Towards Characterization of Wireless Trafficin Coexisting 802.11a/g and 802.11n NetworkHirochika Asai

Kensuke Fukuda

Hiroshi Esaki

University of Tokyo

NII / PRESTO JST

University of Tokyo

1. INTRODUCTION
The number of wireless devices and the traﬃc volume generated by these devices become signiﬁcant today, and many devices begin supporting 802.11n protocol for higher-speed wireless access. However, thediversiﬁcation in link types of end-hosts may degrade
system performance. For example, hosts using 802.11
protocols had better not be relay nodes in a P2P live
streaming system because 802.11 is a half-duplex protocol and usually less stable compared to modern wired
links. Hence, understanding traﬃc characteristics ofvarious link types is essential for improving or building
network architectures. Moreover, estimation of the link
type of a remote host possibly achieves better performance (e.g., higher throughput) in some network systems. Baiamonte et al. [1] have proposed an algorithm
to detect wireless hosts from passive measurement byusing the entropy of packet interarrival time (PIT). Wei
et al. [2] have also proposed an algorithm to classify
access network types. However, these algorithms pay
no attention to new link types such as 802.11n, 3G,
and WiMAX even though each of them has diﬀerent
characteristics and possibly degrades the performanceof network systems.
Our goal is to analyze and model the characteristics
of various link types which can be criteria for system design, and then to provide an algorithm to identify thelink type of a remote host. In this paper, we analyze
PIT, its entropy of 802.11 hosts, and fairness between
802.11a/g and 802.11n in coexisting these wireless networks with packet trace in relatively large-scale experiment. The contributions of this paper are to show that802.11n has diﬀerent characteristics from 802.11a/g in

PIT and its entropy, and to conﬁrm fairness between
802.11a/g and 802.11n hosts in time-domain.

2.

TRAFFIC CHARACTERISTICS

The entropy of PIT has been commonly used to characterize the traﬃc of bottleneck links [1, 2]. A probability mass function (PMF) of PIT is deﬁned as P (τi ) =mi /m, where m is the total number of sampled PITand mi is the number of samples whose PIT is in therange [τi , τi+1 ). N.B., τi = bi, where the time bin b is aconstant value. We then deﬁne the entropyof PIT from∑the PMF in the equation: H := − i P (τi ) log2 P (τi ).
The entropy in this context represents uncertainty ofPIT; for example, PIT of hosts connecting with sharedlinks might be ﬂuctuated due to collisions and the entropy would be larger while those connecting with nonshared (i.e., exclusive) and stable links can certainly
send packets without collisions nor loss. In this paper,
we use the same parameters as those used in Ref. [1],that is, the time bin b is 100µs, a maximum thresholdof PIT is set to 10ms, a time window for calculation ofentropy is 20s, and a minimum threshold of the numberof samples in a time window is 200.
We also deﬁne two fairness indexes, throughput andtransfer duration fairness indexes, to evaluate fairness
between 802.11a/g and 802.11n hosts in coexisting thesenetworks. The throughput fairness index of the protocol p (p ∈ {802.11a/g, 802.11n}) with the channel csp (∆)
is deﬁned as Fsp,c (∆) := 802.11a/g c 802.11n , wheresc(∆)+sc(∆)

spc (∆) is average throughput among hosts using the protocol p with the channel c during the duration ∆. In thesame way, the transfer duration fairness index is also dedp (∆)
ﬁned as Fdp,c (∆) := 802.11a/g c 802.11n , where dpc (∆)dc(∆)+dc(∆)

is average transfer duration among hosts using the protocol p with the channel c during the duration ∆.
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3.

MEASUREMENT AND RESULTS

The measurement was conducted in the biannual symposium of the WIDE project in 9–12 March 2010; thewireless network of the symposium consisted of ninewireless access points (APs: Cisco Aironet 1250) with

802.11a802.11g
802.11n (2.4G)
802.11n (5G)
0100
200
300
400
Peak packet inter-arrival time [us](a) Peak PIT

