Estimating Rate of Queue Usage to Differentiate Cause of Packet Loss in Multi-hopWireless Networks

Mi-Young Park Sang-Hwa Chung∗ Prasanthi SreekumariDepartment of Computer Engineering, Pusan National University

Pusan, 609-735, South Korea

Abstract—When TCP operates in multi-hop wireless net-works, it suffers from severe performance degradation. This isbecause TCP considers all packet losses as congestion signals,and reacts even to wireless packet losses by decreasing unnec-essarily its sending rate. Although several loss differentiationschemes are proposed to avoid such performance degradation,these schemes are designed for the last-hop wireless networks,and their accuracies in wireless loss discrimination are nothigh as much as we expect. In this paper, we suggest a newend-to-end loss differentiation which works entirely at the TCPsender. To improve its accuracy, we estimate the rate of queueusage using information available to TCP. If the estimatedqueue usage is larger than a certain threshold when a packet islost, our scheme diagnoses the packet loss as congestion losses.Otherwise it assumes the packet loss as wireless losses. Throughthe extensive simulations, we compare and evaluate our schemewith previous schemes in terms of accuracy and stability. Andthe results show that our scheme has the highest accuracyamong the previous schemes, and its accuracy is reliable undervarious multi-hop wireless network conditions.

Keywords-TCP; multi-hop wireless networks; end-to-end lossdifferentiation;

I. INTRODUCTION

Multi-hop wireless networks based on IEEE 802.11 areone of the most common features found in many applicationsof broadband home networking, enterprise networking, andmetropolitan area networks. Existing popular applicationssuch as Web browsing, e-mail, and file transfer will be alsoused in these networks, and it is necessary to transmit andto receive data using a reliable data transport protocol.

The most popular protocol for reliable data transport in thewired networks is the transmission control protocol (TCP)[6], due to its robustness under the dynamic network trafficconditions. The success of TCP in wired networks motivatesits extension to wireless networks, and it is assumed thatTCP will also remain one of the dominant protocols in multi-hop wireless networks.

When TCP operates in a multi-hop wireless network,however, it suffers from severe performance degradationbecause of the different characteristics of wireless networks

—————————————————————————————-This work was supported by the Grant of the Korean Ministry of Education,Science and Technology (The Regional Core Research Program/Instituteof Logistics Information Technology)∗Corresponding author.

and wired networks [2], [7]. The performance degradationis mainly caused by TCP’s basic assumption that any packetloss is an indication of congestion. Although this assumptionworks well in wired networks, it is not suitable for wirelessnetworks where most packet drops are caused by wirelesstransmission errors [2].

The appropriate behavior of TCP for the packet lossdue to wireless transmission errors is just to retransmit thelost packet without reducing its congestion window size.However, traditional TCP considers all packet losses ascongestion signals, and reacts even to wireless losses byunnecessarily decreasing its sending rate. To avoid such per-formance degradation, it is important for TCP to differentiatebetween packet losses due to congestion and packet lossesdue to wireless transmission errors. (we call packet lossesdue to congestion as congestion losses, and those due towireless transmission errors as wireless losses.)

For this reason, several loss differentiation algorithms(LDAs) have been proposed to improve TCP performancein wireless networks. These algorithms [1], [3], [4], [5], [8]distinguish the cause of packet losses based on informationavailable at Transport Layer, such as congestion windowsize, a round-trip time (RTT), a relative one-way trip time(ROTT) [10], and etc. However, their accuracies in wirelessloss discrimination are not high as much as we expect,and the accuracies of these have not been evaluated as thenumber of hops increases in multi-hop wireless networks,because these schemes are designed for cellular networkswhere only the last hop communicates through wireless link.

Through this paper, we observe how the accuracies of theLDAs vary in multi-hop wireless networks, and suggest anew end-to-end loss differentiation scheme which has highaccuracy in multi-hop wireless networks. To improve accu-racy, our scheme estimates a relative one-way trip (ROTT)using RTT at the sender side of TCP. If the estimated ROTT(EROTT) is close to the minimum EROTT, our schemeassumes that the queue usage is 1%, and if it is close tothe maximum EROTT, our scheme assumes that the queueusage is 100%. Hence, our scheme estimates the rate ofqueue usage by calculating the ratio of the current EROTTover the maximum EROTT.

