Sensor Network Applications
Wireless Sensor Networks for Habitat
Monitoring
[Mainwaring+ 2002]
Introduction
–
Habitat and environmental monitoring represent essential class of sensor
network applications by placing numerous networked micro-sensors in
an environment where long-term data collection can be achieved
–
The sensor nodes perform filtering and triggering functions as well as
application-specific or sensor-specific data compression algorithms thru
the integration of local processing and storage
–
The ability to communicate allows nodes to cooperate in performing
tasks such as statistical sampling, data aggregation, and system health
and status monitoring
–
Increased power efficiency assists in resolving fundamental design
tradeoffs, e.g., between sampling rates and battery lifetimes
Wireless Sensor Networks for Habitat
Monitoring
[Mainwaring+ 2002]
Introduction
–
The sensor nodes can be reprogrammed or retasked after deployment in
the field by the networking and computing capabilities provided
–
Nodes can adapt their operation over time in response to changes in the
environment
–
The application context helps to differentiate problems with simple and
concrete solutions from open research areas
–
An effective sensor network architecture and general solutions should be
developed for the domain
–
The impact of sensor networks for habitat and environmental monitoring
is measured by their ability to enable new applications and produce new
results
Wireless Sensor Networks for Habitat
Monitoring
[Mainwaring+ 2002]
Introduction
–
This paper develops a specific habitat monitoring application, but yet a
representative of the domain
–
It presents a collection of requirements, constraints and guidelines that
serve as a basis for general sensor network architecture
–
It describes the core components of the sensor network for this domain–
hardware and sensor platforms, the distinct networks involved, their
interconnection, and the data management facilities
–
The design and implementation of the essential network services –
power management, communications, re-tasking, and node management
can be evaluated in this context
Wireless Sensor Networks for Habitat
Monitoring
[Mainwaring+ 2002]
Habitat Monitoring
–
Researchers in the Life Sciences are concerned about the impacts of
human presence in monitoring plants and animals in the field conditions
–
It is possible that chronic human disturbance may adversely effect results
by changing behavioral patterns or distributions
–
Disturbance effects are of concern in small island situations where it may
be physically impossible for researchers to avoid some impact on an
entire population
–
Seabird colonies are extreme sensitive to human disturbance
–
Research in Maine [Anderson 1995], suggests that a 15 minute visit to a
cormorant colony can result in up to 20% mortality among eggs and
chicks in a given breeding year. Repeated disturbance can lead to the
end of the colony
Wireless Sensor Networks for Habitat
Monitoring
[Mainwaring+ 2002]
Habitat Monitoring
–
On Kent Island, Nova Scotia, research learned that Leach’s Storm Petrels
are likely to desert their nesting burrows in case of disturbance during the
first two weeks of incubation
–
Sensor networks advances the monitoring methods over the traditional
invasive ones
–
Sensors can be deployed prior to the breeding season or other sensitive
period or while plants are dormant or the ground is frozen on small islets
where it would be unsafe or unwise to repeatedly attempt field studies
–
Sensor network deployment may be more economical method for
conducting long-term studies than traditional personnel-rich methods
–
A “deploy ‘em and leave ‘em” strategy of wireless sensor usage would
decrease the logistical needs to initial placement and occasional servicing
Wireless Sensor Networks for Habitat
Monitoring
[Mainwaring+ 2002]
Great Duck Island
–
The College of Atlantic (COA) is field testing in-situ sensor networks for
habitat monitoring
–
Great Duck Island (GDI) is a 237 acre island located 15 km south of
Mount Desert Island, Maine
–
At GDI, three major questions in monitoring the Leach’s Storm Petrel
[Anderson 1995]:
1.
What is the usage pattern of nesting burrows over the 24-72 hour
cycle when one or both members of a breeding pair may alternate
incubation duties with feeding at sea?
2.
What changes can be observed in the burrow and surface
environmental parameters during the course of the approximately 7
month breeding season (April-October)?
Wireless Sensor Networks for Habitat
Monitoring
[Mainwaring+ 2002]
Great Duck Island
3.
What are the differences in the micro-environments with and without
large numbers of nesting petrels?
