Large Accidental Wireless Networks and the Digital Divide
Paul Beckman
San Francisco State University
1600 Holloway Avenue, San Francisco, CA, 94132
pbeckman@sfsu.edu
Joshua Mindel
San Francisco State University
1600 Holloway Avenue, San Francisco, CA, 94132
jmindel@sfsu.edu
Sameer Verma
San Francisco State University
1600 Holloway Avenue, San Francisco, CA, 94132
sverma@sfsu.edu
Abstract
This paper examines the existence and extent of LAWNs (large accidental wireless
networks) in several different neighborhoods in a large metropolitan city in the Western
United States. A LAWN is a geographically large wireless network infrastructure
constructed through the uncoordinated and unintended actions of individual residents of a
neighborhood when those residents install unsecured home wireless networks. The
macro-level result of these individual actions is a high-speed wireless network that
extends throughout a neighborhood and is available for access by any casual passerby. A
second focus of this research project was investigation of the possible existence of a
relationship between socio-economic level and LAWN coverage.
Data were collected about the wireless networks in seven neighborhoods of differing
socio-economic level in the city of Oakland, California. An initial data analysis shows
that there is a relationship between socio-economic level and LAWN coverage. Those in
lower socio-economic level neighborhoods have, in general, fewer wireless networks per
house.
Keywords: digital divide, wireless networks, community wireless networks, accidental
wireless networks
Introduction
High-speed wireless networks using the 802.11* network protocols, hereinafter called
“Wi-Fi access points”, or APs, are now commonplace in many residences in the United
States. Such wireless network hardware devices generally can be purchased through
local computer sales retail outlets for less than $100 and installed in less than an hour.
These devices then enable the resident to wirelessly access their own personal high-speed
Internet connection (typically via a cable modem or DSL connection) within a range of
approximately 100 meters. Such hardware devices could also be used merely to
wirelessly connect computer devices within a home without any connection to the
Internet, although that is not the focus of this research project. As both high-speed
Internet access increases throughout the United States and the price of AP hardware
devices drop, it can be presumed that more such devices will be installed.
This topic is not new from a technological standpoint, as high-speed wireless networking
consumer products have been available for several years, but it is a fairly new topic for
academic investigation. From the legal and economic perspective of such groups of
wireless networks, Benkler (2002, page 7) argues that as the number of residential
wireless network nodes increases “open wireless networks will be more efficient in the
foreseeable future.” Taking the viewpoint of constructing a network architecture,
Agarwal et al. (2004) comment that any type of “wireless grid network” may suffer from
limited standards, protocols, quality of service, bandwidth, and network stability.
Meinrath (2005), however, suggests that a major research agenda has only recently begun
in this novel area of wireless networks, and that much work needs to be done that focuses
on the overarching topics of policy assessments, technical research areas, and social
inquiries. Greater knowledge in each of these areas will then lead to greater
understanding and support of open spectrum concepts that better serve the general public.
Related to both of Meinrath’s topics of technical research and social inquiry, Verma and
Beckman (2004) have shown that when a significant number of individuals within a
specific geographic area such as a residential neighborhood purchase and install their
own APs, at some point, a high enough density of those APs will be reached to support
full high-speed wireless network access throughout that neighborhood. Above that
critical density (approximately one AP every 100 meters), a high-speed wireless network
infrastructure (that they call a LAWN, or Large Accidental Wireless Network) will exist
regardless of the intent of the individual AP installers. One additional requirement for a
LAWN to evolve is that the critical density of APs must be reached with enough APs that
do not have security implemented. If this occurs, a casual passerby (or “LAWN
exploiter”) will then be able to connect to the Internet wirelessly and at relatively high
speeds throughout that neighborhood. Therefore, a LAWN can be defined as a
geographically large wireless network infrastructure constructed through the
uncoordinated and unintended actions of individual residents of a neighborhood when
those residents install unsecured home wireless networks. This defines a residential
wireless network infrastructure that is similar in nature to those investigated by Aditya
(2005) as “chaotic wireless deployments”, whose research primarily examined the impact
on end-client performance in such networks.