Whenever a TCP sender receives the third duplicate ACKindicating that a packet is lost, our scheme checks if the

2009 33rd Annual IEEE International Computer Software and Applications Conference

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DOI 10.1109/COMPSAC.2009.73

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estimated queue usage is larger than a certain threshold(50%) or if the currently measured EROTT indicates con-gestion. If one of the two conditions is satisfied, our schemeassumes the packet loss as congestion losses. Otherwise,it assumes the packet loss as wireless losses. Our schemeoperates entirely at the sender side of a TCP, and does notrequire any support either from the receiver side or from theintermediate network nodes. Thus, it is more convenient todeploy our scheme in the current network environment.

Through the extensive simulations, we evaluate and com-pare our scheme with several previous LDAs from twoperspectives: accuracy, and stability. Accuracy is the mainappraisal standard of LDAs because as the accuracy ishigher, it can improve TCP’s performance much more. Inan accuracy point of view, we measure accuracies of wire-less loss discrimination and congestion loss discriminationseparately, and observe how each of the accuracies variesunder various network environment. In a stability respect,we measure the standard deviation of each LDA’s accuracy,in order to see if the accuracy is reliable under dynamicnetwork environment.

For this, we implement several previous end-to-end LDAs,and simulate these in a multi-hop wireless network usingQualNet 4.5 [12]. And we evaluate and compare these LDAschemes (NCPLD [8], Veno [1], West [5], JTCP [4], andRELDS [3]) with our proposed scheme (LDA EQ) based onthe two performance metrics. The simulation results showthat our scheme has the highest accuracy as well as itsaccuracy is more reliable under various multi-hop wirelessnetwork conditions.

The rest of our paper is organized as follows. Section 2describes the five existing end-to-end LDA schemes, and inSection 3 we introduce our loss differentiation scheme basedon estimating the rate of queue usage using informationavailable for TCP. In Section 4 we evaluate and compare theexisting LDAs with our scheme, and show that our schemehas the highest accuracy as well as its accuracy is reliablemore than the other schemes. Finally Section 5 concludesthis paper.

II. RELATED WORK

Several solutions for the problem of the loss classifi-cation have been studied and proposed to improve TCPperformance in wireless networks. These can be broadlyclassified in two classes: those that require support from theintermediate network nodes, and those that work purely onan end-to-end basis which retains TCP semantics. Since it isdifficult to deploy such solutions which require support fromthe network, end-to-end solutions are more desirable. Here,we introduce five end-to-end loss differentiation algorithms:NCPLD [8], Veno [1], West [5], JTCP [4], and RELDS [3].

Samaraweera [8] proposed an non-congestion packet lossdetection (NCPLD) to implicitly detect the type of packet

loss using the variation of delay experienced by TCP pack-ets. On detection of a packet loss, the scheme compares thecurrently measured round trip time (RTT ) with a calculateddelay threshold (delayThreshold). If RTT is less thandelayThreshold, the scheme treats the packet loss as wirelesslosses. Otherwise, it treats the packet loss as congestionlosses.

delayThreshold is calculated as below.

delayThreshold = RTTmin+0.5×RTT× BDP

TotalP ipeSize

where RTTmin is the minimum of measured RTTs, BDP isthe measured bandwidth delay product when a TCP senderexperiences the minimum RTT, and TotalPipeSize is anestimation of the total number of bytes in the network.

TCP Veno [1] estimates the backlog packets (N ) in thebuffer, and N is calculated as below.

N =cwnd

SRTT× (SRTT − RTTmin)

where cwnd is the current congestion window size, SRTTis the smoothed round-trip time. When a packet is lost, Venocompares N with β. If N < β, Veno ascribes the packet lossto wireless transmission errors. Veno suggested 3 as a goodsetting for β.

Yang [5] adopted Spike [10] scheme suggested by Cenand Voelker as its loss differentiation scheme. While Spikescheme uses the relative one-way trip time (ROTT ) takenby a packet to travel from the sender to the receiver, Yang(West) uses RTT at the sender side. Based on RTT , itcomputes the two thresholds, Bspikestart and Bspikeend , toidentify the state of the current connection.

Bspikestart = RTTmin + 0.4 × (RTTmax − RTTmin)Bspikeend = RTTmin + 0.05 × (RTTmax − RTTmin)

where RTTmin and RTTmax are the minimum and themaximum of measured RTT s respectively. If the currentRTT exceeds Bspikestart when the connection is not in thespike state, the connection enters the spike state. And ifRTT is less than Bspikeend when the connection is in thespike state, the connection leaves the spike state. Any packetlosses in the spike state are considered as congestion losses.

