MobiSys '03 Paper
[MobiSys '03 Tech Program Index]
|Pp. 303-316 of the Proceedings|
Characterizing Mobility and Network Usage in a Corporate Wireless Local-Area Network
Characterizing Mobility and Network Usage in a Corporate Wireless Local-Area Network
MIT Laboratory for Computer Science
IBM T.J. Watson Research Center
Wireless local-area networks are becoming increasingly popular. They
are commonplace on university campuses and inside corporations, and
they have started to appear in public areas . It is
thus becoming increasingly important to understand user mobility
patterns and network usage characteristics on wireless networks. Such
an understanding would guide the design of applications geared toward
mobile environments (e.g., pervasive computing applications), would
help improve simulation tools by providing a more representative
workload and better user mobility models, and could result in a more
effective deployment of wireless network components.
Several studies have recently been performed on wireless university
campus networks and public networks. In this paper, we complement
previous research by presenting results from a four week trace
collected in a large corporate environment. We study user mobility
patterns and introduce new metrics to model user mobility. We also
analyze user and load distribution across access points. We compare
our results with those from previous studies to extract and explain
several network usage and mobility characteristics.
We find that average user transfer-rates follow a power law. Load is
unevenly distributed across access points and is influenced more by
which users are present than by the number of users. We model user
mobility with persistence and prevalence. Persistence
reflects session durations whereas prevalence reflects the frequency
with which users visit various locations. We find that the probability
distributions of both measures follow power laws.
Several recent studies characterize the usage of various wireless
networks [3,8,9,10,15,16]. Tang
and Baker  focused on a university building and traced
the activity of 74 users over 12 weeks. Kotz and
Essien [8,9] studied a university campus network
with 1706 users scattered through 161 buildings with a total of 476
access points. Balachandran et al.  examined
usage of a wireless network in a large auditorium during a three day
conference. Tang and Baker  also studied the Metricom
metropolitan-area packet radio wireless network, a public network with
approximately 25,000 radios. Lai et al.  analyzed a
combined wireless and wired network, but that study was limited to
only eight users.
Each study presents patterns of user mobility and network usage
characteristics for one particular domain. In this paper, we
complement these studies by presenting results from a four week trace
gathered on a corporate wireless local-area network (WLAN). Our trace
presents the activity of 1366 users. We use our trace as well as
results from previous research to extract common characteristics of
WLAN usage and to highlight and explain usage differences. We focus on
population characteristics, load distribution across access points
(APs), user level of activity, and user mobility.
We find that variations in the number of wireless users over time
closely follow patterns of the underlying population, even though most
users access the wireless network a fraction of days and a fraction of
time. Hence, the number of users on a network might be adequately
modeled by scaling down general population models.
Our study shows that there exist large personal differences in
users' mobility as well as in their data transfer rates. Some users
transfer over 1Mbps on average while others transfer less than 10Kbps
on average. In general, we find that user average transfer rates
follow a power law. The aggregate data transfer rate seen by an access
point does not seem to depend on the number of users associated with
the access point, but rather on which users are present. In each
building, approximately 30% of access points owe over 40% of their
load to the most active 10% of users on the network. Location of an
access point also plays a role in the aggregate load it observes.
Users spend a large fraction of their time and long periods of time at
a single location, which we call their home
location. Interestingly, they do not reduce their network usage when
moving away from that location and changing location more frequently.
We model user mobility with persistence and
prevalence. Persistence measures how long users stay continuously
associated with the same access point and prevalence reflects how
frequently users visit various locations. Our definitions are based on
Paxson's definitions of "routing persistence" and "routing
prevalence" in his study of Internet routing
stability . We find that the probability distributions
of both measures follow power laws. We use prevalence metrics to
classify users into different mobility categories. We find that 50%
to 80% of users are occasionally or somewhat mobile: they spend most
of their time at a single location, but periodically visit other
Comparing our results with other studies, we find many similarities in
mobility and network usage characteristics. We find that these
characteristics are best explained by factors orthogonal to whether
the network runs on a campus, in a corporation, or in a public
environment. The main factors influencing network usage include
personal differences between users and function of various locations
(including scheduled events). The main differences in user mobility
appear among locations serving as primary places of work and locations
visited occasionally. The density of resources (classrooms, conference
rooms), and differences between individual users also influence
The rest of the paper is organized as follows. We first present the
methodology used to gather our trace in Section 2. In
Section 3, we describe the characteristics of our user
population and contrast it with the population in previous studies. In
Section 4, we describe load distribution across access
points and analyze factors influencing access point
load. Section 5 presents and compares user mobility
characteristics in each environment. We also introduce metrics for
describing these characteristics. In Section 6, we
discuss how some of our findings may benefit network deployment,
application design, and simulation of user mobility. We conclude in
The 802.11b wireless local-area network that we studied is spread
throughout three large corporate buildings hosting computer science
and electrical engineering research groups. The largest of the
buildings, which we call LBldg, has 131 access points and is
approximately 10 miles away from the other buildings. The other
buildings, MBldg and SBldg, are adjacent to each other. They have 36 and
10 access points respectively. The placement of access points in
buildings is based on geometry (one access points per corridor, for
instance). Extra access points are placed in a few highly used rooms,
such as a customer laboratory in SBldg.