It is even possible that a LAWN exploiter would be able to move throughout such a
neighborhood while maintaining a continuous high-speed connection to the Internet, as
their computer connection “hopped” through the APs of that neighborhood. This would
occur as their computing device disconnected from the most recently passed AP and then
connected to the next subsequent unsecured AP. This possibility/capability may
therefore have serious implications for those investigating both mobile commerce and
location-based commerce, as the LAWN provides the underlying wireless infrastructure
that supports continuous high-speed Internet connections to mobile consumers traveling
through residential neighborhoods.
Note that theoretically, an AP density of one AP every 200 meters might be enough to
support a LAWN, but this would require that the LAWN exploiter remain connected at
the last 100th meter of one AP and then immediately be able to connect to the first meter
of the next AP. While this is possible, it would require only slight variations in the
physical location of an AP to remove full LAWN coverage. Therefore, this research
project will presume a required AP density of one AP every 100 meters to support full
LAWN coverage. Countering this effect is the continual improvement in residential
wireless network technology. The emergence on the consumer market of APs with
increasingly greater range would allow a LAWN exploiter to remain connected in areas
with a lower LAWN density, if the APs supporting that LAWN had greater geographic
range. (Note that in the United States, the Federal Communication Commission allows
unlicensed hardware to operate in the radio frequency band used by APs, but it does
tightly control the power of such devices.)
This research investigated the extent to which LAWNs exist across seven neighborhoods
in a large city in the western United States (Oakland, California). A secondary goal of
the research project was to determine if there is a relationship between the extent of
LAWN coverage in a neighborhood and the socio-economic level of that neighborhood.
Such relationships are called “digital divides” and refer to the real or perceived difference
in access to some form of information technology across socio-economic levels. To that
end, data about home APs were collected in seven neighborhoods of varying socioeconomic level. That data supported analyses that determined the existence, extent, and
density of LAWNs as well as any possible relationship between AP density and socioeconomic level.
Methodology
The research project commenced with the construction of an “AP-sniffing” unit. This
unit was comprised of a portable computer containing a “Wi-Fi” card, a GPS locationtracking unit, and NetStumbler (2003) software designed to trap and store relevant
characteristics of the APs that it detects. The unit was then placed in a vehicle and that
vehicle was driven through seven neighborhoods in Oakland, California.
The first step in selecting neighborhoods was to choose areas comprised of one socioeconomic level as determined by income level shown in 1999 United States census
records. To be used in this project, the entirety of a neighborhood had to be of the same
socio-economic level. The second step in selecting neighborhoods was to ensure that
each neighborhood contained the same type of housing density as all other
neighborhoods. Housing density was determined by examining satellite images of each
of the neighborhoods. Neighborhoods containing single-family dwellings were chosen
because those types of neighborhoods were the only type to appear across all seven socioeconomic level areas. (Note on experimental design constraint: Both the large cities of
San Francisco and San Jose were discarded for this experiment because it was not
possible to find neighborhoods of 750 single-family dwellings comprised of residents in
the lowest socio-economic level. In these two cities, all neighborhoods of the lowest
socio-economic level residents had significant numbers of multi-family dwellings.)
It is important that each neighborhood consist primarily of single-family dwellings
because a neighborhood with significantly more multi-family dwellings could bias some
research results. That is, a neighborhood containing many higher-density housing units
such as apartment buildings would also contain more individual households and therefore
more APs than an identical neighborhood comprised of only single-family dwellings
(assuming that the number of APs per household is related to socio-economic level and
not related to housing type, an assumption that was made but not validated). Recall that
LAWN density depends only on the number of APs per geographic area and not on the
number of APs per household. Therefore, if the number of APs per household is constant
within each socio-economic level, LAWN density would be higher in a neighborhood of
high-density housing and lower in a neighborhood of lower-density housing for
neighborhoods of similar socio-economic level. The difference in LAWN density would
therefore be due only to the higher density of households in the neighborhood of multifamily dwellings. This point also raises the issue of lot size per household, as larger lot
sizes increase the physical distance between homes and therefore the physical distance
between APs, which subsequently decreases the LAWN density.
This selection of neighborhoods, therefore, ensured that each neighborhood was
comprised of: 1) similar-density housing (i.e., similar number of households per dwelling
although not necessarily similar number of households per driven mile), 2) households of
similar socio-economic level within that neighborhood, and 3) households of different
socio-economic level than the other neighborhoods.