Wu and Chen [4] proposed a jitter-based TCP (JTCP) toadapt sending rates to the packet losses and jitter ratios.To distinguish congestion losses from wireless losses, JTCPcalculates the average (Jr) of the inter arrival jitter duringone round-trip time. The Jr value points out the conditionof queue length, and it can be calculated as follows.

Jr =(Rnewest − Roldest) − (Snewest − Soldest)

Rnewest − Roldest

where Snewest and Rnewest denote the sending time andthe receiving time respectively for the latest packet acked,and Soldest and Roldest denote the sending time and the

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receiving time respectively for the oldest packet during oneround-trip time. When the sender receives three duplicateACKs, it checks if the time receiving three ACKs exceedsone RTT as well as if Jr is larger than the inverse valueof the current congestion window size. If the two conditionsare satisfied, it ascribes the packet loss to congestion.

Lim and Jang [3] suggested a robust end-to-end lossdifferentiation scheme (RELDS) to precisely discriminatebetween congestion losses and wireless losses. The decisionrule for classifying the cause of packet losses follows.

RTT − SRTT

RTTdev> 2 × (

RTTmin

RTT)3 − 1

where RTTdev is the deviation of RTT which can becalculated using this formula, RTTdev = (3/4)RTTdev +(1/4)|RTT −SRTT |. If the numerical expression above issatisfied when the sender receives the third duplicate ACK,it assumes the packet loss as congestion losses.

These schemes are designed for cellular networks whereonly the last hop communicates through wireless link. Thus,we don’t know whether or not their accuracies are stable in amulti-hop wireless network. Through this paper, not only weobserve how the accuracies of the LDAs vary in multi-hopwireless networks, but also we suggest a new end-to-end lossdifferentiation scheme which has high accuracy in multi-hopwireless networks.

III. PROPOSED LOSS DIFFERENTIATION SCHEME

The common goal of LDAs for TCP is not to exactlydistinguish congestion losses from wireless losses, but toexactly identify whether or not the packet loss is related tocongestion. This is because any packet loss due to wirelesstransmission errors can happen when a TCP connectionexperiences congestion. In this case TCP should assumethe packet loss as congestion losses, and take a measure tocontrol congestion by reducing its sending rate. Otherwise itmight cause more serious congestion and poor performance.Thus, LDAs for TCP should distinguish packet losses relatedto congestion and those unrelated to congestion; we assumethe packet loss unrelated to congestion as wireless losses.

Intuitively, congestion is associated with the rate of queueusage. When the rate of queue usage exceeds a thresholdlimit, it is assumed as congestion, and packets are droppedby the queue manager according to its policy. The basicidea in our scheme is to estimate the rate of queue usageusing information readily available at Transport layer. If theestimated queue usage is larger than a threshold indicatingthat the queue is getting full, when a packet is lost, weassume the packet loss as congestion losses. Otherwise wediagnose it as wireless losses.

To retain TCP semantics when we estimate the rateof queue usage, we have to use information available atTransport layer such as congestion window size (cwnd), around-trip time (RTT), a relative one-way trip time (ROTT),

and etc. A previous study [9] suggested by experimentsthat ROTT can more accurately estimate the queuing delaythan RTT does. This is because RTT measurements conflatedelays along the forward and reverse paths, while ROTTincludes only the delay along the forward. In other words,RTT includes both delays of input queue and output queue,and the delay of input queue might hide the delay of outputqueue. As a result, it might give us wrong information inestimating the rate of queue usage.

Unfortunately, we cannot measure ROTT at the senderside of TCP, and we cannot calculate ROTT simply bydividing RTT in half as shown in the work [9]. Also wetried to send a ROTT measured at the receiver to the senderwhenever an ACK was sent in a simulation. In this case,the ROTT received at the sender was old information, andthe information was not useful as much as ROTT measuredat the receiver side. Thus, we decided to estimate ROTTat the sender side using RTT. As we mentioned before,RTT includes the two delays along the forward and reversepaths. For this reason, we estimate ROTT at the TCP senderby subtracting ACK ROTT from RTT. ACK ROTT is ameasure of the time taken by an ACK to travel from thereceiver to the sender.

