[Firebug-cvs] firebug/web spie.bib,1.5,1.6 spie_2004.tex,1.5,1.6 spie_reviews.tex,1.1,1.2

[<do...@us...>]

Update of /cvsroot/firebug/firebug/web
In directory sc8-pr-cvs1:/tmp/cvs-serv336

Modified Files:
	spie.bib spie_2004.tex spie_reviews.tex 
Log Message:
Collected Anshumans reviews into a single 
review document, updated references.

Index: spie.bib
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RCS file: /cvsroot/firebug/firebug/web/spie.bib,v
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--- spie.bib	19 Jul 2003 13:46:24 -0000	1.6
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*** 3,6 ****
--- 3,32 ----
  
  
+ @inproceedings{bulusu:n2001,
+   author = {Nirupama Bulusu and Vladimir Bychkovskiy and 
+ Deborah Estrin and John Heidemann},
+   title = {GPS-less Low Cost Outdoor Localization For Very
+ Small Devices},
+   booktitle = {21st International Conference on Distribued
+ Computing (ICDCS) 2001},
+   address = {Phoenix, Arizona},
+   month = {April},
+   year = {2001}
+ }
+ 
+ 
+ @inproceedings{bulusu:n2002,
+   author = {Nirupama Bulusu and Vladimir Bychkovskiy and 
+ Deborah Estrin and John Heidemann},
+   title = {Scalable, Ad Hoc Deployable RF-based Localization},
+   booktitle = {Grace Hopper Celebration of Women in Computing 
+ Conference 2002},
+   address = {Vancouver, British Columbia, Canada},
+   month = {October},
+   year = {2002},
+   url = {http://www.cs.ucla.edu/~bulusu/papers/Bulusu02a.html}
+ }
+  
+ 
  @TechReport{ganesan:d2002,
    author = 	 {D. Ganesan and B. Krishnamachari and A. Woo 
***************
*** 76,79 ****
--- 102,117 ----
    month =        June,
    year =         2003,
+ }
+ 
+ 
+ @inproceedings{simic:sn2003,
+   author = {Slobodan N. Simic and Shankar Shastry},
+   title = {Distributed Environmental Monitoring Using
+ Random Sensor Networks},
+   booktitle = {2nd International Workshop on Information
+ Processing in Sensor Networks, (IPSN) 2003},
+   address = {PARC, Palo Alto},
+   month = {April},
+   year = {2003}
  }
  

Index: spie_2004.tex
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RCS file: /cvsroot/firebug/firebug/web/spie_2004.tex,v
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*** 3,6 ****
--- 3,7 ----
  
  \usepackage{graphicx}
+ \usepackage{chicago}
  
  \setlength{\oddsidemargin}{0in}
***************
*** 98,104 ****
  \subsection{Previous work with outdoor sensors}
  
! One or two paragraphs describing other work with 
! wireless sensor networks outdoors, specifically,
! ~\cite{mainwaring:a2002,west:b2001,mehta:v2002,sichitiu:ml2003}.
  
  
--- 99,105 ----
  \subsection{Previous work with outdoor sensors}
  
! \input{spie_reviews}
! 
! \paragraph{Mehta and El Zarki}~\cite{mehta:v2002} ????
  
  
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*** 214,218 ****
  
  \bibliography{spie}
! \bibliographystyle{plain}
  
  \end{document}
--- 215,219 ----
  
  \bibliography{spie}
! \bibliographystyle{chicago}
  
  \end{document}

Index: spie_reviews.tex
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RCS file: /cvsroot/firebug/firebug/web/spie_reviews.tex,v
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--- spie_reviews.tex	19 Jul 2003 13:46:24 -0000	1.2
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*** 7,15 ****
  
  
  
! \paragraph{Sichitui et al.}~\citeyear{sichitui:m2003}
! investigates blah blah blah.
  
  
! \paragraph{Woo et al.}~\citeyear{wwo:a2003} investigate 
! etc etc etc
--- 7,164 ----
  
  
+ \paragraph{Bulusu et al.}~\citeyear{bulusu:n2002,bulusu:n2001}
+ have developed techniques for 
+ RF-based localiazation in sensor
+ networks. Their work can be divided into two 
+ distinct problem areas, namely localization, 
+ and beacon placement. They profess a GPS-less 
+ environments with beacon nodes that are 
+ deployed with their absolute location/position 
+ stored within them and sensor nodes that localize 
+ themselves based on proximity to a subset of beacons.
  
! Some of the salient characteristics of their approach are: 
  
+ \begin{itemize}
  
! \item an idealized radio model with perfect spherical 
! radio propagation and identical transmission range for all radios, 
! 
! \item  a localization algorithm that relies on a 
! connectivity metric threshold to decide
! which beacons to use in computation of a nodes 
! position estimate, which is the centroid
! of the beacons selected.
! 
! \item  measurement based adaptive beacon placement, 
! which can adjust the density of beacons
! and in turn increasing system lifetime, 
! and reduced excessive channel interference and 
! contention.
! \end{itemize}
! 
! As per their results the spherical radio model 
! correlates upto 90% with real conditions in 
! an outdoor uncluttered environment.
! 
! 
! 
! \paragraph{Simic et al.}~\cite{simic:sn2003}
! describe distributed computation 
! and estimation of a scalar field in a sensor 
! network. The scalar field could be anything 
! from temperature to amount or intensity of 
! light. They provide an algorithm and precise 
! theoretical analysis of it.
! 
! The basic idea of their algorithm is as follows. 
! Each node communicates with its neighbors
! and computes the maximal difference quotient 
! of the sensed scalar field. The estimate
! of the gradient at each node is taken to be 
! the vector in the corresponding direction with
! norm equal to the maximal difference quotient. 
! The method amounts to approximate differentiation 
! of the function defined by the scalar field, 
! given its value on a set of random points. 
! They analyze the accuracy and complexity of 
! the algorithm from a probabilistic point of 
! view. The estimated probability that the 
! error is small and converges to one, as the 
! number of nodes goes to infinity, is shown.
! 
!  
! \paragraph{West et al.}~\citeyear{west:b2001}
! are designing parts of the sensor 
! network architecture with microclimate
! monitoring as their application for focus. 
! Their necessities for an architecture stem 
! from preliminary testing. To understand the 
! performance of low-power transceivers, they 
! have taken propagation measurements using 
! commercially available equipment in the local
! Ponderosa pine forests. An interesting 
! observation made in this environment was that if
! the antenna was placed at a height of less 
! than 1 meter, the range severely degraded.
! 
! Some of the suggestions that they make to 
! expand current sensor network implementations 
! and architectures are:
! \begin{itemize}
! \item  Distributed source coding of spatio-temporally 
! correlated vector process.
! \item  Multi-hop protocols with inter-layer 
! interaction, as interaction between layers 
! may prove benfecial in a more compact and power efficient system design.
! \item  Coded macrodiversity in energy-limited 
! multi-hop nets where a transmission using a 
! basic radio is heard by multiple neighboring nodes.
! \end{itemize}
! 
! 
! \paragraph{Ramadurai and Sichitui}~\citeyear{ramadurai:v2003,sichitiu:ml2003}
! develop distributed algorithms 
! for outdoor localization in sensor networks. 
! The method is based on radio-frequency (RF) signal strength
! measurements, which tend to have a certain degree 
! of inaccuracy. They define two classes of nodes, 
! namely unknown and beacon nodes. The "beacon" nodes have known
! absolute (using GPS) positions, and the "unknown" 
! nodes do not know their positions.
! The beacon nodes peridically inject packets which 
! are used to form position estimates
! at the unknown nodes. Once an unknown nodes has 
! a rough idea of its position, it can
! assist other unknown nodes in estimating 
! their position.
! 
! They employ two techniques for associating signal 
! strength measurements to distance. Both techniques 
! are grounded through preliminary empirical data. 
! The first uses bounded values that associate an 
! RSSI reading to a distance range, plus adding power level
! variability gives it increased accuracy and granularity. 
! The second uses probabilistic position estimation, 
! where any node receiving a beacon packet will 
! estimate itself to be located on a surface that 
! has a probability distribution dictated by a mean and 
! standard deviation corresponding to the signal 
! strength received.
! 
! The measurements for both these approaches were 
! collected outdoors with very little interference, 
! however their data also indicates that even in 
! heavily wooded areas the signal propagation is 
! approximately circular, and hence the signal strength is 
! linearly proportional to the distance.
! 
! 
! \paragraph{Mainwaring et al.}~\citeyear{mainwaring:a2002}
! have had success in deploying 
! large scale sensor networks as part of
! a habitat monitoring project at the Great Duck 
! Island. It is reflective of the domain of applications 
! for which sensor networks are going to be used. 
! It serves as a collection of requirements, costraints 
! and guidelines that serve as a basis for a general 
! sensor network architecture. 
! 
! They present a tierd architecture, where at the 
! lowest level are sensor nodes, which perform general 
! purpose computing and are organized into sensor 
! patches. Individual sensor nodes transmit their data 
! through a patch to a sensor network gateway which links
! the sensor patch through a local transit network to 
! a remote basestation. The transit network consists 
! of one or more hops of wireless links. The current 
! system consists of 32 nodes on a small island off 
! the coast of Maine streaming live data onto the web.
! 
! The main tasks at hand that the network spends 
! resources for are 1) data sampling and collection, 
! 2) communications, 3) network retasking and 4) 
! health and status monitoring.  Keeping these in mind, 
! energy required for each task is projected and taken 
! into account to estimate how long the sensor nodes 
! will survive.