The network is configured to run in infrastructure mode, in which
wireless clients connect to the wired network through access points
distributed in the environment. All 177 access points are Cisco
Aironet 350s. We observed a total of 1366 unique MAC
addresses. Laptops were by far the predominant devices on the network.
We do not have information whether any other types of devices were
used at all. We assume that each unique MAC address corresponds to a
user, even though it is possible for a single user to have more than
one MAC address or for users to trade cards with each other.
We used SNMP  to poll access points every 5 minutes,
from Saturday, July 20th 2002 through
Sunday, August 17th 2002. We chose 5 min intervals to ensure that our
study would not affect access point performance. We
collected information about the traffic going through each access
point as well as about the list of users associated with each access
point. For each user, we retrieved detailed information on the amount
of data (bytes and packets) transferred, the error rates, the latest
signal strength, and the latest signal quality. We polled all access
points except three located in MBldg that did not respond to SNMP
Due to a power failure, there is a one-hour hole in the data
(07/30/2002 from 1pm to 2pm). For unknown reasons, we also have a few
holes in the data gathered at a few of the access points during the
evening and night of 08/08/2002. Due to periods where access points
were heavily loaded, some sample intervals stretch to 10 min.
Users were not informed that the study was performed. The only
sensitive information that we gathered were the MAC and IP addresses
of network cards, as well as the names assigned to access points. To
ensure user privacy, we anonymized all three types of information. We
did not map access points to explicit locations or track individual
users. We only present aggregate results.
All data from our trace is available for download at the following
3 Wireless user population characteristics
We saw a total of 1366 distinct users in our four week trace: 796
users spent most of their time in LBldg building, 437 in MBldg, and
133 in SBldg. Figure 1 shows the total number of
users present on the network every day of the
trace. Figure 2 shows the number of users present
on the network during different hours of a day on weekdays. We show
the 10th, 50th, and 90th percentile values registered for each hour
throughout the trace. For all three buildings, the patterns reflect
the office environment and normal office work hours. We also note a
slight reduction in the number of users around lunch time
(Figure 2). However, since the reduction is
small, we conclude that most users work through lunch or leave their
laptops on while they eat, so the machine is "present" even if there
is no activity. Also, some users stay late at night or leave their
laptops on when they go home, since the number of wireless users is
greater than zero during the night.
These patterns are similar to those found at university campus
locations used for working (offices, libraries, academic
buildings) [8,9,15]. They differ from
on-campus locations such as dormitories [8,9] and
public metropolitan networks , which users access both
from work and from home. In these environments, peaks in the number of
users appear during evening hours. There are also much lower
reductions in the number of users on weekends. Small scale networks
such as a single conference room  show much more
variability in the number of users due to the impact of scheduled
Therefore, daily and hourly patterns in
numbers of wireless users on a network are closely tied to
patterns in the underlying population. Differences
appear not so much among public,
academic, or corporate networks but among networks that cover usage at
the work place, at home, or during a specific event.
Figure 1: Total number of wireless users in each building on each day
of the trace. The trace starts on Saturday, July 20th 2002. The figure
show patterns of a normal office work week.
Figure 2: Number of users per hour on weekdays.
For each building, the 10th, 50th, and 90th percentile values for each
hour are shown. The figure shows a strong pattern of regular office
To further characterize the user population, Figure 3
presents the cumulative distribution of the number of days each user
appeared in the trace. Each user was counted only in the distribution
of the building where the user spent most of his or her time. The
number of days that users are present varies greatly: only 12% to
25% of users are present more than 18 out of the 20 work days,
whereas 22% to 38% of users appear only during one or two days. We
suspect that the latter group are outside visitors mostly from other
sites that the company has in the same metropolitan area. It is
interesting to note that the great variety in the number of days
that users are present does not influence the regular pattern shown
in Figure 1. The presence of visitors and the
absence of employees must therefore be uniformly distributed.
In terms of the fraction of days that users access the network, our
distribution is similar to a single building on a university
campus . Compared with a whole
campus [8,9] our trace has more users appearing
only one or two days (visitors) and fewer users appearing more than
2/3 of the days. The higher uniformity of a campus wide distribution
might be related to the fact that the study tracks many users for
prolonged periods of time (i.e., students living on campus) and not
only when they come to work in specific buildings.
Additionally, we computed the fraction of time users remain on the
wireless network on days where they actually use it. We found
that 50% of users remain connected 60% to 100% of the work day.
Figure 3: Number of days users are present in the trace. The distribution
is uneven at the edges. A large fraction of users (around 22% to
38%) appear only one or two days in the trace. Less than 10% of
users come more often than the 20 work days of the trace and a
fraction of these are laptops left in offices.
4 Load distribution across access points
In this section we examine load distribution across access
points. According to current guidelines [1,5],
access points should be distributed based on the physical aspects of
buildings, the signal strength, and signal-to-noise ratios, as well as
the number of users and their application mix. In this section, we
examine how load is balanced across access points in real settings. We
examine the user distribution, the total amount of data transferred, as
well as the data transfer rates. We also examine factors influencing
access point load.