Whenever a TCP sender receives an ACK, our schemecalculates EROTT, max EROTT, and min EROTT as thefollowing.

EROTT = RTT - ACK ROTTmax EROTT = max (max EROTT, EROTT)min EROTT = min (min EROTT, EROTT)

EROTT indicates the estimated ROTT measured at the TCPsender, and min EROTT and max EROTT are the minimumand the maximum of measured EROTTs respectively. Weassume that the queue usage is 1% if the currently measuredEROTT is close to min EROTT, and 100% if the EROTT isclose to max EROTT. Based on this assumption, we use theratio of EROTT over max EROTT to estimates the rate ofqueue usage at Transport layer.

Figure 1 shows the algorithm to estimate the rate ofqueue usage using EROTT whenever a TCP sender sendsa data packet. When a TCP connection starts, max EROTTand min EROTT do not have appropriate values, butmax EROTT and min EROTT will have right values as timegoes. Thus, if max EROTT becomes three times larger thanmin EROTT, we assume that max EROTT is the EROTTwhen the queue usage is 100%, and min EROTT is theEROTT when 1%. In this case, we estimate the queue usageusing the ratio of EROTT over max EROTT as shown at theline 4 in the figure 1. Otherwise, we assume that the TCPconnection is at the start point and the queue usage cannot belarger than 30% as expressed at the line 6. estimated queueindicates the estimated queue usage and its value rangesfrom 1% to 100%.

When our scheme estimates the queue usage, it also usesthe number of bytes in transit (flightsize). This is because as

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01: gap max min = max EROTT / min EROTT02:03: if (gap max min > 3)04: estimated queue = EROTT / max EROTT × 10005: else06: estimated queue = EROTT / min EROTT × 1007: end if08: estimated queue = estimated queue + flightsize / packet size09:10: if (50% < estimated queue < 60%)11: congestion EROTT = (congestion EROTT + EROTT) / 212: end if

Figure 1. Estimating the rate of queue usage

the number of bytes in transit is getting larger, the rate ofqueue usage is also getting larger. Hence, we assume that thequeue usage increases by 1% whenever one packet is addedinto the queue as shown at the line 8. Also we check thestart point of congestion using EROTT. To check the startingpoint of congestion (congestion EROTT), we calculate theaverage of EROTT when estimated queue ranges from 50%to 60% as shown at the line 10 and 12.

Whenever a TCP sender receives the third duplicate ACKwhich indicates that a packet is lost, our scheme checks ifestimated queue is larger than 50% or the current EROTT islarger than congestion EROTT. If one of the two conditionsis satisfied, it assumes the packet loss as congestion losses.If not, it assumes the packet loss as wireless losses.

Our proposed scheme operates entirely at the sender sideof a TCP, and does not require any support either fromthe receiver side or from the intermediate network nodes.Thus, our scheme can be deployed easily in the currentnetwork environment, and is more desirable than thoseschemes which operates at the receiver side of a TCP. Thisis because even if the receiver classifies the cause of loss,it still needs a way to notify the result of classification tothe sender; this requires changes at the sender as well as thereceiver side, which means that it is more expensive froman implementation perspective.

IV. EXPERIMENTS

To evaluate and compare our scheme with previous LDAs,we implemented not only our scheme but also the fiveprevious LDAs, and simulated these in a multi-hop wirelessnetwork using QualNet 4.5 [12]. To analyze the behaviorof these LDAs under dynamic network environment, wedesigned about 240 different scenarios by setting differentvalues for network parameters, and evaluated the LDAsunder the simulation scenarios. Through the extensive sim-

ulations, we aimed 1. to check if our estimated queue usageis highly correlated to the actual queue usage, 2. to observethe variation of accuracies of wireless loss discriminationand congestion loss discrimination under various networkconditions, specially in order to know if the number ofhops influences on these accuracies, 3. to investigate if theaccuracies are reliable under various network environment.

A. Performance metrics

Basically, we evaluate and compare our scheme with theprevious LDAs based on the following metrics: accuracy,and stability.

Accuracy. The main appraisal standard of LDAs is theaccuracy in distinguishing wireless losses and congestionlosses. As the accuracy is higher, the performance of TCPin multi-hop wireless networks will be improved muchmore. We measure three accuracies: the accuracy of wirelessloss discrimination (Aw), the accuracy of congestion lossdiscrimination (Ac), and the average (At) of accuracies ofAw and Ac.