Since there were varying numbers of dwellings in each neighborhood, a count was kept
of the number of dwellings on each street that was driven. That total count was kept to
support an analysis that would show the number of APs per dwelling in each
neighborhood. Note that this calculation presumes that there are a negligible number of
dwellings containing two or more APs. As each AP is capable of supporting numerous
wireless devices, it was not expected that there would be many (or any) dwellings with
more than one AP within. Each neighborhood contained approximately 750 singlefamily dwellings so the results related to the number of APs per dwelling would not be
skewed by significantly fewer or greater dwellings across neighborhoods.
After each neighborhood was driven and all detectable APs had been found, the raw
datafiles from the network-sniffing software were converted into spreadsheet format for
analysis. The data in those spreadsheets were then analyzed to find patterns of interesting
AP characteristics such as number of APs per dwelling, percentage of APs installed
without security, percentage of APs installed without any changes to default settings such
as SSID, percentage of APs of each particular brand, and any other AP characteristic
deemed relevant.
For this initial analysis, wireless network density was measured as the number of
unsecured APs per mile and LAWN existence was operationalized as a density greater
than 16 unsecured APs per mile. Therefore, if any neighborhood showed an unsecured
AP density greater than 16 APs per mile, then it was concluded that a LAWN exists in at
least one neighborhood. Specifically, a LAWN density greater than 16 unsecured APs
per mile suggests that there is more than one unsecured AP every 100 meters and
therefore a casual passerby would be able to connect wirelessly throughout that
neighborhood.
The second step in the data analysis was to investigate the relationship between the
detected AP density and the socio-economic level of each neighborhood. This step
relates to the existence of a LAWN digital divide and required calculating the number of
unsecured APs per mile in each of the different neighborhoods. A graph of unsecured
APs per mile versus socio-economic level will show the effect of a LAWN digital divide.
For this part of the analysis, LAWN density and LAWN existence were operationalized
as described above; socio-economic level was operationalized as the median household
income for 1999:2000 for that set of census blocks existing in that neighborhood as
derived from United States Census Bureau data.
Analysis
The United States Census Bureau (2000) shows seven socio-economic levels represented
in neighborhoods throughout Oakland, California, measured as “median household
income for 1999”. These data subdivide median household income into census blocks in
the income ranges shown in Table 1. Note that the data in Table 1 show the median
income levels using the U.S. Census Bureau classing method of “equal intervals”. Other
classing methods could have been used, and would result in slightly different median
household income ranges.
Neighborhood Median Household Income
1
$0 - $21,165
2
$21166 - $42,331
3
$42,332 - $63,496
4
$63,497 - $84,662
5
$84,663 - $105,827
6
7
$105,828 - $126,993
$126,994 - $148,158
Table 1. Oakland, CA, Neighborhood Median Household Income Ranges
(1999:2000)
Analysis: LAWN Existence
The data collected from the neighborhood drives was used first to determine the density
of unsecured APs to determine the extent of LAWN coverage in each neighborhood.
Table 2 shows the number of unsecured APs, number of houses, and number of linear
street miles for each of the seven neighborhoods. It also shows ratios of APs per house,
houses per mile, and APs per mile. The ratios are shown because LAWN density
depends only on the number of APs per mile, not on the number of APs per house. If the
number of APs per house is high but the housing density is low, then LAWN density may
not be high enough to support full wireless network coverage. Both a higher number of
APs per house AND lower housing density are more likely to be found in higher socioeconomic neighborhoods. Conversely, if the number of APs per house is low but the
housing density is high, LAWN density may be high enough to support full wireless
network coverage. Both a lower number of APs per house AND higher housing density
are more likely to found in lower socio-economic neighborhoods. Obviously, the highest
LAWN density would be found in a neighborhood with a high AP per house ratio AND
high housing density, perhaps in a wealthy urban area.