Figure 4 shows the fraction of users seen at each
access point throughout the trace. A few access points see a small
fraction of users: 10% of access points in LBldg see only 2.5% of
all users who visited the building. Others see a greater fraction of
users, some as much as 50%. Differences between buildings
are partly explained by building sizes (LBldg is much larger than the
other two) and numbers of access points: LBldg has 131 access points,
MBldg has 36, and SBldg has only 10. Since approximately twice as many
users visited LBldg as did MBldg, LBldg has a much smaller ratio of
users to access points (around 7 versus 16 and 30 for MBldg and SBldg,
Figure 5 shows the maximum number of users
simultaneously associated with each access point. Some access points
see few simultaneous users: 40% of access points never see more than
10 users, while other access points see as many as 30 simultaneous
users. Some of the access points with the highest numbers of
simultaneously associated users correspond to large auditoriums and cafeterias.
University campuses [8,9,15] and large-scale
public networks  also see great disparity in the
average and maximum number of users handled by access points. This is
not surprising as these values depend on the popularity of certain
locations (auditoriums or cafeterias for example). Hence, except for
small-scale networks , popularity
differences appear in all environment studied. On a small scale,
Balachandran et al.  find that users are
distributed rather evenly across access points.
Given the regular work schedule of our corporate population, access
points are idle (no user is associated with them) a large fraction of
the time when weekends and nights are considered. However, access
points are also idle some fraction of the time during normal working
hours. Figure 6 shows the fraction of idle time for
cumulative fractions of access points in each of the three
buildings. Most access points are used almost all the time during a
work week. For both MBldg and SBldg, 75% of access points are idle
less than 10% of the time. However, 10% of the access points in
LBldg are still idle over 75% of the time. On the university campus
that Kotz and Essien study [8,9], over one third of
access points are idle on a typical day. Hence, in all
environments, good coverage requires deploying resources, even in
locations where they are seldom used.
Figure 4: Fraction of users seen at each access
point throughout the whole trace. The fraction is computed with
respect to users who visited each building. The figure shows a
wide disparity across access points and across buildings.
Figure 5: Maximum number of users ever simultaneously
associated with each access point. The figure shows a wide disparity
across access points.
Figure 6: Fraction of time that access points are idle during a
normal work week (Monday-Friday, 9am to 6pm). Most access points are
used almost all the time. However, a few access points are idle
large fractions of time. The fraction of time was computed as the
fraction of samples without any users.
4.2 Data transferred
Figure 7 shows the total amount of data
forwarded through each access point during the trace. Access points
are ordered by increasing amount of data they forwarded, and the
cumulative fraction of access points is indicated for each amount. Due
to space limitations, we only present results for traffic going from
access points to wireless users. The graph for the opposite direction
is similar, though with slightly lower values. The amount of data
forwarded varies considerably across access points (from close to 0 up
to 21GB1), indicating an uneven usage of resources.
Figure 7: CDF of the amount of data transferred from each
access point to wireless users over the duration of the trace. The figure
shows an uneven usage of resources. Data from August 7th was ignored
in this computation as all access-point counters were reset by a third
party around 19:40 that day.
Figure 8 shows the average throughput of each access point. We
define access point throughput or load as the total amount of
bytes an access point forwards for any associated user in
either direction over a given
period of time. We computed these averages
in two steps. For each access point, we first computed the average
throughput for each sampling interval (interval between two
consecutive polls of the same access point). We then computed the mean
of these values. Figure 8 shows that much
more data is transferred on average at some access points than others
(with small errors on these averages, as shown in
Figure 8(b)). We obtain similar graphs for the
other two buildings. In the following sections, we discuss these
throughput differences and examine factors that influence access
Figure 8: Average throughput for access points in LBldg. Only intervals
with users were considered in averages. Access points are ordered
by decreasing average.
4.2.1 Correlation between number of users and load
Figure 9 shows access point throughput for
various numbers of associated users for MBldg. Throughputs were
computed over individual polling intervals. The figure shows that
little correlation exists between the number of users and access point
throughput. For more than 14 users, the outliers (99th percentile)
seem to decrease, but this is due to the smaller number of samples
with so many users. We show the results for MBldg as access points saw
highest numbers of simultaneously associated users most
frequently. The graphs for the other buildings show even less
We computed the correlation coefficients for each building, and found
0.10, 0.20, and 0.15 for LBldg, MBldg, and SBldg
respectively. For intervals where access point load exceeded 100Kbps,
the number of users and the load are even less correlated (-0.14,
0.03, and -0.06). This phenomenon can be explained by noticing
that most users are passive most of the time. When only passive users
are present, increasing their number slightly increases the
load. However, as soon as some users become active (i.e., start
transferring large amounts of data), they drastically increase the
average throughput and the influence of other users becomes
We located a few access points that had the higher average
transfer rates. They were our "dining conference room,"
laboratories, and conference rooms serving small meetings and
teleconferences. These locations differ from most popular and crowded
locations corresponding to cafeterias and auditoriums.
Figure 9: Throughputs measured at access points in MBldg for various
numbers of associated users. 50th, 75th, and 99th percentile shown
(only samples over 100Kbps were taken into account). The figure shows
that little correlation exists between the two numbers.
