Prometheus Wiki
Loading...
 

Sensor networks

Philip W. Rundel. Michael F. Allen, Eric A. Graham
Contributors :


Protocols that receive sufficient votes and a high star rating will be considered for Gold Leaf Status by the PrometheusWiki Editorial Board.



Protocols
Category: Sensor networks

Summary

Overview

Technological advances in sensors, sensor data logging and communication, and software management of sensor networks are providing transformative potential for new and innovative avenues ecological and ecophysiological research.

Background

Available sensors for ecological and ecophysiological research can be divided into categories of physical sensors, chemical sensors, and biological sensors, and these are summarized with comments on cost and reliability in Table 1. Physical sensors include those for temperature, relative humidity, leaf wetness, PFD and total irradiance, and wind speed and direction. The technology for physical sensors is mature and relatively stabilized due to a large commercial base for their manufacture. However, current physical sensor technology generally does not provide three-dimensional scale of measurement to allow mapping fluxes in environmental volumes and interfaces. Sensor networks are required for most such applications, with 2-D and 3-D sonic anemometers as exceptions.

Chemical sensors are widely used for measurement of atmospheric and soil CO2concentrations and fluxes, but robust sensors for inorganic, organic, and biochemistry sensing applications largely remain restricted to laboratory use. To date there are few commercially available sensors for critical chemical compounds and ions that are robust enough to measure under sustained field operation. Existing electrochemical and optical oxygen sensors are subject to biofouling after short periods of operation and require frequent calibration.

Biological sensors are those used to measure physiological functions and biological reactions in both natural and engineered ecosystems. Simple examples are sap flow sensors that provide data that can be calibrated to provide a measure of transpiration flux. Imagers, both digital cameras and soil minirhizotron cameras, are rapidly developing as important tools to provide dynamic data on both above- and belowground phenology and quantitative measures of growth dynamics. Such imagers need to be capable of long-term deployment, and should be capable of integration with other sensing devices.

The effective deployment of sensors in natural environments requires decisions on how to best provide a broad and appropriate spatial coverage of measurements for the science questions being asked. A common approach is to use multiple nodes of nested sensors, each with layers of inexpensive sensors capable of measuring temperature, radiance, and other environmental variables. Expensive, special-function instrumentation such as air flux towers and gas exchange systems can be located more sparsely. Models are available to help determine the optimum location for specialized sensors so that simple proxies derived from the higher density network, allow for broader spatial and temporal scales of data interpolation and extrapolation.

Sensor networks can be tethered (wired) or wireless, and consist of anywhere from a small number to hundreds of nodes or more, with each node connected to one to several sensors. In a wireless system, each node typically has a radio transceiver with an antenna, a microcontroller, an electronic circuit for interfacing with the sensors, and an energy source (usually a battery). Rapid technological advances have led to commercial designs for wireless sensor nodes and have lowered their cost. However, there are size and cost constraints on sensor nodes that result in corresponding constraints on resources such as energy, memory, computational speed and communications bandwidth. The architecture of wireless sensor networks can vary from a simple star network to an advanced multi-hop wireless mesh network. The propagation technique between the hops of the network can be routing or flooding.

Table 1. Examples of major sensor modalities with comments on cost, reliability, and power requirements.

Category Example Comments
Physical Temperature (e.g. thermocouple, thermistor, IR sensor) Inexpensive to intermediate cost, reliable, low power requirements
  Relative humidity Intermediate, reliable, low power
  Leaf wetness Inexpensive, reliable, low power
  Soil moisture Inexpensive to moderate, issues with calibration and measurement units, low power; many choices
  PFD, total irradiance Intermediate, reliable with calibration issues, low power
  Wind speed and direction  
  Cup anemometer Inexpensive to intermediate, reliable, fail at low wind speed. low power
  Hot wire anemometer Intermediate, less reliable, higher power
  2-D/3-D sonic anemometer Intermediate to expensive, very reliable, moderate power
Chemical Atmospheric carbon dioxide Expensive, reliable, moderate power, requires careful calibration
  Soil carbon dioxide Intermediate, reliable, low power, calibration and issue
  Soil carbon dioxide efflux Expensive, reliable, moderate power, requires careful calibration
  Nitrate sensor Expensive, under development for reliable terrestrial deployments
  Phosphorus sensor Not available for terrestrial deployments
Biological Digital imagers Moderately expensive, reliable, moderate power; high band width, software requirements
  Minirhizotron camera Expensive, variable power requirements, high band width
  Sap flow sensors Commercial probes moderate, control system needed; calibration issues
  Acoustic sensors Moderate, reliable, moderate power, high band width; software needs

 

Notes and troubleshooting tips

Both individual sensors and sensor networks are subject to problems resulting from calibration drift and/or other errors. Individual sensors require regular calibration and appropriate metadata archiving of associated information. For more complex sensor networks, automated systems to perform QA/QC have been developed because of the unwieldy amount of data that can be collected by even a modest sensor network. Some but insufficient progress has been made to develop automated fault detection systems for complex sensor networks.

 

Literature references

Allen MF, Vargas R, Graham E, Swenson W, Hamilton M. Taggart M, Harmon, TC, Rat’ko A, Rundel P, Fulkerson B, Estrin D (2007) Life within a Pixel. BioScience 57, 859-867.


Arzberge P, Bonner J, Fries D, Sanderson A (2005) Sensors for environmental observatories: report of the NSF sponsored workshop, December 2004. Baltimore, MD: World Technology Evaluation Center (WTEC) http://www.wtec.org/seo/final/Sensors_for_Environmental_Observatories.pdf


Benson BJ, Bond BJ, Hamilton MP, Monson RK, Han R (2010) Perspectives on next-generation technology for environmental sensor networks. Frontiers in Ecology and Environment 8, 193–200.


Porter J, Arzberger P, Hanson PC, Kratz TK, Gage S, Williams T, Shapiro S, Bryant P, Lin F, King H, Hanson T, Braun H, Michener W (2005) Wireless sensor networks for ecology. BioScience 55, 561-572.


Porter JH, Nagy ES, Hanson PC, et al. (2009) New eyes on the world: advanced sensors for ecology. BioScience 59, 385–97.

Rundel, PW, Graham EA, Allen MF, Fisher JS, Harmon T (2009) Tansley Review: Environmental sensor networks in ecological research. New Phytologist 182, 589–607.

 


Contributors to this page: Admin36802 points  .
Page last modified on Tuesday 03 of May, 2011 09:58:13 EST by Admin36802 points . (Version 9)