Aw =Dw

Nw× 100

where Dw is the number of packet losses exactly identifiedas wireless losses by a scheme, and Nw is the number ofpacket losses caused by wireless transmission errors.

Ac =Dc

Nc× 100

where Dc is the number of packet losses exactly identifiedas congestion losses by a scheme, and Nc is the number ofpacket losses caused by congestion.

At =Aw + Ac

2Stability. This metric indicates whether or not the ac-

curacy of each scheme is stable under various network

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conditions. To measure this, we use the standard deviation(S) of the accuracy. As the standard deviation is lower, it isassumed that the accuracy is more reliable under dynamicnetwork environment.

S =

√√√√ 1n

n∑i=i

(Ai − A)2

where Ai is the accuracy measured in a simulation scenarioi, A is the average of accuracies measured in a set ofsimulation scenarios. And n is the number of simulationscenarios.

B. Simulation methodology

We used a 5-hop chain topology of IEEE 802.11b wire-less nodes in our experiment as shown in Figure 2. Thebandwidth of the wireless channel is 2Mbps, and the MAClayer protocol is 802.11b. And we set DropTail as itsqueuing policy, set the maximum segment size of TCP equalto 1K bytes, and the packet size equal to 1Kbytes. Thecongestion window size is limited to 16 packets, and TCPreceivers always implement the Delayed ACKs algorithm. Inall experiments, each scenario lasts about 200 seconds, anddata packets of TCP are continually transmitted during thesimulation after the warm-up period (35 seconds). All TCPflows are originated at the first node (node 1) and destinedto one of nodes on the right of Figure 2.

To achieve our aims in the experiment, we groupedour simulation scenarios into three groups: a group withpacket losses caused by only wireless transmission errors(W group), a group with packet losses caused by onlycongestion (C group), and the last group which is mixedwith the two types of packet losses (M group).

W group is designed to observe the accuracy of wirelessloss discrimination according to the rate of packet losses,and the number of hops. Thus, in this group, all packetlosses are caused by only wireless transmission errors, andonly one TCP flow is used to avoid causing congestion.The rate of packet losses ranges from 1% to 6%, and weused three different error models for each packet loss rate:deterministic, uniform, and exponential. When we analyzedthe results of W group, we used the average value ofaccuracies observed in the three different error models foreach packet loss rate. Each scenario in W group has differentvalue in terms of loss rate, hop count, and wireless errormodel. Thus, W group consists of 90 different scenarios bycombining the three factors differently.

C group is planed to observe the accuracy of congestionloss discrimination according to the rate of packet losses,the number of hops, and the queue size. Thus, all packetlosses in this group are caused by only congestion. To makedifferent levels of congestion, we increased the number ofTCP flows gradually. As we increase the number of TCPflows, the rate of packet losses due to congestion increases.

Figure 2. A chain topology

The rate of packet losses due to congestion ranges from 1%to almost 15% as we increase the number of TCP flows.Each scenario in C group has different value in terms of thenumber of TCP flows, hop count, and queue size (20KB -60KB). Thus, C group consists of 125 different scenariosby combining the three factors differently.

M group is designed to evaluate our scheme under a morerealistic network environment. For this, we mixed the twotypes of packet losses (wireless losses, congestion losses),and observed the accuracy according to the number of hopsand the queue size. The rate of packet losses in each scenarioranges from 4% to 8%, and the ratio of wireless losses tocongestion losses is approximately 5:5, 3:7, 7:3, 2:8, or 8:2under different network parameters. Each scenario in Mgroup has different value in terms of hop count, and queuesize. Thus, M group consists of 25 different scenarios bycombining the two factors differently.

C. Simulation results

1) Estimation of the rate of queue usage: By tracingthe estimated queue usage and the actual queue usage in asimulation, we observed how precise our estimated queueusage is. Figure 3.(a) shows the actual queue usage andour estimated queue usage within a certain period of time.As shown in the graph, we can see that our estimatedqueue usage is close to the actual queue usage. To measuremathematically the correlation between our estimated queueusage and the actual queue usage, we used Pearson’s cor-relation coefficient, which indicates the strength of a linearrelationship between two variables.