Tang and Baker  find that: "Usually, the throughput as
a whole increases as the number of users increases" (i.e., the total
throughput through routers increases with the number of
users). However, they find that: "the maximum throughput is achieved
by a single user and application." Kotz and
Essien [8,9] also notice little correlation
between the number of users and the amount of traffic going through
access points. They find the largest numbers of users at access points
located in lecture halls, while most traffic comes from
residences. However, it is not clear from their results whether the
difference is attributable to the extra amount of time that users
spend daily on the network at residences or their level of activity at
these locations. Balachandran et al.  point
towards the fact that: "load distribution [...] does not directly
correlate to the number of users at an access point." Indeed, they
find that peak load is not achieved when the maximum number of users
are present. They also find that although the number of users is
almost constant, load varies considerably over time. We confirm their
conclusion on a larger scale. Additionally,
Figure 9 shows that the number of users
and the load are rather uncorrelated regardless of whether the
load is high or not.
We conclude that offered load and number of users associated with
an access points are weakly dependent in our environment, but also in
the other environments studied.
4.2.2 Correlation between time of day and load
Figure 10 shows access point throughputs
registered on various hours of the day. Throughputs were computed
separately for each access point and each polling interval. The value
was then associated with the hour of the first poll. The figure shows
that little correlation exists between time and throughputs other than
the fact that sometimes users are not present on the network, as shown
earlier on Figure 2. The correlations
coefficients between time of day and load are 0.016, 0.020, and
0.030 for LBldg, MBldg, and SBldg respectively. Similarly, in the
trace presented by Balachandran et al. , the
offered load oscillated between 0Mbps and 2Mbps as long as users were
present on the network (during morning and afternoon sessions),
showing little correlation between load and time of day.
Interestingly, in MBldg, we found that the few users who stay later at
night generate only little activity whereas in LBldg, a lot of
activity persists on the network until midnight (as shown on
Figure 10). This shows a difference in the
characteristics of the wireless population that did not appear when
examining only the number of users on the network
(Figure 2). Also, for all buildings, users
detected around 3am to 6am were idle laptops left on in offices, since
no activity was detected during these periods.
Even though load on access points is not directly correlated with the
time of day, it may be influenced by specific events such as regularly
scheduled meetings. For instance, for one of the access points, most
peaks (above 2Mbps) occurred regularly between 12pm and 2pm. However,
only few access points showed such clear patterns.
Figure 10: Throughputs (per polling interval)
measured at every access points on each hour of the day on every
workday in LBldg: 50th, 75th, and 99th percentile (of samples with values
over 100Kbps) shown. No correlation
seems to appear between throughput and hour of the day.
4.2.3 Personal user differences
Personal user differences are another factor influencing access point
load: Some users are more active than others. To appreciate these
differences, we compare the average rates at which users transfer data
(in either direction). For any two consecutive polls, we compute the
average transfer rate of each user who remained associated with the
same access point over the interval. We then computed the average of
all values for each user to get the overall average for that user.
We compare individual average traffic rates to determine personal
differences in user's level of activity.
Figure 11 shows a great disparity among
users. Some users have quite high average traffic rates while others
are hardly ever active. Except for passive users (transfer rate under
10Kbps), the average user transfer rates follow
a power law distribution. For both MBldg and SBldg, we find a similarly shaped
distribution with a 10Kbps threshold under which the distribution does
not follow a power law anymore.
Figure 11: Distribution of average individual user transfer rates in LBldg. Averages
follow a power law, except for passive users with average transfer
rates under 10Kbps.
Whenever users with high network usage characteristics appear, an
access point may expect its load to increase
significantly. Figure 12 shows the
fraction of the throughput due to users whose average data transfer
rates are above 0.04Mbps. Access point indices correspond to those of
Figure 8 (i.e., decreasing average access point
throughput). These users represent only 10% of all users however they
account for over 40% of the bandwidth usage at over 30% of access
points. We obtain similar graphs for the other buildings. We also
looked at users whose average rate was above 0.02Mbps. They
represented 20% of all users, but they were responsible for over 40%
of load at 60% of all access points.
Tang and Baker  find some differences among what they
call user sub-communities (users whose offices are grouped around
different access points). They find that, in general: "While the wings
with most users (2b and 3b) also have the highest peak throughput, the
users on the 3b wing attain that throughput more often." Other than
this general finding, few conclusions are drawn in the other studies
about personal user differences. We find that these differences
are significant. Since our trace monitored
1366 users as they worked in offices, attended meetings, and relaxed in
common areas, we believe that personal differences will extend to
other environments as well.
Figure 12: In LBldg, fraction of total throughput attributable
to users with average
transfer rates higher than 0.04Mbps. Access point indices follow those in
Figure 8 (decreasing average throughput).
4.2.4 Influence of access point location on load
Figure 12 shows that access points with
high average throughputs (those at lower indices in the figure) owe
slightly larger fractions of their throughputs to the most active
users. Active users also use a large fraction of bandwidth at
locations with little total activity. Therefore, the fact that some
access points have much higher average transfer rates than others is
also due to other factors than which users are present. In this
section, we analyze the impact of access point location on its load by
testing whether location influences users' level of activity.
To determine whether user's level of activity depends on location or
not, we perform a one-way classification analysis of
variance . The factor that we are investigating is
location. The hypothesis is that there is no effect of location on a
user's data transfer rate. For each user, this analysis method
compares the distribution of the rates achieved at each location
visited (F-test ).