–
Presence/absence data is obtained through occupancy detection and
temperature differentials between burrows with adult birds and burrows
that contain eggs, chicks, or are empty
–
Petrels will most likely enter or leave during the daytime; however, 5-10
minutes during late evening and early morning measurements are
needed to capture the entry and exit timings
–
More general environmental differentials between burrow and surface
conditions can be captured by records every 2-4 hours during the
extended breeding season; whereas, the differences between “popular”
and “unpopular” sites benefit from hourly sampling
Wireless Sensor Networks for Habitat
Monitoring
[Mainwaring+ 2002]
Great Duck Island Requirements
1.
Internet Access
–
The sensor networks at GDI must be accessible via the Internet since the
ability to support remote interactions with in-situ networks is essential
2.
Hierarchical Network
–
Habitats of interest are located up to several kilometers away. A second
tier of wireless networking provides connectivity to multiple patches of
sensor networks deployed at each of the areas.
3.
Sensor Network Longevity
–
Sensor networks that runs for several month from non-rechargeable
power sources would be desirable since studies at GDI can span multiple
field seasons
Wireless Sensor Networks for Habitat
Monitoring
[Mainwaring+ 2002]
Great Duck Island Requirements
4.
Operating off-the grid
–
Every level of the network must operate with bounded energy supplies
–
Renewable energy such as solar power may be available some
locations, disconnected operation is a possibility
–
GDI has enough solar power that run the application 24x7 with small
probabilities of service interruptions due to power loss
5.
Management at-a-distance
–
Remoteness of the field sites requires the ability to monitor and manage
sensor networks over the Internet. The goal is no on-site presence for
maintenance and administration during the field season, except for
installation and removal of nodes
Wireless Sensor Networks for Habitat
Monitoring
[Mainwaring+ 2002]
Great Duck Island Requirements
6.
Inconspicuous operation
–
It should not disrupt the natural processes or behaviors under study
–
Removing human presence from the study areas would eliminate a
source of error and variation in data collection and source of disturbance
7.
System behavior
–
Sensor networks should present stable, predictable, and repeatable
behavior at all times since unpredictable system is difficult to debug and
maintain
–
Predictability is essential in developing trust in these new technologies
for life scientists
Wireless Sensor Networks for Habitat
Monitoring
[Mainwaring+ 2002]
Great Duck Island Requirements
8.
In-situ interactions
–
Local interactions are required during initial development, maintenance
and on-site visits
–
PDAs can be useful in accomplishing these tasks – they may directly
query a sensor, adjust operational parameters and so on
9.
Sensors and sampling
–
The ability to sense light, temperature, infrared, relative humidity, and
barometric pressure are essential set of measurements
–
Additional measurements may include acceleration/vibration, weight,
chemical vapors, gas concentrations, pH, and noise levels
Wireless Sensor Networks for Habitat
Monitoring
[Mainwaring+ 2002]
Great Duck Island Requirements
10. Data archiving
–
Sensor readings must be achieved for off-line data mining and analysis
–
The reliable offloading of sensor logs to databases in the wired, powered
infrastructure is essential
–
It is desirable to interactively “drill-down” and explore sensors in near
real-time complement log-based studies. In this mode of operation, the
timely delivery of sensor data is the key
–
Nodal data summaries and periodic health-and-status monitoring also
requires timely delivery of the data
Wireless Sensor Networks for Habitat
Monitoring
[Mainwaring+ 2002]
System Architecture
–
A tiered architecture is developed
–
The lowest level consists of the sensor nodes that perform general
purpose computing and networking as well as application-specific sensing
–
The sensor nodes may be deployed in dense patches and transmit their
data through the sensor network to the sensor network gateway
–
Gateway is responsible for transmitting sensor data from the sensor patch
through a local transit network to the remote base station that provides
WAN connectivity and data logging
–
The base station connects to database replicas across the internet
–
At last, the data is displayed to researchers through a user interface
Wireless Sensor Networks for Habitat
Monitoring
[Mainwaring+ 2002]
System Architecture
Figure 1: System architecture for habitat monitoring
Wireless Sensor Networks for Habitat
Monitoring
[Mainwaring+ 2002]
System Architecture
–
The autonomous sensor nodes are placed in the areas of interest where
each sensor node collects environmental data about its immediate
surroundings
–
