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Data Collection

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Prepared by

Bjorn Freeman-Benson (Object Technology International)
and Brian Paisley (Forest Technology System)


Desired Programs

Use Cases


A local forest technology company, Forests 'R' Us, wants to build and sell a system for gathering and analyzing weather information to predict forest fires and help with water table management. The Arbor2000 will be sold to National Forests, Environment Canada, the U.S. Forest Service, and large private land owners. It will consist of hardware and software both locally in the owner's office building and remotely in the forests.

The data sensors in the forest report at various intervals to the central computer via satellite, packet radio, cell phone, dial-up phone, or dedicated line. The central computer stores and analyzes the information. The users run a wide variety of reports, browsers, historical trend analysis, and future prediction algorithms over the data. Furthermore, given the inherently geographic nature of the data, many of the reports incorporate maps.

The sensors, such as temperature, sunlight intensity, wind speed and direction, rainfall, and so on, come in three basic types:

  1. those that report on a regular basis (every minute, hour, day, month),
  2. those that only report when a significant event occurs (a certain amount of rain has fallen, the temperature rises above a threshold), and
  3. those that must be queried.

Some sensors fall in multiple groups, for example, they report events but can also be queried.

The sensors are produced by different manufacturers and return numeric values in a wide variety of units (miles/hour, km/hour, lumens, watts, calories/day, etc.) and at widely varying intervals and tolerances.

Additionally, the data links are not necessarily reliable, and yet the system must deal with all these issues while presenting both a uniform and a detailed view of the data to the user and his or her agent/analysis programs.

Desired Programs

Forests 'R' Us needs three categories of programs:

  1. one to gather the sensor data as it arrives and store it in a database,
  2. one to configure the field sensors, and
  3. the one to provide the user interface for browsing and analyzing the data.

Gathering the sensor data is relatively simple: the field sensors send information packets to the central computer, and the central computer stores them. Each packet contains the sensor ID, the time stamp, and the numeric sensor measurement. For cost reasons, many sensors are grouped into sensing units which send their data together (e.g., wind speed, direction, humidity, and temperature) via one phone call rather than four separate calls.

Configuring the field sensors consists of telling the main program where each sensor is physically located and what type of sensor it is. Additionally, many sensors have different settings for measurement units and errors, reporting intervals, etc., so these too are configured. Because this is a 7x24 system, sensors can be replaced at any time, usually with an upgraded model and thus with different measure units, error tolerances, etc.

The browsing and analyzing programs are the heart of the system. The analysis algorithms provide fire danger ratings, water table estimates, flash flood warnings, and so on. The browsing interfaces provide detailed information, both tabular and geographic, from the database. For example, the temperature maps similar to those seen on the evening news are one of the possible graphical outputs. The user should be able to navigate through the information in many ways including:

  1. Map browsing multiple sensor types (temperature and rainfall) or multiple time periods (temperature over the previous month).
  2. Browsing the type and status of the sensors at any location or locations.
  3. Browsing the reliability and age of the information for any sensor and/or location.

To provide for future expansion, each of the numeric values available for display (e.g., temperature, rainfall, fire danger, flash flood risk, etc.) should be computed via a plug-in module. (Forests 'R' Us intends to sell additional modules for other risk factors, such as earthquake prediction, in the future.)

Use Cases

Use Case #1

There are sixteen sensor groups, each with three or four sensors, placed in the Rumbling Range National Forest. The sensors are randomly chosen from rainfall, temperature, sunlight, wind speed, wind direction, and snowpack sensors. The sensors report from once a minute to once a day and in a variety of units.

Jane Arden, a National Park Service Ranger, wants to post the fire danger results outside the Visitor Information Center, so she uses the Arbor2000 to examine the graphical view of fire danger in the forest. Overall, the fire danger is "moderate" with one area of "low danger + high uncertainty". Looking into the uncertain area, she finds that a number of the sensors have not reported for quite a while, leading to the uncertainty. Further investigation reveals that none of the sensors in groups 2 and 4 have reported, and further checking shows that groups 2 and 4 are the only two which use the 555-3473 phone modem. She dispatches a repair crew to figure out the problem with the phone line while she posts the "moderate" fire danger sign in front of the visitor's center. She also checks the fire danger last year, and finds out that it was "low" over the entire forest, so she calls the Rumbling Range Spokesman-Review and asks them to print a story about how the fire danger is higher this year due to lower than expected rainfall.

Use Case #2

The Rumbling Range National Forest buys two additional sensor arrays and hires a helicopter crew to plant them in the forest. After they return with Global Positioning System confirmation of the Latitude and Longitude of the sensors, Jane configures the system to receive the new data. Fortunately, the Arbor2000 is clever enough to store the unidentified incoming data until Jane had time to indicate where the arrays were located and what sensor types they were.

Use Case #3

Forests 'R' Us comes out with a new plug-in module that it generously gives away free over the Internet. This new module computes trend analysis of the sunlight sensors to detect premature failure. Ms. Arden downloads and runs the module against the Rumbling Range database, only to discover that sensor #372 on Bald Mountain shows signs of age -- its measured output has slowly declined over the past four years. Jane decides to hike to the top of the mountain and replace the sensor.

When she reaches the top, she discovers that the problem is not the sensor, but rather a small pine tree shielding the sensor from the sun. Unwilling to cut down the only tree on Bald Mountain, she relocates the sunlight sensor 100 meters to the south. When she returns to base, she updates the database with the sensor's new location.

Last updated by Torsten Layda, SWX Swiss Exchange, DesignFest® Webmaster.