We only examine users who visited at least two locations and whose
average transfer rate was above 10Kbps. For LBldg the classification
is statistically significant for 27% of users. We reject the
hypothesis of location neutrality in all these cases, and conclude
that, for these users, location significantly affects transfer rate.
Similarly, we obtain a significant result for 32% of users in MBldg
and 23% of users in SBldg. We reject the hypothesis for all of
them. Hence we conclude that user transfer rates are
affected by the location.
From anecdotal evidence, we know that, in our corporation, during
talks held in large auditoriums, users mostly check their email or
browse the Web. During conference calls held in small rooms, users go
over presentations and download attachments pertaining to the meeting,
hence using much more bandwidth. We plan to investigate the
relationships between applications and location in future work.
Other studies also seem to find some relationship between location and
activity level, mostly because location determines the type
of activities that users pursue. Balachandran et
al.  never see peaks greater than 0.57Mbps for
any user. Kotz and Essien [8,9] find that the
daily throughput per MAC address varies greatly between buildings,
with residences seeing much more traffic than social locations. They
do not indicate whether the differences are attributable to higher
throughputs or to the length of time that users spend in each
location. However, they do find differences in the types of
applications predominantly used in each campus building.
4.2.5 Access point peak throughput periods
In our data set, we observed many polling intervals where average
access point throughputs exceeded 3Mbps. We observed extremely few
intervals with 5Mbps or higher averages. Therefore, in this section,
we present results for intervals averaging over 4Mbps. We call such
intervals peak throughput periods or peaks. We find that peaks
last short periods of time and seem highly correlated with
location. However, the network we studied was well provisioned and did
not seem to experience much overload.
Figure 13 shows the number of polling intervals
(consecutive or not) where the average transfer rate exceeded 4Mbps
for each of the 131 access points in LBldg. In the figure, access
points are ordered by decreasing number of peaks. Some access points
experience peak throughput periods quite often while the transfer rate
at others never exceeds 4Mbps. A few of these peaks
lasted over an hour, but 48% of them lasted only for one polling
interval in both LBldg and MBldg. The fraction was 64% in SBldg.
In , Tang and Baker found that in their network,
throughputs greater than 3Mbps were due mostly to a single user rather
than distributed across several users. They also found that some
locations were seeing significantly more peaks than others.
We also looked at how often more than one access point experienced a
period of high load. For LBldg, there were 188 events where load
exceeded 3.5Mbps at some access point. 50 of these events happened at
the same time or within a few minutes of one or two other events.
However, since we do not track access point location, some of these
simultaneous events are probably unrelated. Hence, most peaks affect
a single access point at the time.
Figure 13: Distribution of the number of high data transfer rate intervals
across access points. Some access points see high transfer rates quite
often while most access points never see high transfer rates (access
points are ordered by decreasing number of peaks).
We conclude that individual user differences have a high impact on
access point load: 20% of users account for 40% of the data
transferred at over 60% of the access points. Access point location
also influences load, but much less. Popular locations see many
simultaneous users and a larger fraction of all users, but the number
of users does not influence load significantly. Unpopular locations
remain idle most of the time. Time of day influences the number of
users present on the network, but it does not influence access point
load significantly, even during the night. Finally, many network
usage characteristics such as the uneven distribution of users and load
across access points, and the differences in location popularity, are
independent of whether the network is deployed on a university campus,
a corporation or at a conference.
5 User mobility characteristics
In this section, we examine user mobility characteristics and compare
our results with the characteristics found in the other studies. We
assigned a home building to each user corresponding to the
building where they spent most of their time.
For each building, Table 1 shows the fraction of users
who visited only that building, either of the other buildings, or all
buildings. Most wireless users stay within one building, but a
significant fraction (20% to 45%) move between two or more
buildings. This is mostly the case for MBldg and SBldg, located near
each other. Only a small fraction (up to 11%) of users visit all
three buildings. These mobility patterns are much more constrained
than those found on a university campus [8,9] where
the median user in their trace visited five buildings. The difference
is due to a higher concentration of resources (libraries, conference
rooms) within each of our corporate buildings. Also, many university
users both work and live on campus and visit different campus
locations for each type of activity.
Table 1: Fraction of users who visited one, two or all
three buildings. Each user is counted with the
building where the user spent most of his or her time.
Figure 14 shows the number of access points each user
visited during the trace. The figure shows cumulative fractions of
users who visited increasing numbers of access point. We find that
50% of users in SBldg, MBldg, and LBldg visited respectively 3, 9,
and 7 access points or fewer. These numbers include users who use
their wireless cards only a few days in the trace. On the other hand,
50% of users visit between 7 and 40 access points, with one user
visiting as many as 50. University campus users show even greater
mobility disparities than corporate users: the median corporate user
visits a similar number of access points as the median university
user [8,9], but the tails of our distributions
(Figure 14) are shorter. Hence, there might exist small
differences between user populations, but the differences might also
be due to the fact that Kotz and Essien [8,9]
study many users while attending activities other than working.
Figure 14: Number of access points visited by users during the
Figure 15 shows the distribution of the number of
access points visited daily (50th, 75th, and 99th percentiles). The
daily values are lower than the cumulative values presented in
Figure 14, showing that users visit different locations
on different days. On a daily basis, the least-mobile users (up to the
50th percentile) visit up to three access points in a single day. Most
users (up to 75% of them) visit up to 3 or 5 access points, and the
most-mobile 25% of users visit between 5 and 25 access points in a
single day. As in our study, Tang and Baker  also find
differences in user mobility, categorizing many as stationary, some as
somewhat mobile, and a small fraction as highly mobile. Hence, in
different environments there exist large personal differences in
user mobility, with most users spending a large fraction of their time
at one location.