Since these sensors are placed close to the area of interest, they can be
built using small and inexpensive individual sensors – high spatial
resolution can be achieved through dense deployment of sensor nodes
–
This architecture offers higher robustness compared to traditional
approaches which use a few high quality sensors with complex signal
processing
–
The computational module is a programmable unit that provides
computation, storage and bidirectional communication with other nodes
Wireless Sensor Networks for Habitat
Monitoring
[Mainwaring+ 2002]
System Architecture
–
The computational module interfaces with the analog and digital sensors
on the sensor module, performs basic signal processing and dispatches
the data according to the needs of the application
–
Compared to traditional data logging systems, networked sensors offer
two main advantages: they can be re-tasked in the field and they can
communicate with the rest of the system
–
In-situ re-tasking gives researchers the ability to refocus their
observations based on the analysis of the initial results – initially, absolute
temperature readings are desired, after a while, only significant
temperature changes exceeding a threshold may become more useful
Wireless Sensor Networks for Habitat
Monitoring
[Mainwaring+ 2002]
System Architecture
–
Individual sensor nodes communicate and coordinate with one another
–
These nodes form a multi-hop network by forwarding each other’s
messages and if needed, the network can perform in-network aggregation
(e.g., relaying the average temperature across the region)
–
Eventually, data from each sensor needs to be propagated to the Internet
–
The propagated data may be raw, filtered or processed data
–
Since direct wide area connectivity cannot be brought to each sensor path
due to several reasons (e.g., cost of equipment, power, disturbance
created by the installation of the equipment in the environment), wide are
connectivity is brought to a base station instead
Wireless Sensor Networks for Habitat
Monitoring
[Mainwaring+ 2002]
System Architecture
–
The base station may communicate with the sensor patch using a
wireless LAN where each sensor patch is equipped with a gateway that
can communicate with the sensor network and provides connectivity to
the transit network
–
The transit network may consist of a single hop link or series of networked
wireless nodes and each transit network design has different
characteristics with respect to expected robustness, bandwidth, energy
efficiency, cost and manageability
–
To provide data to remote end-users, the base station includes WAN
connectivity and persistent data storage for the collection of sensor
patches
Wireless Sensor Networks for Habitat
Monitoring
[Mainwaring+ 2002]
System Architecture
–
It is expected that WAN connection will be wireless
–
The architecture needs to address the disconnection possibilities
–
Each layer (sensor nodes, gateways, base stations) has some persistent
storage to protect against data loss due to power outage as well as data
management services
–
At the sensor level, these will be primitive, taking the form of data logging
–
The base station may provide relational database service while the data
management at the gateways falls somewhere in between
–
When it comes to data collection, long-latency is preferable to data loss
–
Users interact with the sensor network in two ways
Wireless Sensor Networks for Habitat
Monitoring
[Mainwaring+ 2002]
System Architecture
–
Remote users access the replica of the base station database
–
This approach assists on integration with data analysis and mining tools
while masking the potential wide area disconnections with the base
stations
–
On-site users may require direct interaction with the network and this can
be accomplished with a small, PDA-sized device, referred to as gizmo
–
Gizmo allows the user to interactively control the network parameters by
adjusting the sampling rates, power management parameters and other
network parameters
–
The connectivity between any sensor node and gizmo may or may not
rely on functioning on multi-hop sensor network routing
Wireless Sensor Networks for Habitat
Monitoring
[Mainwaring+ 2002]
Implementation Strategies
Sensor Network Node
–
UC Berkeley motes are used as the sensor nodes
–
Mica uses a single channel, 916 MHz radio from RF Monolithics to
provide bi-directional communication at 40 Kbps, an Atmel Atmega 103
microcontroller running at 4 MHz and 512 KB nonvolatile storage
–
A pair of conventional AA batteries and a DC boost converter provide the
power source; however, other renewable energy sources can be used
Wireless Sensor Networks for Habitat
Monitoring
[Mainwaring+ 2002]
Implementation Strategies
Sensor Board
–
The Mica Weather Board provides sensors that monitor changing
environmental conditions with the same functionality as a traditional
weather station
–
The Mica Weather Board includes temperature, photoresistor, barometric