Pearson’s correlation coefficient ranges from -1 to +1. Ifthe correlation coefficient of two variables (x and y) is closeto 1, it indicates that there is a strong positive correlationbetween the two variables. In this case, the values for yincrease as values for x increase. If the correlation coefficientis close to -1, it means that there is a strong negativecorrelation between these, and the values for y decrease asvalues for x increase. Generally, if the correlation coefficientis larger than 0.4 or smaller than -0.4, it is assumed that thereis a meaningful correlation positively or negatively betweentwo variables.

We calculated the correlation coefficient between theestimated queue usage and the actual queue usage in allour simulation scenarios. When we observed the correlationcoefficient, the values always vary under different networkconditions. Thus, we checked how the correlation coefficientchanges according to the queue size, and the number of hops.Figure 3.(b) and (c) show the variation of the correlation

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(a)

(b) (c)

Figure 3. Correlation between the estimated queue usage and the actual queue usage

coefficient of LDA EQ (we named our scheme LDA EQ)according to the queue size and the number of hops respec-tively.

In case of Figure 3.(b), the correlation coefficient isalways higher than 0.4, which means that our estimatedqueue usage is highly correlated with the actual queue usage.Specially, it tends to increase as the queue size increases. Incase of Figure 3.(c), the correlation coefficient is decreasingas the number of hops is increasing. However, the correlationcoefficient of LDA EQ is still higher than 0.4 in mostcases. Hence, our estimated queue usage is appropriate todifferentiate the packet losses related to congestion and thoseunrelated to congestion.

2) Accuracy of classifying cause of packet losses: Firstof all, in W group, we observed how Aw changes accordingto the number of hops, and according to the rate of packetlosses. Figure 4.(a) shows Aws of all LDA schemes as therate of packet losses increases. Among the LDA schemes,NCPLD, and Veno have low accuracy, while LDA EQ, andJTCP have high accuracy of wireless loss discrimination.

Figure 4.(a) shows that Aws of West and LDA EQ tend toincrease as the rate of packet losses increases, while those ofRELDS and JTCP fluctuate under different packet loss rate.Also, we can see that the accuracy of our scheme fluctuatesless than the other schemes.

Figure 4.(b) shows that the variation of Aw as the numberof hops increases. The graph shows that the accuracies ofWest, JTCP, and LDA EQ tend to decrease as the numberof hops increases. Although our scheme is affected by thenumber of hops, its variation is rather less than that ofRELDS or West. From the two graphs, we can see that Aw

of our scheme is higher than the other schemes, and is lessaffected by the rate of packet losses as well as the numberof hops.

In C group, we observed Acs of all LDA schemesaccording to the loss rate, hop count, and the queue size.For this, we caused packet losses by only congestion, andremoved all packet losses caused by wireless transmissionerrors. To make different levels of congestion, we increasedthe number of TCP flows gradually. As we increase the

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(a) 2 hops (b) loss rate: 4%

Figure 4. Aw in W Group

number of TCP flows, the rate of packet losses due tocongestion increases, and the rate ranges from 1% to almost15%.

Figure 5.(a) shows the variation of Acs of all LDAschemes as the rate of packet losses due to congestionincreases. As shown in the graph, NCPLD, Veno, and Westhave the highest accuracy of congestion loss discrimination,while JTCP has the lowest accuracy. Although the accuracyof our scheme is not the highest, it is always higher than70%. Figure 5.(b) shows the variation of Ac as the numberof hops increases. In this graph, we can see that Acs of mostschemes are decreasing a little bit as the number of hops isincreasing, while Ac of JTCP tends to increase a little bitas the number of hops increases. Figure 5.(c) shows that Ac

of LDA EQ is highly affected by the queue size, and thatof LDA EQ increases as the queue size increases.

In terms of Ac, NCPLD, and Veno always have the highestaccuracy under different network conditions, while Aws ofthese are the lowest as shown in Figure 4. And, Acs ofJTCP, RELDS, and LDA EQ are lower than Aws of these.If we compare Figure 5 with Figure 4, we can see thatAcs of all schemes are rather stable than their Aws underdifferent network conditions. In other words, the accuracyof wireless loss discrimination of all schemes fluctuatesrather severely under different network conditions, whilethe accuracy of congestion loss discrimination is stable.However, our scheme is more stable in both Aw and Ac

compared to the other schemes; only Ac of LDA EQ ishighly affected positively by the queue size as shown inFigure 5.(c).