Figure 15: Distribution of number of access points visited
by users each day of the trace. The 50th, 75th, and 99th percentiles
are represented. Note that graphs for MBldg and SBldg are slightly
offset along the x-axis to improve readability.
|LBldg ||MBldg ||SBldg |
|one bldg ||81% ||55% ||72% |
|two bldgs ||16% ||34% ||26% |
|three bldgs ||3% ||11% ||2% |
5.1 Home location and guest location
Since most users are stationary a large fraction of the
time, we compare user behavior in the location where they
spend most of their time - their home location - with their
behavior in other locations - guest locations.
To determine each user's home location, we could simply identify the
access point with which they are most frequently associated. However,
visitors or users who use the Ethernet when working in their offices
should not be assigned a home location. Therefore, we fix a threshold
on the fraction of time that a user must spend with an access point
for it to be considered the user's home location. We computed home
locations for thresholds of 30%, 40% and 50%. With 30% or 40%
thresholds, a few users who divided their time rather equally among
various buildings ended-up with a home location in the wrong building.
With a 50% threshold, 10% to 25% of users did not have a home
location, but all home locations were within buildings where users
spent most of their time. We therefore chose to use a 50% threshold
to find user home locations. Figure 16 shows the
cumulative distribution of the fraction of time spent by users at
their home location. Users spend up to 100% of their time at their
home location, with half the users spending at least 60% of their
time there. Given the low daily user mobility found in the other
studies, we expect users in other environments to show similar
distributions of the amount of time spent at a single home location.
Figure 16: Fraction of time that users spend at their home
locations. The fraction of time was measured as the fraction of
samples where the user was seen at its home location.
We find that the median user transfers around 20 MB in a single day at
his or her home location which is similar to the amounts of data
transferred by university users [8,9]. At guest
locations, users transfer approximately half that amount. However,
since, by definition, users spend a large fraction of time at home
locations, time plays a leading role in this
difference. Figure 17 shows average daily transfer
rates for home and for guest users (computed as the total amount of
data transferred by a user divided by the total amount of time the
user spent associated with home or guest access points during that
day). Median values are similar for both guest and home users, but
outliers (90th percentile values) show higher activity at guest
locations. Hence, mobility does not seem to have a negative
impact on user transfer rates.
Figure 17: Average daily data transfer rates per user, at home (top) and
at guest (bottom) locations. The 10th, 50th, and 90th percentiles are represented.
To better model the mobility of a user population or that of an
individual user within the population, we compute two metrics:
access-point prevalence in user traces and user
persistence at various locations. These notions are motivated by
Paxson's analogous definitions . These two metrics
characterize mobility patterns independently of the duration of the
trace and independently of the amount of time that users spend on the
network. We start by presenting prevalence metrics. We discuss
persistence in the following section.
Access-point prevalence in a user's trace is the measure of the
fraction of time that a user spends with a given access point. If a
user visits an access point frequently or spends a lot of time at the
access point, the prevalence of this access point in the user's trace
will be high. Home locations therefore have high prevalence values
whereas guest locations have lower prevalence values.
The prevalence distribution for a network is a matrix where each row
corresponds to an access point and each column corresponds to a user,
as illustrated in Figure 18. We compute one
prevalence matrix for each building to compare mobility within each
building. Figure 19 shows the probability
distribution of prevalence values from the LBldg matrix. Zero-value
prevalences have been discarded from the graph as most users visit
only a few access point (so zero-value prevalences dominate). The
graph shows that users visit a few access points frequently
(prevalences higher than 50% have non-zero probabilities) while
visiting most access points rarely (prevalences of 0% to 5% are
frequent). More precisely, the prevalence probability distribution
follows a power law with a low exponent as shown in the figure. We
obtain almost identical graphs for MBldg (with the same power
law). For SBldg, we find a significantly larger fraction of
prevalences close to 1, pointing towards lower mobility within that
building. Hence, for SBldg, we find that the distribution follows a
power law only for prevalence metrics in the range [0,0.9]. The
difference is most probably due to the smaller size of the building:
there are only 10 access points in SBldg.
Figure 18: Prevalence matrix for a network of n users and k access points.
Figure 19: Probability distribution of prevalence values for LBldg. Zero
valued prevalences are not counted. The other values are in bins
of size 5%. The distribution follows a power
law with a low exponent.
Given these distributions, for each building, we characterize each
user with two numbers: the maximum prevalence and the median
prevalence. For a given maximum prevalence, the median is inversely
proportional to the mobility of the user. The more access points a
user visits, the lower the median prevalence.
