Localization for Mobile Sensor Networks ,
Lingxuan Hu, David Evans (University of Virginia, USA) 

ABSTRACT:
Many sensor network applications require location awareness, but it is often 
too expensive to include a GPS receiver in a sensor network node. Hence, 
localization schemes for sensor networks typically use a small number of seed 
nodes that know their location and protocols whereby other nodes estimate their 
location from the messages they receive. Several such localization techniques 
have been proposed, but none of them consider mobile nodes and seeds. Although 
mobility would appear to make localization more difficult, in this paper we 
introduce the sequential Monte Carlo Localization method and argue that it can 
exploit mobility to improve the accuracy and precision of localization. Our 
approach does not require additional hardware on the nodes and works even when 
the movement of seeds and nodes is uncontrollable. We analyze the properties of 
our technique and report experimental results from simulations. Our scheme 
outperforms the best known static localization schemes under a wide range of 
conditions. 


Practical Robust Localization over Large-Scale 802.11 Wireless Networks ,
Andreas Haeberlen, Eliot Flannery, Andrew Ladd, Algis Rudys, Dan Wallach, Lydia Kavraki (Rice University, USA)

ABSTRACT:
We demonstrate a system built using probabilistic techniques that allows for 
remarkably accurate localization across our entire office building using 
nothing more than the built-in signal intensity meter supplied by standard 
802.11 cards. While prior systems have required significant investments of 
human labor to build a detailed signal map, we can train our system by spending 
less than one minute per office or region, walking around with a laptop and 
recording the observed signal intensities of our building's unmodified base 
stations. We actually collected over two minutes of data per office or region, 
about 28 man-hours of effort. Using less than half of this data to train the 
localizer, we can localize a user to the precise, correct location in over 95% 
of our attempts, across the entire building. Even in the most pathological 
cases, we almost never localize a user any more distant than to the neighboring 
office. A user can obtain this level of accuracy with only two or three signal 
intensity measurements, allowing for a high frame rate of localization results. 
Furthermore, with a brief calibration period, our system can be adapted to work 
with previously unknown user hardware. We present results demonstrating the 
robustness of our system against a variety of untrained time-varying phenomena, 
including the presence or absence of people in the building across the day. Our 
system is sufficiently robust to enable a variety of locationaware applications 
without requiring special-purpose hardware or complicated training and 
calibration procedures. 


Particle Filters for Location Estimation in Ubiquitous Computing: A Case Study
Jeffrey Hightower and Gaetano Borriello

ABSTRACT:
Location estimation is an important part of many ubiquitous
computing systems. Particle filters are simulation-based probabilistic 
approximations which the robotics community has shown to be eective for
tracking robots’ positions. This paper presents a case study of applying
particle filters to location estimation for ubiquitous computing. Using
trace logs from a deployed multi-sensor location system, we show that
particle filters can be as accurate as common deterministic algorithms.
We also present performance results showing it is practical to run particle
filters on devices ranging from high-end servers to handhelds. Finally,
we discuss the general advantages of using probabilistic methods in location
systems for ubiquitous computing, including the ability to fuse
data from dierent sensor types and to provide probability distributions
to higher-level services and applications. Based on this case study, we
conclude that particle filters are a good choice to implement location 
estimation for ubiquitous computing.