SYSTEM ARCHITECTURE

We now describe the system architecture, functionality
of individual components and how they operate together.
We explain how they address the requirements set forth in
Section 2.
We developed a tiered architecture. The lowest level consists
of the sensor nodes that perform general purpose computing
and networking in addition to application-specific
sensing. The sensor nodes may be deployed in dense patches
that are widely separated. The sensor nodes transmit their
data through the sensor network to the sensor network gateway.
The 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. Finally, the data is displayed
to scientists through a user interface. Mobile devices, which
we refer to as the gizmo, may interact with any of the networks
– whether it is used in the field or across the world
connected to a database replica. The full architecture is
depicted in Figure 1.
The lowest level of the sensing application is provided by
autonomous sensor nodes. These small, battery-powered
devices are placed in areas of interest. Each sensor node
collects environmental data primarily about its immediate
surroundings. Because it is placed close to the phenomenon
of interest, the sensors can often be built using small and inexpensive
individual sensors. High spatial resolution can be
achieved through dense deployment of sensor nodes. Compared
with traditional approaches, which use a few high
quality sensors with sophisticated signal processing, this architecture
provides higher robustness against occlusions and
component failures.
The computational module is a programmable unit that
provides computation, storage, and bidirectional communication
with other nodes in the system. The computational
module interfaces with the analog and digital sensors on the
sensor module, performs basic signal processing (e.g., simple
translations based on calibration data or threshold filters),
and dispatches the data according to the application’s needs.
Compared with traditional data logging systems, networked
sensors offer two major advantages: they can be retasked in
the field and they can easily communicate with the rest of
the system. In-situ retasking allows the scientists to refocus
their observations based on the analysis of the initial results.
Suppose that initially we want to collect the absolute temperature
readings; however after the initial interpretation
of the data we might realize that significant temperature
changes exceeding a defined threshold are most interesting.
Individual sensor nodes communicate and coordinate with
one another. The sensors will typically form a multihop network
by forwarding each other’s messages, which vastly extends
connectivity options. If appropriate, the network can
perform in-network aggregation (e.g., reporting the average
temperature across a region). This flexible communication
structure allows us to produce a network that delivers the
required data while meeting the energy requirements. We
expand on energy efficient communication protocols in Section
6.

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Great Duck Island Requirements

• Internet access
  The sensor networks at GDI must be accessible via the
  Internet. An essential aspect of habitat monitoring applications
  is the ability to support remote interactions with
  in-situ networks.
• Hierarchical network
  The field station at GDI needs sufficient resources to host
  Internet connectivity and database systems. However, the
  habitats of scientific interest are located up to several kilometers
  further away. A second tier of wireless networking
  provides connectivity to multiple patches of sensor networks
  deployed at each of the areas of interest. Three to four
  patches of 100 static (not mobile) nodes is sufficient to start.
• Sensor network longevity
  Sensor networks that run for 9 months from non-rechargeable
  power sources would have significant audiences today. Although
  ecological studies at GDI span multiple field seasons,
  individual field seasons typically vary from 9 to 12 months.
  Seasonal changes as well as the plants and animals of interest
  determine their durations.
• Operating offthegrid
  Every level of the network must operate with bounded energy
  supplies. Although renewable energy, for example solar
  power, may be available at some locations, disconnected operation
  remains a possibility. GDI has sufficient solar power
  to run many elements of the application 24x7 with low probabilities
  of service interruptions due to power loss.
• Management atadistance
  The remoteness of the field sites requires the ability to
  monitor and manage sensor networks over the Internet. Although
  personnel may be on site for a few months each summer,
  the goal is zero on-site presence for maintenance and
  administration during the field season, except for installation
  and removal of nodes.
• Inconspicuous operation
  Habitat monitoring infrastructure must be inconspicuous.
  It should not disrupt the natural processes or behaviors under
  study. Removing human presence from the study areas
  both eliminates a source of error and variation in data collection,
  as well as a significant source of disturbance.
• System behavior
  From both a systems and end-user perspective, it is critical
  that sensor networks exhibit stable, predictable, and repeatable
  behavior whenever possible. An unpredictable system
  is difficult to debug and maintain. More importantly,
  predictability is essential in developing trust in these new
  technologies for life scientists.
• Insitu interactions
  Although the majority of interactions with the sensor networks
  are expected to be via the Internet, local interactions
  are required during initial deployment, during maintenance
  tasks, as well as during on-site visits. PDAs serve an important
  role in assisting with these tasks. They may directly
  query a sensor, adjust operational parameters, or simply assist
  in locating devices.
• Sensors and sampling
  For our particular applications, the ability to sense light,
  temperature, infrared, relative humidity, and barometric pressure
  provide an essential set of useful measurements. The
  ability to sense additional phenomena, such as acceleration/
  vibration, weight, chemical vapors, gas concentrations,
  pH, and noise levels would augment them.
• Data archiving
  Archiving sensor readings for off-line data mining and
  analysis is essential. The reliable offloading of sensor logs to
  databases in the wired, powered infrastructure is an essential
  capability. The desire to interactively “drill-down” and explore
  individual sensors, or a subset of sensors, in near realtime
  complement log-based studies. In this mode of opera-

Figure 1: System architecture for habitat monitoring
tion, the timely delivery of fresh sensor data is key. Lastly,
nodal data summaries and periodic health-and-status monitoring
requires timely delivery.