pressure, humidity, and passive infrared (thermopile) sensors
Table 1: Mica Weather Board
Wireless Sensor Networks for Habitat
Monitoring
[Mainwaring+ 2002]
Implementation Strategies
Sensor Board
Figure 2: Mica Hardware Platform: The Mica sensor node (left) with the Mica
Weather Board developed for environmental monitoring applications
Wireless Sensor Networks for Habitat
Monitoring
[Mainwaring+ 2002]
Implementation Strategies
Energy Budget
–
Typical habitat monitoring applications need to run for nine months
–
The application chooses how to allocate the energy budget between
sleep modes, sensing, local calculations and communications
–
Since different nodes have different functions, they also have different
power requirements, for instance, the nodes near the gateway may need
to forward all messages from a patch while a node in a nest may only
need to report its own readings
–
When a set of power limited nodes exhaust their power supplies, the
network can become disconnected and inoperable
–
There is a need to budget the power with respect to the energy
bottlenecks of the network
Wireless Sensor Networks for Habitat
Monitoring
[Mainwaring+ 2002]
Implementation Strategies
Energy Budget
–
The baseline life time of the node is determined by the current draw in
the sleep state
–
Minimizing power in sleep mode means turning off the sensors, the radio
and putting the processor into a deep sleep mode
Table 2: Power required by various Mica operations
Wireless Sensor Networks for Habitat
Monitoring
[Mainwaring+ 2002]
Implementation Strategies
Sensor Deployment
–
A wireless sensor network using Mica motes with Mica Weather Board
has been deployed in July 2002
–
Environmental protective packaging has been designed which minimally
obstruct sensing functionality
Figure 3: Acrylic enclosure used for deploying the Mica mote
Wireless Sensor Networks for Habitat
Monitoring
[Mainwaring+ 2002]
Implementation Strategies
Patch Gateways
–
Usage of different gateway nodes directly affects the underlying available
transit network
–
Two designs implemented: an 802.11b single hop with an embedded
Linux system and a single hop mote-to-mote network
–
Initially, CerfCube [Cerfcube] which is a small StrongARM-based
embedded system to act as a sensor patch gateway, is chosen
–
Each gateway is equipped with a CompactFlash 802.11b adapter
–
Gateway use permanent storage of up to 1GB
–
The mote-to-mote solution consisted of a mote connected to the base
station and a mote in the sensor patch
Wireless Sensor Networks for Habitat
Monitoring
[Mainwaring+ 2002]
Implementation Strategies
Patch Gateways
–
The differences between the mote and CerfCube include different
o
communication frequency
o
power requirements
o
software components
–
The mote’s MAC layer does not require bi-directional link like 802.11b
–
In addition, the mote sends raw data with a small packet header (4 bytes)
directly over the radio as opposed to overheads imposed by 802.11b and
TCP/IP connections
Wireless Sensor Networks for Habitat
Monitoring
[Mainwaring+ 2002]
Implementation Strategies
Base-station installation
–
For achieve remote access, collection of sensor patches is connected to
the Internet through a wide-area link
–
On GDI, Internet connectivity is accomplished through a two-way satellite
connection provided by Hughes and similar to DirecTV system
–
The satellite system is connected to a laptop which coordinates the
sensor patches and provides a relational database service
Database Management System
–
The base station uses Postgres SQL database which stores timestamped readings from the sensors, health status of the individual
sensors, and metadata (e.g., sensor locations)
Wireless Sensor Networks for Habitat
Monitoring
[Mainwaring+ 2002]
Implementation Strategies
Database Management System
–
The GDI database is replicated every fifteen minutes over the wide-area
satellite link to Postgres database in Berkeley
User Interfaces
–
Many user interfaces can be implemented on top of the sensor database
–
GIS systems provide a widely used standard for analyzing geographical
data and most statistics and data analysis packages implement
interfaces to relational databases
–
Number of web interfaces can be implemented to provide the ubiquitous
interfaces to the habitat data
Energy-Efficient Computing for Wildlife Tracking:
Design Tradeoffs and Early Experiences with ZebraNet
[Juang+ 2002]
Introduction
–
Focus is on issues related to dynamic sensor networks with mobile
nodes and wireless communication between them
–
In this system, the sensor nodes collars carried by the animals under
study; wireless ad hoc networking techniques are used to swap and store
data in a peer-to-peer manner and to pass it towards a mobile base
station that sporadically traverses the area to upload data
–
Biology and biocomplexity research has been focused on gathering data