500

0.014

802.11a802.11g
802.11n (2.4G)
802.11n (5G)0.014

802.11a802.11n

0.012
0.01

0.01

0.008
0.006
0.004

234Entropy

56

one controller (Cisco Wireless Controller 5508), and215 (client) stations. All the APs were operated inlightweight mode, and consequently, all the data frames
through APs were encapsulated by the CAPWAP protocol (RFC 5415) and went through the controller. We
had captured these encapsulated data frames at a monitored interface between the controller and APs by tcpdump (total: 122 million frames). After the measurement, we extracted 802.11 frames from the encapsulated traﬃc trace. We had also measured the information of associated stations from APs every ten seconds
by SNMP. The maximum number of stations simultaneously connected to APs was 148. The number of measured associations of 802.11a, 802.11g, 802.11n (2.4GHz)
and 802.11n (5GHz) are 105, 129, 129 and 116, respectively; N.B., we double-counted the hosts that support
both 2.4GHz and 5GHz etc.
We show the cumulative distribution function (CDF)of peak PIT by protocols in Figure 1(a). The peak PIT
is the peak value in the PMF P with the time bin b = 1.
We conﬁrmed that the peak PIT of 802.11a/g mostly
distributed above 120µs, and that of 802.11n concentrated around 10µs, meaning that the block ACK mechanism in 802.11n aﬀected PIT signiﬁcantly. We thenshow the CDF of the entropy of PIT by protocols in
Figure 1(b). The entropy of 802.11n has smaller values than that of 802.11a/g. The result points out thatthe identiﬁcation algorithm in Ref. [1] makes inaccurate annotations to more than 20% hosts though it isappropriate for 802.11a/g because it judges a host withH ≤ 3.5 as “wired”. Thus, the simple entropy-basedalgorithm has a diﬃculty in identifying 802.11n hosts.
We also evaluate fairness between 802.11a/g and 802.11n
hosts in coexisting these networks. 802.11a and 802.11n
share the 5GHz band, and 802.11g and 802.11n also
share the 2.4GHz band, implying the potential conﬂicts(i.e., fairness issue). In this evaluation, we focus on
the channel 36 in 5GHz band. Figures 2(a) and (b)show the probability distribution functions (PDF) of
throughput and transfer duration fairness indexes foreach second by protocols, respectively. Here, throughput is calculated from the accumulated frame length,

0.2
0.4
0.6
0.8
Throughput fairness index(a) Throughput (Fsp,c )(b) EntropyFigure 1: CDF of peak PIT and entropy of PIT
by protocols

007

0.006

0.002

01

0.008

0.004

0.002

0802.11a802.11n0.012

PDF

10.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0PDF

CDF

CDF

1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
01

0

0.2
0.4
0.6
0.8
Transfer duration fairness index

1(b) Transfer duration (Fdp,c )Figure 2: PDF of fairness indexes by protocols(c = 36)
and transfer duration is estimated from the transmission rate and frame length although control frames suchas ACK were not captured. From Figure 2(a), the PDFs
of the throughput fairness index are biased by protocols, and∫the index of∫ 1802.11n distributes to larger val0.5
ues; i.e., 0 ydx  0.5 ydx, where x is the throughput
fairness index of 802.11n and y is the PDF of x. Thismeans 802.11n has an advantage in throughput. On theother hand, from Figure 2(b), the PDFs of the transfer duration∫ fairness index∫ 1 are ﬂat and similar to each
0.5
other; i.e., 0 ydx ' 0.5 ydx, where x is the transfer
duration fairness index of 802.11n and y is the PDF ofx. Note that the PDFs have large jumps around at 0and 1. We can ignore these jumps because they comefrom errors in transfer duration estimation due to diﬀerent ACK mechanisms between 802.11a/g and 802.11n.
Hence, the transfer duration is approximately fair between 802.11a and 802.11n. This ﬁnding enables us
to fairly compare 802.11a/g and 802.11n in coexistingthese network.

4.

CONCLUSION

We had measured and analyzed traﬃc from hosts using 802.11 protocols in coexisting 802.11a/g and 802.11n
wireless networks. We showed that the entropy of PITof 802.11n was diﬀerent from that of 802.11a/g. However, the entropy-based link type estimation algorithm
has diﬃculty in distinguishing 802.11n hosts from wired
hosts. We also showed 802.11a/g and 802.11n were fairin terms of transfer duration though 802.11n gained interms of throughput.
We will investigate further characteristics of wirelessnetwork traﬃc to identify link types of remote hosts andto ﬁnd criteria for system design as well as cross-layer
characteristics (i.e., TCP).

5.
REFERENCES[1] V. Baiamonte, K. Papagiannaki, and G. Iannaccone.
Detecting 802.11 wireless hosts from remote passive
observations. In NETWORKING 2007, pages 356–367.
[2] W. Wei, B. Wang, C. Zhang, J. Kurose, and D. Towsley.
Classification of access network types: Ethernet, wirelessLAN, ADSL, cable modem or dialup? Comput. Netw.,
52(17):3205–3217, 2008.