12-06-2012, 01:52 PM
INSTRUMENTING THE WORLDWITHWIRELESS SENSOR NETWORKS
[attachment=24186]
INTRODUCTION
The availability of low-power micro-sensors, actuators, embedded
processors, and radios is enabling the application of distributed
wireless sensing to a wide range of applications, including environmental
monitoring, smart spaces, medical applications, and
precision agriculture [1][2]. Most deployed sensor networks involve
relatively small numbers of sensors, wired to a central processing
unit where all of the signal processing is performed [3]. In
contrast, this paper focuses on distributed, wireless, sensor networks
in which the signal processing is distributed along with the
sensing.
MOTIVATING APPLICATION
The potential applications of wireless sensor networks are highly
varied: e.g., Physiological monitoring; Environmental monitoring
(air, water, soil chemistry); Condition based maintenance; Smart
spaces; Military; Precision agriculture; Transportation; Factory instrumentation
and inventory tracking
Habitatmonitoring [Cerpa-etal01, Hamilton, Steere-etal00] provides
a rich collection of sensing modalities and environmental
conditions and we use it to motivate our technical discussion. Consider
the goal of supporting data collection and model development
of complex ecosystems. Scientists and environmental impact monitoring
authorities would like to monitor soil and air chemistry, as
well as plant and animal species populations and behavior. For
the latter, the primary modalities are imaging and acoustics to localize,
identify and track species or phenomena based on implicit
signals (acoustic and seismic), or explicit signals (RF tags). These
facilities must be deployable in remote locations that lack installed
energy and communication infrastructures, motivating the need for
low-power wireless communication.
TECHNICAL CHALLENGES
Most envisioned sensor network applications encounter one ormore
of the following challenges: Untethered for energy and communication requiring maximal
focus on energy efficiency. Ad hoc deployment, requiring that the system identifies
and copes with the resulting distribution and connectivity
of nodes. Dynamic environmental conditions requiring the system to
adapt over time to changing connectivity and system stimuli.
Unattended operation requiring configuration and reconfiguration
be automatic (self-configuration)
To address these challenging environments, several strategies
are likely to be key building blocks/techniques for wireless sensor
networks:
Localization
Node location is employed by routing protocols that use spatial addresses,
and by signal processing algorithms (e.g. beamforming)
that are used for tasks such as target tracking. The underlying algorithm
problem is that of localization whereby the nodes in the
network discover their spatial coordinates upon network boot-up.
When the sensor nodes are deployed in an unplanned topology,
there is no a priori knowledge of location. The use of GPS in
sensor nodes is ruled out in many scenarios because of power consumption,
antenna size, and overhead obstructions such as dense
foliage. The ad hoc nature of deployment rules out infrastructure
for many scenarios of localization. It is critical that sensor network
nodes be able to estimate their relative positions without assistance,
using means that can be built-in.
CONCLUSIONS
In conclusion, wireless sensor networks present fascinating challenges
for the application of distributed signal processing and distributed
control. These systems will challenge us to apply appropriate
techniques and metrics in light of the technology opportunities
(cheap processing and sensing nodes) and challenges (energy
constraints).
[attachment=24186]
INTRODUCTION
The availability of low-power micro-sensors, actuators, embedded
processors, and radios is enabling the application of distributed
wireless sensing to a wide range of applications, including environmental
monitoring, smart spaces, medical applications, and
precision agriculture [1][2]. Most deployed sensor networks involve
relatively small numbers of sensors, wired to a central processing
unit where all of the signal processing is performed [3]. In
contrast, this paper focuses on distributed, wireless, sensor networks
in which the signal processing is distributed along with the
sensing.
MOTIVATING APPLICATION
The potential applications of wireless sensor networks are highly
varied: e.g., Physiological monitoring; Environmental monitoring
(air, water, soil chemistry); Condition based maintenance; Smart
spaces; Military; Precision agriculture; Transportation; Factory instrumentation
and inventory tracking
Habitatmonitoring [Cerpa-etal01, Hamilton, Steere-etal00] provides
a rich collection of sensing modalities and environmental
conditions and we use it to motivate our technical discussion. Consider
the goal of supporting data collection and model development
of complex ecosystems. Scientists and environmental impact monitoring
authorities would like to monitor soil and air chemistry, as
well as plant and animal species populations and behavior. For
the latter, the primary modalities are imaging and acoustics to localize,
identify and track species or phenomena based on implicit
signals (acoustic and seismic), or explicit signals (RF tags). These
facilities must be deployable in remote locations that lack installed
energy and communication infrastructures, motivating the need for
low-power wireless communication.
TECHNICAL CHALLENGES
Most envisioned sensor network applications encounter one ormore
of the following challenges: Untethered for energy and communication requiring maximal
focus on energy efficiency. Ad hoc deployment, requiring that the system identifies
and copes with the resulting distribution and connectivity
of nodes. Dynamic environmental conditions requiring the system to
adapt over time to changing connectivity and system stimuli.
Unattended operation requiring configuration and reconfiguration
be automatic (self-configuration)
To address these challenging environments, several strategies
are likely to be key building blocks/techniques for wireless sensor
networks:
Localization
Node location is employed by routing protocols that use spatial addresses,
and by signal processing algorithms (e.g. beamforming)
that are used for tasks such as target tracking. The underlying algorithm
problem is that of localization whereby the nodes in the
network discover their spatial coordinates upon network boot-up.
When the sensor nodes are deployed in an unplanned topology,
there is no a priori knowledge of location. The use of GPS in
sensor nodes is ruled out in many scenarios because of power consumption,
antenna size, and overhead obstructions such as dense
foliage. The ad hoc nature of deployment rules out infrastructure
for many scenarios of localization. It is critical that sensor network
nodes be able to estimate their relative positions without assistance,
using means that can be built-in.
CONCLUSIONS
In conclusion, wireless sensor networks present fascinating challenges
for the application of distributed signal processing and distributed
control. These systems will challenge us to apply appropriate
techniques and metrics in light of the technology opportunities
(cheap processing and sensing nodes) and challenges (energy
constraints).