameter Value

Time slot duration (s) 20 lsPropagation delay (d) �1lsSIFS: short inter frame space [12] 10 lsDIFS: distributed inter frame space [12] 50 lsMinimum contention window (W) 31 slotsPHY header 192 bitsMAC header 272 bitsACK 112 bits + PHYRTS 160 bits + PHYRTS Threshold 256 BytesPacket payload (P) 1024 Bytes

Parameter Scenario 1 Scenario 2

Channel data rate for Cs (Rs) 6 Mb/s 6 Mb/sChannel data rate for Cm (Rm) 2 Mb/s 6 Mb/sChannel data rate for the single channel 9 Mb/s 12 Mb/sAAC scheme (R)Actor transmission range for Cs (rs) �400 m �400 mActor transmission range for Cm (rm) �500 m �400 mActor transmission range for the single channel AAC scheme (r) �300 m �250 mCluster radius 100 m 100 mActor density (q) 3.18 · 10�5 actor/m2 3.18 · 10�5 actor/m2

ph[1] 0.9 0.7

1

10

100

1000

10000

10 20 30 40 50 60Actor Traffic (Kb/s)

Med

ia A

cces

s D

elay

(ms) Single-Channel AAC

Multi-Channel Single-hop AACMulti-Channel Multi-hop AAC

Fig. 4. Media access delay: single channel vs multi-channelAAC scheme—scenario 1.

1

10

100

1000

10000

10 20 30 40 50Actor Traffic (Kb/s)

Ove

rall

Syst

em D

elay

(ms)

Single-Channel AACMulti-Channel AAC

Fig. 5. Overall system delay: single channel vs multi-channelAAC scheme—scenario 1.

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Fig. 6 depicts the media access delay for the sin-gle channel and multi-channel AAC schemes, forthe present scenario. The two schemes appear tobehave in a manner similar to the first scenario.Yet, note that, in the single channel scheme themedia access delay is now further increased com-pared to this of the first scenario (see also Fig.4), since traffic is less localized and therefore relayoverhead is added. On the other hand, the media

access delay in the single hop communication ofthe multi-channel scheme is decreased since thesingle hop traffic is now less than in the first sce-nario. In addition, the media access delay forthe multi-hop communication of the multi-chan-nel scheme is also decreased compared to this ofthe first scenario. This is because, despite the factthat the multi-hop traffic is now increased, the6 Mb/s channel, compared to the 2 Mb/s channel

1

10

100

1000

10000

10 15 20 25 30 35 40 45 50Actor Traffic (Kb/s)

Med

ia A

cces

s D

elay

(ms)

Single-Channel AACMulti-Channel Single-hop AACMulti-Channel Multi-hop AAC

Fig. 6. Media access delay: single channel vs multi-channelAAC scheme—scenario 2.

1

10

100

1000

10000

10 20 30 40 50Actor Traffic (Kb/s)

Ove

rall

Syst

em D

elay

(ms)

Single-Channel AACMulti-Channel AAC

Fig. 7. Overall system delay: single channel vs multi-channelAAC scheme—scenario 2.

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of the first scenario, offers a lower media accessdelay.Finally, Fig. 7 depicts the overall system delay

for the two AAC schemes, concerning the secondscenario. In this case, the results are similar tothose obtained during the first scenario with a fur-ther improvement of the multi-channel schemecompared to the single channel one, in the lowerregions of actor traffic.

5. Conclusions

The paper discusses two IEEE 802.11-based dis-tributed AAC schemes (a single channel one and amulti-channel one) appropriate for WSANs. Ana-lytical and simulation results show that the multi-channel scheme performs better than the single

channel scheme especially for higher volumes ofactor generated traffic. The key feature of the pro-posed scheme is the separation of the single hopfrom the multi-hop traffic. In this way, as it isshown, the multi-channel scheme not only im-proves the performance of adjacent actors� com-munication, but also improves the performanceof the multi-hop communication.

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Dimitris Vassis was born in Ioannina,Greece, in 1978. He received theDiploma in Electrical and ComputingEngineering and the MBA in Techno-economic Systems both from theNational Technical University of Ath-ens (NTUA), Greece, in 2001 and 2004respectively. Currently, he is a Ph.D.student in the University of the Aegean,

Department of Information and Communication Systems

Engineering. His research interests are in the fields of perfor-mance evaluation and performance analysis of wireless accessnetworks.

George Kormentzas is currently lec-turer in the University of the Aegean,Department of Information and Com-munication Systems Engineering. Hewas born in Athens, Greece on 1973.He received the Diploma in Electricaland Computer Engineering and thePh.D. in Computer Science both fromthe National Technical University of

Athens (NTUA), Greece, in 1995 and 2000, respectively. From

2000 to 2002, he was a research associate with the Institute ofInformatics & Telecommunications of the Greek NationalCenter for Scientific Research ‘‘Demokritos’’. His researchinterests are in the fields of traffic analysis, network control,resource management and quality of service in broadbandnetworks. He has published extensively in the fields above, ininternational scientific journals, edited books and conferenceproceedings. He is a member of pronounced professional soci-eties, an active reviewer and guest editor for several journalsand conferences and EU-evaluator for Marie Curie Actions.George Kormentzas has participated in a number of nationaland international research projects, serving in some instances asthe project�s technical representative for University of Aegeanand/or as WP leader and/or as the project�s Technical Manager.

Dr. Charalabos Skianis is currently aresearch associate at the National Cen-tre for ScientificResearch �Demokritos�,in Greece and a visiting Lecturer at theDepartment of Information and Com-munication Systems, in the Universityof the Aegean in Samos, Greece. Heholds a Ph.D. inComputer Science fromthe University of Bradford, U.K. His

research interests include queuing systems, computer and com-

munication systems performance modelling and evaluation,ubiquitous and pervasive computing, mobile and wireless net-working. He is a member of pronounced professional societies,an active reviewer and guest editor for several journals andconferences and within the scientific/organizing committee ofseveral conferences.