Sensors measure environmental characteristics or processes and produce an output in millivolt (mV) signals that can be recorded automatically by datalogging instruments. Sensor networks consist of a series of sensors linked by a common platform that are designed to function as a coordinated environmental observatory. Environmental sensor technology, including platform development, open-source software, cyberinfrastructure, remote power systems and data processing are rapidly expanding and hold a great deal of promise to augment the environmental processes that can be recorded automatically (Rundel et al. 2009).
Most environmental sensors were originally designed to measure micro-meterological parameters such as temperature, humidity, radiation and windspeed. However, a new suite of emerging tools is increasing the diversity of processes that can be recorded automatically. For example, sensors with the capacity for real-time recognition of chemical and genetic processes are now being deployed directly into the field (Benson et al.). Other innovative sensors are able to measure the movement of animals, the identification of insects based on species-specific wing beat frequencies, water flow through plant stems, and plant water potential. The use of remotely or automatically controlled digital cameras and video for recording environmental data is also rapidly expanding (Graham et al. 2009).
A major limitation of deploying sensors into natural systems has been power requirements. However, advances in solar cell technology have augmented the variety of sensors that can be deployed at remote sites. On the horizon are more advanced power options that include harvesting power from propagated radio waves or mechanical vibration. Efficient network design also promises to reduce power consumption and possibly allow nodes within a sensor network to distribute power requirements evenly.
The large quantity of data collected with sensor networks requires innovative ways of processing and management data. Some advanced sensor networks can be programmed for cooperative signal processing in which replicate sensors vary sampling intensity based on how quickly a process is changing. This can be accomplished by communication among sensors and programming to vary the frequency of points recorded based on the rate of change in a particular process. Effective communication among sensor nodes can be accomplished with wireless sensor networks which allow increased freedom of deployment and offer superior potential for cooperative signal processing. The drawback with wireless systems is the possibility of loss of communication, thus requiring new ways of backing up data to prevent errors and data loss.
As with any new technology, the potential for increasing our ability to observe the environment also necessitates new challenges. Nonetheless, sensor networks offer unparalleled potential for transforming our view of natural processes.
Benson BJ, Bond BJ, Hamilton MP, Monson RK, Hans R Perspectives on next-generation technology for environmental sensor networks. Frontiers in Ecology and the Environment 8:193-200
Graham EA, Yuen EM, Robertson GF, Kaiser WJ, Hamilton MP, Rundel PW (2009) Budburst and leaf area expansion measured with a novel mobile camera system and simple color thresholding. Environmental and Experimental Botany 65:238-244
Rundel PW, Graham EA, Allen MF, Fisher JC, Harmon TC (2009) Environmental sensor networks in ecological research. New Phytologist 182:589-607