Neighborhood # APs # houses # miles APs/house Houses/Mile APs/Mile
1
52
1130
5.04
0.05
224.30
10.32
2
38
686
2.83
0.06
242.80
13.45
3
75
734
3.47
0.10
211.41
21.60
4
76
666
2.68
0.11
248.29
28.33
5
126
713
5.46
0.18
130.64
23.09
6
78
735
5.89
0.11
124.73
13.24
7
124
629
6.57
0.20
95.77
18.88
Table 2. Number of Unsecured APs, Houses, Miles, and Associated Ratios per
Neighborhood
Analysis: LAWN Digital Divide
Unsecured AP density was compared to median household income range (for all
neighborhoods) to determine if there was a relationship between the two, thus indicating
a LAWN digital divide. A positive relationship between unsecured AP density and
socio-economic level would indicate that those in higher socio-economic levels have
greater access to LAWNs than those in lower socio-economic levels. A negative
relationship between unsecured AP density and socio-economic level would indicate that
those in lower socio-economic levels have greater access to LAWNs.
Results
Results: LAWN Existence
The LAWN density values of number of APs per mile shown in Table 1 indicate that,
with come caveats, LAWNs do exist in some of the areas. Neighborhoods 3, 4, 5, and 8
have AP densities above 16 per mile, indicating that a full wireless network exists in
those areas. Note however, that, for neighborhoods whose LAWN densities are just
slightly over the 16 per mile threshold, inspection of a geographic map showing exact AP
locations would be required to determine if full LAWN coverage existed. This is so
because the values reported in Table 1 are averages and there may exist in neighborhoods
pockets of higher and lower AP density which could result in incomplete coverage over
the neighborhood as a whole. In general, however, the data analysis shows that LAWNs
do exist in the city of Oakland, California.
Results: LAWN Digital Divide
There is an interesting effect that can be seen across the neighborhoods with regard to
LAWN coverage and the values that comprise it. As one might suspect, the number of
APs per house increases with socio-economic status (aside from the apparently
anomalously low AP density in neighborhood 6), while the number of houses per mile
generally decreases with socio-economic status. The combination of these two effects,
which results in the number of APs per mile, is that:
those neighborhoods in the middle of the socio-economic spectrum
have the highest LAWN density and coverage
The significantly lower number of APs per house in the lower socio-economic areas
overwhelms the higher housing densities found there, resulting in incomplete LAWN
coverage. Conversely, the significantly lower housing densities in the higher socioeconomic areas overwhelm the higher number of APs per house found there, also leading
to incomplete LAWN coverage. Therefore, it appears that a LAWN digital divide does
exist, but without a more detailed statistical analysis this statement cannot be made
unequivocally.
Conclusions
This research project was initiated first to determine if large accidental wireless networks
exist in a variety of neighborhoods in a major metropolitan area in the western United
States and second to determine if there are characteristics of installed APs (such as AP
density) that are related to different socio-economic levels. Results of data gathered in
those neighborhoods suggests that full LAWN coverage does exist in five of the seven
neighborhoods investigated, based on the average number of APs per mile in those
neighborhoods. Experimental results also show that a LAWN digital divide exists and is
the result of the interplay of the number of APs per house and the housing density in each
neighborhood. The highest LAWN density exists in those middle socio-economic
neighborhoods because they have a relatively higher AP per house density and a
relatively lower housing density. The lowest socio-economic neighborhoods have too
low of an AP per house density to overcome their high housing density and hence
provide full LAWN coverage. Higher socio-economic neighborhoods also have a barrier
to full LAWN coverage in their relatively low housing density. Even with a low housing
densities, however, one of the top two socio-economic neighborhoods does show a high
enough density of APs per mile to barely reach the minimum value for LAWN coverage.
The societal impact of this research is multi-fold. First, it is becoming more popular for
city governments to initiate projects that will provide full high-speed wireless networks
throughout their metropolitan geographic area, as is currently happening in at least the
cities of San Francisco (Kim, 2006) Philadelphia (WPNPC, 2005), and Toronto (AP,
2006). The results of this research show that the underlying infrastructure for such large
wireless networks may already be in place in some parts of some cities. It may be to a
city’s advantage to find a way to induce its citizens to leave their home wireless networks
unsecured to increase the spread of its LAWNs. There would need to be corresponding
changes in Internet Service Provider Acceptable Use Policies (Siau, 2002) to allow
individuals to open their Internet access to outsiders. There are also security issues raised
by opening wireless APs to outsiders, but these could possibly be addressed with
software solutions. Other issues associated with such city-wide wireless initiatives are
mostly procedural and not related to the costly task of constructing the wireless
infrastructure; this research project shows that the underlying infrastructure construction
issue has already been partially solved.
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