With these two measures, we categorize users into five groups as shown
in Table 2. By increasing mobility, the categories
are: stationary, occasionally mobile, regular, somewhat mobile, and
highly mobile. Stationary users stay with a single access point almost
all the time so their maximum prevalence is high and their median is
equal to their maximum. Occasionally mobile users spend most of their
time with a single access point and visit others infrequently. Their
maximum prevalence is high, while their median prevalence is low. As
the user spends less time with a single access point, their maximum
prevalence decreases. We categorize these users as either somewhat
mobile or highly mobile. Finally, regular users alternate regularly
between a few access points so both their median and maximum
prevalences are medium. In all three buildings, around 40% of users
are only occasionally mobile. Few users, however, are totally
stationary (around 10% for LBldg and MBldg). Users appear more
stationary in SBldg, probably due to the small number of access points
in the building (only 10). A considerable fraction of users (10% to
40%) is somewhat mobile, but only a few users are highly mobile.
|U1 ||U2 ||... ||Un |
|AP1 ||Prev1,1 ||Prev2,1 ||... ||Prevn,1 |
|Ap2 ||Prev1,2 ||Prev2,2 ||... ||Prevn,2 |
|... ||... ||... ||... |
|APk ||Prev1,k ||Prev2,k ||... ||Prevn,k |
Table 2: User categorization based on prevalence metrics. For each
building, the fraction of users who belong to each category is
indicated under the category name (LBldg, MBldg, and SBldg respectively).
|Median Prevalence (Pmed) |
|Maximum Prevalence |
Pmed ε [0,0.25)
Pmed ε [0.25,0.50)
Pmed ε [0.50,1]
Pmax ε [0,0.33)
|highly mobile |
|N/A ||N/A |
Pmax ε [0.33,0.66)
|somewhat mobile |
Pmax ε [0.66,1]
|occ mobile |
|N/A ||stationary (9%,10%,32%) |
Prevalence has one major limitation: It does not take into account the
amount of time users stay associated with access points. A user who
spends a week with an access point and another week with another
access point will have the same prevalence metrics as a user who
continuously moves between two access points. To complement the
prevalence metric, we compute user persistence at various
locations. The persistence is the amount of time that a user stays
associated with an access point before moving to another access point
or leaving the network. Since we poll access points every 5 to
10 min, we see only visits longer than that interval.
Given our distinction between home and guest locations, we plot the
probability distribution of persistence separately for each group (and
for each building). Figure 20 shows the
probability distribution of persistence values up to 400 min separated
between home locations and guest locations. Both distributions follow
power laws. For guest users, the exponents are higher indicating that
shorter sessions are more frequent. Additionally, we note a knee
in the probability distribution of guest users, which indicates two
different trends in persistence value distributions. The knee appears
around 100 min. After that threshold, the power law distribution
becomes even steeper indicating that longer sessions become even rarer
above that threshold. The two trends that appear may be explained as
follows. Short sessions are due to users moving around, attending
talks which last between 20 min and one hour, and also users taking
breaks in common rest areas. Hence, for up to an hour, various session
durations are registered. Longer sessions are mostly due to meetings
that last between an hour and two hours but hardly ever take longer
than that. We find an almost identical fit for both MBldg and SBldg
distributions with a slightly higher exponent after the 100 min knee
for MBldg and slightly different constants.
Figure 20: Probability distribution of persistence values up to 400 min
for home (bottom) and for guest users (top) in LBldg. Above 400 min, the probability
is close to negligible. Since our sampling interval is 5 min and often
stretches slightly above that value, most persistence values up to
5 min are rounded up to the 5 min-10 min interval.
Balachandran et al.  find that user session
durations follow a General Pareto Distribution with a shape parameter
of 0.78. This is equivalent to a power law distribution with
exponent 1.78 (i.e., [ 1/(x1.78)]). This distribution is
closest to what we obtain for guest users, which is what we might
expect since during a conference users are not at their normal home
Finally, we compare each user's median persistence at home and at
guest locations. Figure 21 shows the
scatterplot obtained for LBldg. A few users have a home median
persistence lower than a guest median persistence. These are mostly
users regularly alternating among a few access points. In most cases,
persistence at home is equal to, or even one or two orders of
magnitude greater than, persistence at guest locations. For users
without a home location, median persistence varies within the same
range as for other users.
Figure 21: Scatterplot of median guest and median home persistence values
for users who visited LBldg. Users without a home location in LBldg
are assigned a home median of 1 min so they are also represented on
the graph. Home median persistence is almost always greater than guest
median persistence, often by as much as one order of magnitude.
In conclusion, the primary characteristic of user mobility is
that many users spend a large fraction of their time in a single
location. They visit this location frequently and stay for long
periods of time. When they move away, they do not reduce their data
transfer rates, but they spend short periods of time at any location
and they do not visit the same location frequently.
In this section, we discuss possible applications of user mobility and
network usage characteristics to wireless network deployment, to
workload generation, and to application design.
6.1 Wireless network design and deployment
Several approaches recently introduced new algorithms to relieve
"hot-spots" and dynamically balance load among access points. Cisco
access points  balance load between each other (within
an overlapping cell) using the number of users, their error rates, and
signal strength. Balachandran et al.  improve
load balancing by explicitly re-directing users to satisfy
pre-negotiated bandwidth range service agreements. Balancing users
across access points is important. As shown in
Section 4.2.5, even in a well provisioned network, access
points often experience periods of high demand lasting a few minutes.
In our trace, most of these peaks affected a single access point at a
time. Therefore, quickly load balancing users could often unload one
or two heavily-loaded access points.