and observations on a range of species to understand their interactions
and influences on each other
–
For example, how human development into wilderness areas affects
indigenous species there; understand the migration patterns of wild
animals and how they may be affected by changes in weather patterns or
plant life, by introduction of non-native species, and by other influences
Energy-Efficient Computing for Wildlife Tracking:
Design Tradeoffs and Early Experiences with ZebraNet
[Juang+ 2002]
Introduction
–
Finding and learning these details require long-term position logs and
other biometric data such as heart rate, body temperature, and frequency
feeding
–
Current wildlife tracking studies rely on simple technology, for example,
many studies rely on collaring a sample subset of animals with simple
VHF transmitters
–
Researchers periodically drive through and/or fly over an area with a
receiver antenna, and listen for pings from previously collared animals
–
Once animal is found, its behavior can be observed and its observed
position can be logged; however, there are limits to such studies
–
First, data collection is infrequent and can miss many “interesting events”
Energy-Efficient Computing for Wildlife Tracking:
Design Tradeoffs and Early Experiences with ZebraNet
[Juang+ 2002]
Introduction
–
Second, data collection is mostly limited to daylight hours, but animal
behavior and movements in night hours can be different
–
Third, data collection is impossible or very limited for secluded species
that avoid human contact
–
The most elegant trackers commercially available use GPS to track
position and use satellite uploads to transfer data to a base station
–
These systems also suffer from several limitations
–
First, at most a log of 3000 position samples can be logged and no
biometric data
–
Second, since satellite uploads are slow and uses high power
consumption, they are done infrequently – this limits how often position
samples can be gathered without overflowing 3000-entry log storage
Energy-Efficient Computing for Wildlife Tracking:
Design Tradeoffs and Early Experiences with ZebraNet
[Juang+ 2002]
Introduction
–
Third, downloads of data from the satellite to the researchers are both
slow and expensive, therefore, constraining the amount of data collected
–
Finally, these systems operate on batteries without recharge – when
power is drained, the system become unusable unless it is retrieved,
recharged and re-deployed
–
ZebraNet project is building tracking nodes that include a low-power
miniature GPS system with user-programmable CPU, non-volatile
storage for data logs, and radio transceivers for communicating either
with other nodes or with a base station
Energy-Efficient Computing for Wildlife Tracking:
Design Tradeoffs and Early Experiences with ZebraNet
[Juang+ 2002]
Introduction
–
One of the key principles of ZebraNet is that the system should work in
arbitrary wilderness locations; no assumptions are made about the
presence of of fixed antenna towers or cellular phone service
–
The system uses peer-to-peer data swaps to move the data around;
periodic researcher drives bys and/or fly-overs can collect logged data
from several animals despite encountering relatively few within range
–
Even though ad hoc sensor networks have been heavily studied, not
much has been published about the characteristics of mobile sensor
networks with mobile base stations and very few studies focus on
building real systems
Energy-Efficient Computing for Wildlife Tracking:
Design Tradeoffs and Early Experiences with ZebraNet
[Juang+ 2002]
Introduction
–
This paper has the following unique contributions:
o
To the best knowledge of authors, this is the first study of mobile
sensor networks protocols in which the base station is also mobile. It
is presumed that researchers will upload data while driving or flying
by the region
o
Zebra-tracking is a domain in which the node mobility models are
unknown which makes it a research goal. Understanding how, when
and why zebras undertake long-term migrations is the most
essential biological question of this work.
o
ZebraNet’s data collection has communication patterns in which
data can be cooperatively passed towards a base station
o
Energy tradeoffs are examined in detail using real system energy
measurements for ZebraNet prototype hardware in operation
Energy-Efficient Computing for Wildlife Tracking:
Design Tradeoffs and Early Experiences with ZebraNet
[Juang+ 2002]
Introduction
–
–
Some of the interesting research questions to be explored are:
o
How to make the communications protocol both effective and powerefficient?
o
To what extent can we rely on ad hoc, peer-to-peer transfers in a
sparsely-connected spatially-huge sensor network?
o
How can we provide comprehensive tracking of a collection of
animals, even if some of the animals are reclusive and rarely are
close enough to humans to have their data logs updated directly?
