University of California, Santa Cruz
The Jack Baskin School of Engineering
Presents

Dissertation Defense
"Energy Consumption Trade-offs in Power Constrained Networks"

Cintia Margi
PhD Candidate
Computer Engineering

Thursday,June 8, 2006
2:00PM-4:00PM
Engineering 2 Room 399

Abstract
Wireless Sensor Networks are a valuable technology to support many 
applications in different areas, such as: environmental and habitat 
monitoring, surveillance, indoor climate control, structural monitoring, 
mapping, disaster management, and so on. Participating nodes in these 
networks are inherently resource constrained, since they have limited 
processing capabilities, storage, communications speed and bandwidth, and 
mainly they have very limited power supply.
Most of current research on sensor network protocols focus on applications 
that requires low sampling rate and low bandwidth, such as applications 
that make use of sensors like humidity or temperature. Also, the main 
assumption is that communications cost dominate in Sensor Networks. But 
this is not the case for all types of sensor networks. One of the most 
complex scenarios is a Visual Sensor Networks (VSN), which include cameras 
as sensing devices. This kind of sensor networks has different processing 
and network requirements. These requirements are associated with the 
application of the visual sensor network performance (e.g. image/video 
acquisition frequency, processing and transmission),but they have a huge 
impact on the node lifetime.

The main goal of this work is to understand the energy consumption 
trade-offs between computation and communication in power constrained 
networks in general, and in visual sensor networks, in particular. In order 
to do so, we need to evaluate and model: energy spent to process data by 
sensors (e.g., how much energy vision algorithms require, etc.), as well as 
energy required for communications.

The first step is to develop an accurate model for energy consumption due 
to communications. Such energy model must be as close to reality as 
possible, taking into account all radio states, i.e., energy spent not only 
while transmitting and receiving a packet, but also while in idle, 
overhearing, or sleep modes. We have developed such a model and used it to 
instrument the QualNet and GloMoSim network simulators. We have used 
QualNet/GloMoSim instrumented with our model to compare energy consumption 
of power-aware MAC protocols, multi-hop ad-hoc wireless networks routing 
protocols, and to evaluate an analytical model of energy consumption in 
single-hop IEEE 802.11 ad-hoc networks.

Next, we look at a model that also includes processing and sensing tasks, 
besides communication. Since we were targeting power constrained networks, 
we had to look into a typical platform for wireless mobile applications: a 
laptop. This step provided a good opportunity to define our methodology, 
which determines the energy consumption of basic tasks.

 From this point on, we focused on Visual Sensor Networks. We designed and 
implemented a wireless camera network testbed, Meerkats, which is 
based on the Stargate platform.
We followed the same methodology developed for the laptops' testbed to 
characterize the energy consumption of the Meerkats' node. Along this work, 
we also extended and validate the on-board battery monitoring capability on 
the Stargate.

Following this step, we tailored the tasks to the ones representative of 
activities carried out by wireless camera networks targeting surveillance 
applications. Given this set of tasks (which we call elementary tasks), a 
quantitative power consumption and temporal analysis of set of duty cycles 
that apply to the Meerkats testbed was done.

Then we used what we learned from the duty cycle analysis along with a set 
of experiments on the Meerkats testbed to understand how a framework for 
lifetime prediction can be build. We proposed a simple deterministic 
lifetime prediction model based on task composition, which we validate 
using the experiments executed on the Meerkats testbed.
Finally, we consider a set of possible duty cycles given requirements for 
the Meerkats testbed and analyze how they affect the node lifetime.


Advisor: Professor Katia Obraczka