Our analysis provides further information that may be helpful in
designing load balancing algorithms: a) we find high personal
differences in network usage; b) users with high average transfer
rates represent a small fraction of all users; and c) any access point
sees only a small fraction of all users. Additionally, location seems
to play some role in user's level of activity. Given these network
usage characteristics, we propose that access points keep mobility and
network usage figures for each moderately active client. They could
then better react to overload because they would know how long
associated users were likely to stay and what amount of resources they
were likely to require. Of course, to protect user privacy, access
points should neither make this data openly available nor communicate
it to other access points.
For each associated user, access points already keep counters of
bytes and packets transferred. For users transferring more than 2Mbps
or 3Mbps, each access point could preserve a running average of data
transferred as well as the user's peak transfer rates. Each access
point could also preserve a running average of persistence values,
updating them as follows every time a user de-associates
from an access point: Puser,t = a* Puser,t-1 + (1-a) Duser, where Puser,t-1 is the
previous average persistence for a user, Duser is the amount of
time the user remained associated this time, and a Î [0,1] is a factor adjusting the importance of history over the
latest value. The average transfer rate, Xuser,t, could be
computed in a similar manner. In a situation of overload, an access
point could choose to re-direct a user to another location if this
user had a history of using a lot of resources (high X), but not
staying very long (low P). The access point would not waste
resources redirecting users that are always idle. It would also avoid
re-directing users who stay with the access point for prolonged
periods of time (perhaps those who have their office there).
Access point popularity is another useful metric for network
deployment. To provide coverage, we find that some access points are
deployed in locations where they are seldom used. These access points
could self-tune to reduce their power consumption or increase their
coverage (while decreasing maximum data rates). Additionally, access
point could compute and compare user persistence and prevalence
metrics to determine their relative popularity. Such relative
popularity metrics, coupled with load metrics, would help system
administrators determine the most appropriate locations for new access
points. Ideally, system administrators want to deploy extra access
points before users see performance degradation, so they need other
metrics to determine where new access points should be added.
6.2 Refining workload generation
Several tools exist to simulate wireless
networks [11,12,14]. They model characteristics of
the wireless network quite accurately, but they require users to
dynamically define the location of a node or to define
trajectories. However, tools could use the concept of "home
locations" as well as power law distributions for persistence and
prevalence to simulate user mobility automatically. Persistence could
serve to determine how long a user stays at a location on average,
whereas prevalence could serve to determine which location the user
Additionally, we find it reasonable to use scaled down population
models to determine the number of users present on the simulated
network at any point in time.
6.3 Guiding application design
Knowledge of network usage characteristics may also prove helpful in
designing applications for mobile environments. For example, since
users spend a large fraction of time at their home location the design
of mobile systems might benefit from optimizations for this particular
usage pattern. An application may, for example, keep information
about each user at their home location.
Additionally, in all environments studied, most users visit only a few
locations during a day, and spend a large fraction of their time at their
home location before visiting other locations on subsequent days. This
specific mobility pattern should also influence design decisions. For
example, when synchronizing application data, it would be an appropriate
design choice to designate the home location as the "master"
In this paper, we presented the analysis of results from a four week
trace gathered in a large corporate environment and showing the
network usage of 1366 different users. Analyzing mobility and network
usage, we find several characteristics, many of which are
shared by users in other environments such as university campuses and
In spite of increasing popularity of wireless networks, the number of
days each user appears on the network is highly variable among
different users. However, the general patterns in the numbers of users
per day and the number of users per hour follow a regular office
Load is unevenly distributed across access points. Some, located in
popular areas such as large auditoriums, often see high numbers of
users simultaneously associated with them (up to 30). Others, located
in less visited areas, are usually idle. We find that the amount of
traffic at an access point is weakly dependent on the number of users
present or the time of day. Most traffic is due to a small fraction of
active users: the most-active 10% of users are responsible for more than
40% of the data transferred at 30% of locations. Load is also
somewhat related to access point location, as we find that location
impacts user transfer rate significantly for 30% of active
users. Additionally, user's average level of activity follows a power
We introduce persistence and prevalence to characterize
and classify user mobility. Probability distributions of both metrics
follow power law distributions. Persistence at guest locations also
has a higher exponent than persistence at home locations, clearly
showing that users associate with access points longer when staying at
their home locations. Using prevalence, users can be categorized into
mostly stationary, occasionally mobile, regular, somewhat mobile, and
highly mobile. We find that 50% to 80% of users fall into the
occasionally and somewhat mobile categories. Finally, we find that
mobility does not influence user level of activity on the network.
However, most devices in our study were laptops; mobility results may
become different as PDAs and other small devices become more popular.
We plan to repeat this study, using SNMP in combination with syslog
and tcpdump as well as monitoring software on mobile
devices. Our goal is to get more detailed information on network usage
and develop more detailed models of both mobility and network usage.
We thank Apratim Purakayastha for his helpful suggestions throughout
the project. We thank Drew Wyskida for helping us gain access to the
access points. We gratefully acknowledge the help of David Kotz with
SNMP and data collection scripts. We thank Hari Balakrishnan and Chuck
Blake for their suggestions on result presentation and analysis. We
also thank everyone who read and commented on draft versions of this
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1Throughout the text, 1MB = 220 Bytes
and 1 Mbps = 2-3 MBps
File translated from
On 19 Feb 2003, 19:06.