<?xml version="1.0" standalone="no"?>
                    <!DOCTYPE div SYSTEM "/www/backend/www-xml-443/dtd/caidaML.dtd">
                    <!-- do NOT ERASE the DOCTYPE declaration! --><div>


<tr bgcolor="#f4f4f4">
  <td>
<font face="helvetica,arial" size="2">
<b>URL:</b>
</font>
</td>
  <td>
<font face="helvetica,arial" size="2">
<a href="http://portal.acm.org/ft_gateway.cfm?id=1862209&amp;type=pdf&amp;coll=GUIDE&amp;dl=GUIDE&amp;CFID=105494744&amp;CFTOKEN=22242868">http://portal.acm.org/ft_gateway.cfm?id=1862209&amp;type=pdf&amp;coll=GUIDE&amp;dl=GUIDE&amp;CFID=105494744&amp;CFTOKEN=22242868</a>
</font>
  </td>
</tr>


<tr bgcolor="#e9e9e9">
  <td>
<font face="helvetica,arial" size="2">
<b>Entry Date:</b>
</font>
</td>
  <td>
<font face="helvetica,arial" size="2">
2010-10-22


</font>
  </td>
</tr>


<tr bgcolor="#f4f4f4">
  <td>
<font face="helvetica,arial" size="2">
<b>Abstract:</b>
</font>
</td>
  <td>
<font face="helvetica,arial" size="2">
Knowing the geographical location of an Internet host is of importance to many of today's Internet services. In this
paper we focus on geolocating Internet hosts based purely on latency measurements. Existing latency measurement-based
geolocation techniques use the observed latencies from multiple landmarks to the target host to determine maximum bound or both
the maximum and minimum bounds of the geographical region where the target host is located. Due to the large variance of
Internet latency measurements, the region constrained based on such maximum-minimum bounds tends to be relatively large
resulting in large estimation errors. We propose a geolocation algorithm, GeoWeight, which improves the geolocation accuracy by
further limiting the possible target region by dividing the constrained region to sub-regions of different weights. The weight
assigned to a subregion indicates the probability of the target being in that sub-region; a higher weight indicating a more
probable region. By considering latency measurements from multiple landmarks and computing the resultant weights of overlapping
regions a better constrained target region can be obtained. This paper presents the GeoWeight algorithm and evaluates its
performance using both synthetic and real data by geolocating target hosts in North America. We compare GeoWeight with two
popular geolocation techniques, Octant and CBG, by geolocating the same set of targets. The results show that the GeoWeight
algorithm outperforms existing techniques.



</font>
  </td>
</tr>
</div>