In M group, we mixed the two types of packet losses(wireless losses, congestion losses) to evaluate our schemeunder a more realistic network environment. We measuredthe three accuracies (Aw, Ac, At) according to the numberof hops, and the queue size. Figure 6.(a) shows that theaccuracies of LDAs are affected by the number of hops,and these accuracies are decreasing as the number of hops

is increasing. Although the accuracy is decreasing accordingto the hop count, the accuracy of our scheme is always thehighest among the other schemes. Figure 6.(b) shows theaccuracies of LDAs as the queue size is increasing. Theaccuracies of West, RELDS, and LDA EQ tend to increaseas the queue size is increasing, and our accuracy is also thehighest among the LDAs.

Figure 6.(c) shows the average accuracy of each of Aw,Ac, and At observed in M group. From the graph, we cansee that if a scheme has high accuracy of wireless lossdiscrimination (Aw), it has low accuracy of congestion lossdiscrimination (Ac), and vice versa. For example, in case ofJTCP, its Aw is the highest, but its Ac is the lowest. And, incase of West, its Ac is the highest, but its Aw is lower thanthe other schemes. However, our scheme has high accuracyin both Aw and Ac compared to the other schemes. Thus,the average accuracy (At) of LDA EQ is the highest (77%)among these LDAs as shown in the graph 6.(c).

3) Evaluation of stability: As shown in the previoussection, the accuracy of each LDA varies under differentnetwork conditions. Even if its accuracy is higher, its accu-racy will not be reliable if it fluctuates highly under dynamicnetwork environment. Thus, it is necessary to measure thelevel of fluctuation of the accuracy. For this, we measuredthe stability of accuracy of each scheme using the standarddeviation.

The standard deviation is a simple measure of the vari-ability of a data set. A low standard deviation indicates thatall of the data are very close to the mean value, while a highstandard deviation indicates that the data is spread out overa large range of values. In other words, we can interpret thatif the value of standard deviation of accuracy is low, thenthe accuracy of an LDA is stable under various networkconditions. Otherwise, the accuracy of an LDA fluctuateshighly according to various network conditions. Thus, as thestandard deviation is lower, the accuracy is more reliable.

Figure 7 shows the average standard deviation of accura-

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(a) 60KB, 1 hop

(b) 60KB, 25 flows

(c) 15 flows, 3 hops

Figure 5. Ac in C Group

(a)

(b) 1 hop

(c)

Figure 6. Accuracies in M Group

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Figure 7. Stability

cies observed in M group. For each scheme, we measuredthree standard deviations: Sw, Sc, and St. Sw indicates thestandard deviation of Aw, Sc indicates the standard deviationof Ac, and St is corresponding to At.

In the graph, the accuracies of NCPLD, Veno, and JTCPare more stable than the other schemes because their stan-dard deviation is lower than the other schemes. However,their accuracies are also lower as shown in Figure 6.(c).When we compare the stability of our scheme with those ofWest and RELDS which have high accuracy comparatively,our scheme is more stable than West and RELDS. Forexample, around 70% of accuracies of LDA EQ observed inM group ranges from 77% (At) - 12% (St) to 77% + 12%approximately, while that of accuracies of RELDS rangesfrom 64% (At) - 14% (St) to 64% + 14% roughly. Thereforewe can conclude that our scheme has the highest accuracy aswell as its accuracy is more reliable than the other schemes.

V. CONCLUSION

In this paper, we suggested a new end-to-end loss differ-entiation which operates entirely at the sender side of a TCP.Thus, our scheme does not require any support either fromthe receiver side or from the intermediate network nodes, andit can be deployed easily in the current network environment.

To improve its accuracy, our scheme estimates the rateof queue usage using EROTT at the sender side of TCP.Whenever a TCP sender receives the third duplicate ACKindicating that a packet is lost, our scheme checks if the es-timated queue usage is larger than a certain threshold (50%)or if the currently measured EROTT indicates congestion. Ifone of the two conditions is satisfied, it assumes the packetloss as congestion losses. Otherwise, it assumes the packetloss as wireless losses.

Through the extensive simulations, we evaluated andcompared our scheme with several previous LDAs from thetwo perspectives: accuracy, and stability. The results showthat our scheme has high accuracy in both accuracies ofwireless loss discrimination and congestion loss discrimi-

nation compared to the other schemes. Also, it shows thatits accuracy is reliable under various multi-hop wirelessnetwork conditions.

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