Changeset 2799

Timestamp:

10/30/09 18:03:59 (4 weeks ago)

Author:

jaein

Message:

 

Files:

• HydroWatch/Tim/doc/ipsn10/sec_intro.tex (modified) (2 diffs)

HydroWatch/Tim/doc/ipsn10/sec_intro.tex

@@ -11 +11 @@
 in general, breaking through the 1\% duty-cycle barrier for practical deployment scenarios is not possible with current radio technology.
 
-Even after radio duty-cycling, idle listening is still one of the major energy consumers of sensor nodes. As shown in as shown in Figure~\ref{fig:energy}(a),  idle listening consumes XX\% over the overall energy budget, compared to just XX\% for sampling and transmitting of data. In other words, a significant energy cost is paid to allow the node to be in a state of ``always on'', and only a small part of the energy is used in the actual sampling and transmission of information\cite{prabal07batch}.
+Even after radio duty-cycling, idle listening is still one of the major energy consumers of sensor nodes. As shown in Figure~\ref{fig:energy}(a),  idle listening consumes XX\% over the overall energy budget, compared to just XX\% for sampling and transmitting of data. In other words, a significant energy cost is paid to allow the node to be in a state of ``always on'', and only a small part of the energy is used in the actual sampling and transmission of information\cite{prabal07batch}.
 
 \begin{figure}[ht]
@@ -26 +26 @@
 
 
-An obvious way to reduce the amount of energy consumed by idle listening is to turn the radio off. Whilst the allows a large amount of energy to be redistributed to tasks such as sampling and sending (when the radio is switched on again), this approach incurs an additional network overhead each time the radios are turned back on in where network routing tables must be reformed and [other things?]. Figure~\ref{fig:energy}(c) illustrates the nature of this additional cost, showing the relationship between time the radios are off and the effective energy consumed per bit of data transmitted. Once radios are off long enough, the effect of amortizing the cost of updating the network state over long periods becomes clear where in these cases the net energy cost is less than a typical low-power listening (LPL) MAC \cite{lpl04sensys}. 
+An obvious way to reduce the amount of energy consumed by idle listening is to turn the radio off. Whilst this allows a large amount of energy to be redistributed to tasks such as sampling and sending (when the radio is switched on again), this approach incurs additional network overhead each time the radios are turned back on where network routing tables must be reformed and [other things?]. Figure~\ref{fig:energy}(c) illustrates the nature of this additional cost, showing the relationship between the time the radios are off and the effective energy consumed per bit of data transmitted. Once radios are off long enough, the effect of amortizing the cost of updating the network state over long periods becomes clear where in these cases the net energy cost is less than a typical low-power listening (LPL) MAC \cite{lpl04sensys}. 
 
-The other key trade-off in turning off radios is that of network responsiveness. Once nodes only become active in scheduled intervals then the times a user can communicate with nodes, or the times which nodes can report to a base are now constrained. This has clear implications for event-driven applications or query-based systems, where a user may want an immediate responsive from the network.
+The other key trade-off in turning off radios is that of network responsiveness. Once nodes only become active at scheduled intervals then the times a user can communicate with nodes, or the times which nodes can report to a base are now constrained. This has clear implications for event-driven applications or query-based systems, where a user may want an immediate response from the network.
 
 \subsection{Motivation}~\label{sec:motivation}
 The balance between network responsiveness and the energy consumption dedicated to actual sensing/sending is an important one that has been under-explored in the community to date. The ``always on'' philosophy has dominated sensor networks systems such that (when taking into account the relative energy consumption of listening compared to sampling), network responsiveness has implicitly been allocated as the highest priority for networks.
 
-We argue that for the vast majority of environmental sensing applications, network responsiveness should not be the highest priority role for networks. In order to reduce this implicit bias towards network responsiveness, we argue that moving towards a ``radio off'' model is far more appropriate for most applications. By redistributing energy consumption away from idle listening, we can more freely control the relationship between energy consumption a more flexible network \emph{utility}. Furthermore, we believe the ``slow-and-steady'' approach to environmental sensing, where nodes consume as little energy as possible, is the wrong paradigm. We argue that consuming as \emph{much} energy as possible, within appropriate energy constraints, allows for networks to perform a far more useful function over the life of the network.
+We argue that for the vast majority of environmental sensing applications, network responsiveness should not be the highest priority role for networks. In order to reduce this implicit bias towards network responsiveness, we argue that moving towards a ``radio off'' model is far more appropriate for most applications. By redistributing energy consumption away from idle listening, we can more freely control the relationship between energy consumption in a more flexible network \emph{utility}. Furthermore, we believe the ``slow-and-steady'' approach to environmental sensing, where nodes consume as little energy as possible, is the wrong paradigm. We argue that consuming as \emph{much} energy as possible, within appropriate energy constraints, allows for networks to perform a far more useful function over the life of the network.
 
 Given this model of thinking, the main contributions of this paper are as follows: