Sensor Network Applications

1
Sensor Network Applications
2
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

3
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

4
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

5
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

6
Wireless Sensor Networks for Habitat Monitoring
Mainwaring 2002

• Habitat Monitoring
• On Kent Island, Nova Scotia, research learned
  that Leachs 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

7
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
  Leachs Storm Petrel Anderson 1995
• 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?
• What changes can be observed in the burrow and
  surface environmental parameters during the
  course of the approximately 7 month breeding
  season (April-October)?

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Wireless Sensor Networks for Habitat Monitoring
Mainwaring 2002

• Great Duck Island
• 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

9
Wireless Sensor Networks for Habitat Monitoring
Mainwaring 2002

• Great Duck Island Requirements
• 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
• 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.
• 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

10
Wireless Sensor Networks for Habitat Monitoring
Mainwaring 2002

• Great Duck Island Requirements
• 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
• 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

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Wireless Sensor Networks for Habitat Monitoring
Mainwaring 2002

• Great Duck Island Requirements
• 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
• 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

12
Wireless Sensor Networks for Habitat Monitoring
Mainwaring 2002

• Great Duck Island Requirements
• 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
• 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

13
Wireless Sensor Networks for Habitat Monitoring
Mainwaring 2002

• Great Duck Island Requirements
• 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

14
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

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Wireless Sensor Networks for Habitat Monitoring
Mainwaring 2002

• System Architecture

Figure 1 System architecture for habitat
monitoring
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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

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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

18
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 others 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

19
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

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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

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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

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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

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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
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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
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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

26
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
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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
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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

29
Wireless Sensor Networks for Habitat Monitoring
Mainwaring 2002

• Implementation Strategies
• Patch Gateways
• The differences between the mote and CerfCube
  include different
• communication frequency
• power requirements
• software components
• The motes 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

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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 time-stamped readings from the sensors,
  health status of the individual sensors, and
  metadata (e.g., sensor locations)

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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

32
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

33
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

34
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

35
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

36
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

37
Energy-Efficient Computing for Wildlife Tracking
Design Tradeoffs and Early Experiences with
ZebraNet Juang 2002

• Introduction
• This paper has the following unique
  contributions
• 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
• 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.
• ZebraNets data collection has communication
  patterns in which data can be cooperatively
  passed towards a base station
• Energy tradeoffs are examined in detail using
  real system energy measurements for ZebraNet
  prototype hardware in operation

38
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
• How to make the communications protocol both
  effective and power-efficient?
• To what extent can we rely on ad hoc,
  peer-to-peer transfers in a sparsely-connected
  spatially-huge sensor network?
• 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

39
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
  trackers overall design goals
• GPS position samples are taken every three
  minutes
• Detailed activity logs taken for three minutes
  every hour
• One year of operation without direct human
  intervention that is, not counting on
  tranquilizing and re-collaring an animal more
  than once per year
• No fixed base stations, antennas, or cellular
  service
• A high success rate for eventually delivering all
  logged data is essential while latency is not as
  critical
• For a zebra collar, a weight limit of 3-5 lbs is
  recommended

40
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

41
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

42
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-to-peer 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

43
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

44
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 coarse-grained than many prior sensor
  proposals. Larger the weight limits and storage
  budgets allow researchers to consider different
  protocols with improved leverage for
  sparsely-connected, physically-widespread sensors

45
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.