Great Duck Island

The College of the Atlantic (COA) is field testing in-situ
sensor networks for habitat monitoring. COA has ongoing
field research programs on several remote islands with
well established on-site infrastructure and logistical support.
Great Duck Island (GDI) (44.09N,68.15W) is a 237 acre island
located 15 km south of Mount Desert Island, Maine.
The Nature Conservancy, the State of Maine and the College
of the Atlantic hold much of the island in joint tenancy.
At GDI, we are primarily interested in three major questions
in monitoring the Leach’s Storm Petrel [2]:
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)?
3. What are the differences in the micro-environments
with and without large numbers of nesting petrels?
Each of these questions has unique data needs and suitable
data acquisition rates. Presence/absence data is most
likely acquired through occupancy detection and temperature
differentials between burrows with adult birds and burrows
that contain eggs, chicks, or are empty. Petrels are
unlikely to enter or leave during the light phase of a 24 hour
cycle, but measurements every 5-10 minutes during the late
evening and early morning are needed to capture time of
entry or exit. More general environmental differentials between
burrow and surface conditions during the extended
breeding season can be captured by records every 2-4 hours,
while differences between “popular” and “unpopular” sites
benefit from hourly sampling, especially at the beginning of
the breeding season.
It is unlikely that any one parameter recorded by wireless
sensors could determine why petrels choose a specific nest
site, rather we hope that by making multiple measurements
of many variables we will be able to develop predictive models.
These models will correlate which conditions seabirds
prefer.

HABITAT MONITORING

Researchers in the Life Sciences are becoming increasingly
concerned about the potential impacts of human presence in
monitoring plants and animals in field conditions. At best it
is possible that chronic human disturbance may distort results
by changing behavioral patterns or distributions, while
at worst anthropogenic disturbance can seriously reduce or
even destroy sensitive populations by increasing stress, reducing
breeding success, increasing predation, or causing a
shift to unsuitable habitats. While the effects of disturbance
are usually immediately obvious in animals, plant populations
are sensitive to trampling by even well-intended researchers,
introduction of exotic elements through frequent
visitation, and changes in local drainage patterns through
path formation.

Disturbance effects are of particular concern in small island
situations, where it may be physically impossible for
researchers to avoid some impact on an entire population. In
addition, islands often serve as refugia for species that cannot
adapt to the presence of terrestrial mammals, or may
hold fragments of once widespread populations that have
been extirpated from much of their former range.

Seabird colonies are notorious for their sensitivity to human
disturbance. Research in Maine [2] suggests that even 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 will lead to complete abandonment of
the colony. On Kent Island, Nova Scotia, researchers found
that Leach’s Storm Petrels are likely to desert their nesting
burrows if they are disturbed during the first 2 weeks of
incubation.

Sensor networks represent a significant advance over traditional
invasive methods of monitoring. Sensors can be
deployed prior to the onset of the breeding season or other
sensitive period (in the case of animals) or while plants are
dormant or the ground is frozen (in the case of botanical
studies). Sensors can be deployed on small islets where it
would be unsafe or unwise to repeatedly attempt field studies.
The results of wireless sensor-based monitoring efforts
can be compared with previous studies that have traditionally
ignored or discounted disturbance effects.

Finally, sensor network deployment may represent a substantially
more economical method for conducting long-term
studies than traditional personnel-rich methods. Presently,
a substantial proportion of logistics and infrastructure must
be devoted to the maintenance of field studies, often at some
discomfort and occasionally at some real risk. A “deploy ’em
and leave ’em” strategy of wireless sensor usage would limit
logistical needs to initial placement and occasional servicing.
This could also greatly increase access to a wider array of
study sites, often limited by concerns about frequent access
and habitability.

Improving Life and Industry with Wireless Sensors

Intel Research, working with the academic community and industry, is addressing many of the significant challenges for ad hoc sensor networks to become a reality. Already, a broad spectrum of sensor network pilot applications have been demonstrated. As sensor network technology emerges from research laboratories, the ability to instrument the world is likely to transform every facet of our lives. New Uses, New Users:

Intel and BP, one of the world's largest petroleum and petrochemicals companies, are collaborating on a joint research project using a wireless sensor network to provide continuous vibration monitoring of the engines on one of BP's oil tankers off the Shetland Islands in northern Scotland. View Video (WMV file, 10MB; requires Media Player).
Smart Surrogates, by Terry Knott, BP Frontiers magazine, Issue 9, April 2004BP is at the forefront of applying the latest sensory network digital technology to a broad spectrum of its businesses. Learn more.

