Changeset 2756

Timestamp:

10/30/09 03:45:10 (4 weeks ago)

Author:

wark

Message:

chnages to intro

Location:

HydroWatch/Tim/doc/ipsn10

Files:

• ipsn10-energy.tex (modified) (2 diffs)
• sec_intro.tex (modified) (1 diff)

HydroWatch/Tim/doc/ipsn10/ipsn10-energy.tex

@@ -45 +45 @@
 %Long-term, environmental sampling by networks of embedded sensors has long been a vision of the computer-science community. This vision has subsequently driven much of the research over the past decade around low-power communications and network protocols, with the primary focus to date on reducing radio power consumption whilst maintaining good communications reliability. Despite these advances however, almost all existing applications of sensor networks still hold to the traditional ``data logger'' paradigm, where a user preselects a fixed operating point (such as sensor sampling rate and reporting interval) and then deploys. In this paper we propose a new architecture for environmental sensor networks, where nodes can adapt their behavior based on the amount  of energy available to harvest from the environment and within the constraints of a user-policy. Rather than nodes attempting to use as little energy has possible, nodes attempt to consume as \emph{much} energy as possible to ensure the greatest amount information can be returned as frequently as possible, whilst still keeping within necessary constraints to ensure long network lifetimes. 
 
-Long-term, environmental sampling by networks of embedded sensors has long been a vision of the computer-science community. This vision has subsequently driven much of the research over the past decade around low-power communications and network protocols, with the primary focus to date on reducing radio power consumption whilst maintaining good communications reliability. Despite these advances however, almost all existing applications of sensor networks still hold to the traditional ``data logger'' paradigm, where a user preselects a fixed operating point (such as sensor sampling rate and reporting interval) and then deploys. In this paper we argue that "sample-and-send" protocols are the wrong paradigm for environmental sensing. We demonstrate that in moving to a model where the radio is completely off for large periods, a whole new suite of functionality can be utilized in order to optimize the performance of nodes, subject to environmental conditions. Within this framework, we explore how the information returned by a network can be maximized while at the same time meeting fundamental requirements around responsiveness of the network and energy budgeting.
+Long-term, environmental sampling by networks of embedded sensors has long been a vision of the computer-science community. This vision has subsequently driven much of the research over the past decade around low-power communications and network protocols, with the primary focus to date on reducing radio power consumption. Despite these advances however, almost all existing applications of sensor networks still hold to the traditional ``data logger'' paradigm, where a user preselects a fixed operating point (such as sampling and reporting interval) and then deploys. In this paper we argue that "sample-and-send" protocols are the wrong paradigm for environmental sensing. We demonstrate that in moving to a model where the radio is completely off for large periods, a whole new suite of functionality can be utilized in order to optimize the performance of nodes, subject to environmental conditions. Within this framework, we explore how the information returned by a network can be maximized while at the same time meeting fundamental requirements around responsiveness of the network and energy budgeting.
 
 \end{abstract}
@@ -51 +51 @@
 \category{C.2}{Computer Systems Organization}{Computer Communication Networks}
 
-\terms{Experimentation, Design}
+\terms{Design,Experimentation}
 
-\keywords{Adaptive, Energy Harvesting}
+\keywords{Utility, Adaptive, Energy Harvesting}
 
 %------------------------------------------------------------------------- 

HydroWatch/Tim/doc/ipsn10/sec_intro.tex

@@ -23 +23 @@
 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}.
 
-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 allows 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}(b) 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 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 allows 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}. 
 
 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 a 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.