Changeset 2818

Timestamp:

10/30/09 20:28:36 (4 weeks ago)

Author:

jaein

Message:

 

Location:

HydroWatch/Tim/doc/ipsn10

Files:

• energy.bib (modified) (5 diffs)
• sec_eval.tex (modified) (3 diffs)

HydroWatch/Tim/doc/ipsn10/energy.bib

@@ -10 +10 @@
 
 
-@conference{hui08,
+@conference{hui08sensys,
         Author = {Jonathan Hui and David Culler},
         Booktitle = {ACM Sensys},
@@ -99 +99 @@
         Author = {Bugra Gedik and Ling Liu and Philip S. Yu},
         Issn = {1045-9219},
-        Journal = {IEEE Transactions on Parallel and Distributed Systems},
+        Journal = {IEEE TPDS},
         Number = {12},
         Pages = {1766-1783},
@@ -202 +202 @@
         Year = {2007}}
 
-@article{hui08sensys,
-        Address = {New York, NY, USA},
-        Author = {Hui,, Jonathan W. and Culler,, David E.},
-        Doi = {http://doi.acm.org/10.1145/1460412.1460415},
-        Isbn = {978-1-59593-990-6},
-        Journal = {SenSys '08: Proceedings of the 6th ACM article on Embedded network sensor systems},
-        Location = {Raleigh, NC, USA},
-        Pages = {15--28},
-        Publisher = {ACM},
-        Title = {IP is dead, long live IP for wireless sensor networks},
-        Year = {2008}}
-
 @article{ye06sensys,
         Author = {Wei Ye and Fabio Silva and John Heidemann},
@@ -313 +301 @@
 @article{simjee06,
         Author = {Farhan Simjee and Pai H. Chou},
-        Journal = {International Symposium on Low Power Electronics and Design (ISLPED `06)},
+        Journal = {ISLPED},
         Month = {Oct},
         Title = {Everlast: Long-Life, Supercapacitor-Operated Wireless Sensor Node},
@@ -507 +495 @@
 @article{kansal03,
         Author = {Kansal,, Aman and Srivastava,, Mani B.},
-        Journal = {ISLPED `03},
+        Journal = {ISLPED},
         Title = {An environmental energy harvesting framework for sensor networks},
         Year = 2003}

HydroWatch/Tim/doc/ipsn10/sec_eval.tex

@@ -11 +11 @@
 In order to validate the performance of our optimization protocol, we have retrospectively tested our protocol for several months on outdoor environmental solar data. Given the lack of periods of little sun from this data, we simulated this by inserting periods of low solar energy in order to be able to validate performance under these types of conditions.
 
-Figure~\ref{fig:vlsb1} shows the performance of the protocol over 110 days of data for a typical node. In this case, an interval is defined as one day where the parameters $F_s(n,k)$ and $F_r(n,k)$ are recalculated every day/interval. Figure~\ref{fig:vlsb1}(a) shows the case where 3 days ahead energy prediction is used, whereas Figure~\ref{fig:vlsb1}(b) shows the case where an estimate of the energy on the day only is used. We can observe the way in which longer-term energy forecast information changes the behavior of the system. The forecast information allows the system to take greater risks in how far it can drop it's stored energy below the target value. This in turn allows for a smoother progression of report and sample parameters. In the case of limited forecast information, there is much greater fluctuation in these same parameters in order to keep within the target energy range.
+Figure~\ref{fig:vlsb1} shows the performance of the protocol over 110 days of data for a typical node. In this case, an interval is defined as one day where the parameters $F_s(n,k)$ and $F_r(n,k)$ are recalculated every day/interval. Figure~\ref{fig:vlsb1}(a) shows the case where 3 days ahead energy prediction is used, whereas Figure~\ref{fig:vlsb1}(b) shows the case where an estimate of the energy on the day only is used. We can observe the way in which longer-term energy forecast information changes the behavior of the system. The forecast information allows the system to take greater risks in how far it can drop it's stored energy below the target value. This in turn allows for a smoother progression of report and sample parameters. In the case of limited forecast information, there is much greater fluctuation in the same parameters in order to keep within the target energy range.
 
 \begin{figure*}[ht]
@@ -25 +25 @@
 
 
-This smoothing effect brought about by greater prediction power can also be observed in the distribution of utility values as defined in Equation~\ref{equ:utility}. Figure~\ref{fig:util_cdf1} shows the CDFs of utility of both cases where greater prediction can be seen to reduce the proportion of days with low utility. This effect can further be observed in Figure~\ref{fig:UvsEh1} showing the relationship between daily energy harvested and utility. Additional predictive power enables the system to greatly increase utility during days with little harvested energy, which is achieved by a slight reduction in utility in days with high energy.
+This smoothing effect brought about by greater prediction power can also be observed in the distribution of utility values as defined in Equation~\ref{equ:utility}. Figure~\ref{fig:util_cdf1} shows the CDFs of utility of both cases where greater prediction can be seen to reduce the proportion of days with low utility. This effect can further be observed in Figure~\ref{fig:UvsEh1} showing the relationship between daily energy harvested and utility. Additional predictive power enables the system to greatly increase utility during days with little harvested energy, which is achieved by a slight reduction in utility on days with high energy.
 
 %\begin{figure}[ht]
@@ -91 +91 @@
 \subsection{Comparison with Related Work}
 For comparative analysis of our proposed protocol, we consider two related works:
-Vigorito \cite{vigorito07} and Hsu \cite{hsu06} as described in Section~\ref{sec:relatedadapt}. The key differences between our protocol and those of the related work are also described in this section.
+Vigorito \cite{vigorito07} and Hsu \cite{hsu06} as described in Section~\ref{sec:relatedadapt}. The key differences between our protocol and the related work are also described in this section.
 
 %Figure~\ref{fig:vigorito_algorithm} shows