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Instrumenting the World: An Introduction to Wireless Sensor Networks
The Promise of Wireless Sensor Networks
Digital Home Technologies for Aging in Place
Sensor Network Technology
SCIENTIFIC AMERICAN: Smart Sensors to Network the World by David Culler and Hans Mulder. An emerging class of microelectronic devices are enabling us to more freely connect the cyberworld to the real world.
More Info

Wireless Sensor Network Topologies

Sensors
The development of network technologies has prompted sensor folks to consider alternatives that reduce costs and complexity and improve reliability. Early sensor networks used simple twisted shielded–pair (TSP) implementations for each sensor. Later, the industry adopted multidrop buses (e.g., Ethernet). Now we’re starting to see true web-based networks (e.g., the World Wide Web) implemented on the factory floor.
As wireless sensors become real commodities on the market, new options or new arguments for old options are causing professionals to consider network strategies once ruled out. Let’s look at the three classic network topologies (point-to-point, multidrop, and web), assess their strengths and weaknesses, and look at how the rules have changed now that wireless systems are coming online.

In addition, to build functional sensor networks, you’ll probable have to integrate hardware and software from multiple vendors (see the sidebar “Network Questions,”). So along with everything else, you have to come to terms with standards and protocols—those that exist, those that are emerging, and those needed to ensure interoperability on the factory floor.
Point-to-Point NetworksTheoretically, these systems are the most reliable because there is only one single point of failure in the topology—the host itself (see Figure 1). You can improve the system by adding redundant hosts, but wiring two hosts can be a problem. The 4–20 mA standard allows multiple readout circuits if the standard loads are used at each readout. Problems can arise if readout devices load the circuit beyond its capability, but most designers are familiar with the limitations and are sufficiently careful.

Some networks provide frequency-modulated (FM) signals on the wires to carry multiple sensor readings on separate FM channels. Some standards (e.g., the HART bus) support multiplexing of digital signals on the existing analog wiring in older plants. These architectures blur the distinction between point-to-point and multidrop networks.
Early wireless networks were simple radio-frequency (RF) implementations of this topology. These networks used RF modems to convert the RS-232 signal to a radio signal and back again. Fluke (Everett, Wash ington) developed a digital voltmeter that could be configured to accept a voltage signal and transmit the signal over a dedicated radio frequency channel. The reliability of these implementations was sometimes suspect because most were designed with simple FM coding. Interference and multipath propagation effects caused significant degradation in factory environments, so many networks proved to be unreliable unless designers were particularly careful. The Federal Communications Commission licensed companies and devices to operate at the allocated frequencies.

Complete wireless local area networks (LANs) were implemented using this technique.These were successful in the office environment but didn’t fare as well in factories. Many designers implemented remote data acquisition systems with this topology by using a data concentrator in the field to feed the data to a radio transmitter for transmission to the hosts, where the signals were demultiplexed into the original sensor signals.

Multidrop Networks Multidrop buses began to appear in the late 70s and early 80s. One of these, Modbus from Modicon (Schneider Auto mation, North Andover, Massa chusetts), led the way into the industrial sphere, followed by several proprietary and open buses (e.g., the Manufacturing Auto mation Protocol, QBus, and VME Bus).The emergence of intelligent sensors and microcomputers capable of operating in industrial environments irrevocably changed the sensor network landscape. Multidrop networks (buses) reduced the number of wires required to connect field devices to the host, but they also introduced another single point of failure—the cable. Several suppliers of industrial-grade products offered redundant cabling designs, but these came with an increase in complexity.

Once the industry began the migration to multidrop buses, problems associated with digitization began to emerge. With the previous point-to-point systems, digitization occurred in the host, where a single clock could be used to time stamp when the analog signals from multiple sensors were acquired. With the distributed intelligence required to implement a multidrop network, synchronization of clocks became a critical issue in some applications. This remains an important design parameter for any distributed digital system.The introduction of Ethernet in the mid-80s was a landmark in standardization, if not technological innovation. A group of large companies agreed that the future of computer networking required an open interconnect standard that would allow multiple-vendor systems to work together with minimal difficulty.

Researchers looked closely at the carrier sense multiple access with collision detection (CSMA/CD) protocol when they investigated the behavior of networks under stress. But they considered most industrial applications too time critical for such a nondeterministic protocol. Now, fifteen years later, most factories have converted their shop floor networks to Ethernet because it is the best compromise between cost and performance. Many companies now offer solutions that use Ethernet to implement suitable robust industrial networks.

Wireless systems use the same types of protocols to implement multidrop topologies, simulating hard-wired connections with RF links. The IEEE-802.11 standard was the first wireless standard that promised to bring the interoperability of Ethernet connectivity to wireless networks. Many of these, however, are not compatible at the over-the-air level.