This research work gives quantitative explorations of the design
decisions behind some of these questions
Energy-Efficient Computing for Wildlife Tracking:
Design Tradeoffs and Early Experiences with ZebraNet
[Juang+ 2002]
ZebraNet Design Goals
–
The ZebraNet project is a direct and ongoing collaboration between
researchers in experimental computer systems and in wildlife biology
–
The wildlife biologists have determined the tracker’s overall design goals:
o
GPS position samples are taken every three minutes
o
Detailed activity logs taken for three minutes every hour
o
One year of operation without direct human intervention – that is, not
counting on tranquilizing and re-collaring an animal more than once
per year
o
No fixed base stations, antennas, or cellular service
o
A high success rate for eventually delivering all logged data is
essential while latency is not as critical
o
For a zebra collar, a weight limit of 3-5 lbs is recommended
Energy-Efficient Computing for Wildlife Tracking:
Design Tradeoffs and Early Experiences with ZebraNet
[Juang+ 2002]
ZebraNet Design Goals
–
Ultimately, this detailed information may include several position
estimates, temperature information, weather data, environmental data,
and body movements that will serve as signatures of behavior; however,
in this initial system, the focus is only on position data
–
Overall, the key goal is to deliver to researchers a very high fraction of the
data collected over the months or years that the system is in operation
–
Therefore, ZebraNet must be power-efficient, designed with appropriate
data log storage, and must be rugged to ensure reliability under tough
environmental conditions
Energy-Efficient Computing for Wildlife Tracking:
Design Tradeoffs and Early Experiences with ZebraNet
[Juang+ 2002]
ZebraNet Problem Statement
–
The biologists design goals need to be translated into the engineering
task at hand
–
Success rate at delivering position data to the researchers –data homing
rate– should approach 100%
–
Weight limits on each node translate almost directly to computational
energy limits since weight of the battery and solar panel takes bulk of the
total weight of a ZebraNet node; therefore, collar and protocol design
decisions must manage the number and size of data transmissions
required
–
System design choices must be made that limit the range of
transmissions since the required transmitter energy increases
dramatically with the distance transmitted
Energy-Efficient Computing for Wildlife Tracking:
Design Tradeoffs and Early Experiences with ZebraNet
[Juang+ 2002]
ZebraNet Problem Statement
–
The amount of storage needed to hold position logs must be limited – if
many redundant copies are stored and swapped, the storage
requirements can scale as O(n2)
–
Although the energy cost of storage is small compared to that of
transmissions, it is still necessary to develop storage-efficient design
–
Due to limited transceiver, coverage and a base station only sporadically
available, ZebraNet must forward data through other nodes in peer-topeer manner and store redundant copies of position logs in other tracking
nodes
–
Some of the key challenges in ZebraNet come from the spatial and
temporal scale of the system
Energy-Efficient Computing for Wildlife Tracking:
Design Tradeoffs and Early Experiences with ZebraNet
[Juang+ 2002]
ZebraNet Problem Statement
–
In terms of temporal scale, keeping a system running autonomously
months at a time is challenging; it requires tremendous design-time
attention to both hardware and software reliability
–
In terms of spatial scale, ZebraNet is also aggressive; it is the specific
intent of the system to operate over an area of hundreds or thousands of
square square kilometers
–
Due to the large distances involved and sparse sensor coverage,
energy/connectivity tradeoffs become key
Energy-Efficient Computing for Wildlife Tracking:
Design Tradeoffs and Early Experiences with ZebraNet
[Juang+ 2002]
ZebraNet Problem Statement
–
These challenges mentioned here tackles several open problems:
–
ZebraNet protocol promises good communication behavior on mobile
sensors forwarding data towards a mobile base station
–
ZebraNet explores design issues for sensors that are more coarsegrained than many prior sensor proposals. Larger the weight limits
and storage budgets allow researchers to consider different protocols
with improved leverage for sparsely-connected, physicallywidespread sensors
References
[Anderson 1995] J.G.T. Anderson, Pilot survey of mid-coast Maine seabird
colonies: an evaluation of techniques, Bangor, ME, 1995. Report to the
State of Maine Dept. of Inland Fisheries and Wildlife.
[Cerfcube] Cerfcube embedded StrongARM system,
http://www.intrinsys.com/products/cerfcube
[Juang+ 2002] P. Juang, H. Oki, Y. Wang, M. Martonosi, L-S Peh, and D.
Rubenstein, Energy-Efficient Computing for Wildlife Tracking: Design
Tradeoffs and Early Experiences with ZebraNet, ACM SIGARCH Computer
Architecture News, vol. 30, no. 5, December 2002 .
[Mainwaring+ 2002] A. Mainwaring, J. Polastre, R. Szewczyk, D. Culler, and J.
Anderson, Wireless Sensor Networks for Habitat Monitoring, 1st ACM
International Workshop on Wireless Sensor Networks and Applications
(WSNA 2002), Atlanta, Georgia, September 28, 2002.