Editorial Feature

Wireless Sensor Monitoring for Volcanoes


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Wireless sensor networks are making a valuable contribution to the monitoring of hazardous and active volcano eruptions. They are widely used for studying volcanic activities and give key insights into how volcanoes erupt. These sensors are small and economical. Three types of wireless sensor networks have been deployed on active volcanoes.

  • The Tungurahua volcano in July 2004 sparked the need to use wireless sensor technology in the active monitoring of volcanic eruptions. This type of wireless sensor can acquire infrasound data using wireless nodes.
  • The second type of wireless sensor network was deployed at Reventador in July/August 2005. It consists of 16 nodes and measures infrasonic and seismic signals with high resolution.
  • The Tungurahua volcano in August 2007 resulted in the deployment of the third type of sensor network.

In volcano monitoring, the amount of gas discharged is associated with a magma tank. The traditional way of monitoring a volcano is by placing gas samples at a crater. However, a remote spectroscopic sensing method developed by volcanologists is safer in its application than the traditional method.

These sensor networks are important in the geophysics community. An interior structure of volcanos can be studied using volcanic tomography. Mappings of the volcanic movement can be generated by collecting and analyzing signals from various stations. Accuracy and precision of these mappings increases due to the addition of numerous stations to a data collection network.

Design Principle

A data-collection network is paramount to increasing the accuracy when mapping volcanic eruptions and the development of such a wireless network satisfies scientific requirements. The following section will discuss network hardware and give an overview of the network’s operation.

Network Hardware

The sensor network, deployed on Reventador, has 16 stations with acoustic and seismic sensors. Each station consists of the following components:

  • Moteiv TMote Sky wireless sensor network node
  • Seismometer
  • 8-dBi 2.4-GHz external omnidirectional antenna
  • Custom hardware interface board
  • Microphone

The TMote Sky wireless sensor network node comprises the following components:

  • 48 kbytes of program memory
  • MSP430 microcontroller
  • 1 Mbyte of external flash memory
  • 10 Kbytes of static RAM
  • 2.4-GHz Chipcon CC2420 IEEE 802.15.4 radio

Sensor nodes are charged by alkaline D cell batteries. These batteries have a high capacity and low cost.

Typical Network Operation

Nodes transfer status messages. Seismoacoustic data nodes sample two or four channels and store this data in a local flash memory. Then, in case of an event, the node sends a message to a base station laptop.

Overcoming High Data Rates: Event Detection and Buffering

Low radio bandwidth limits current sensor-network nodes. IEEE 802.15.4 radios consist of raw data rates of about 30 Kbytes/s. However, the data rates can be reduced to less than 10 Kbytes/s by overheads caused by medium access control, packet framing, and multihop routing. A short-term average/long-term average threshold detector has been produced to calculate two exponentially weighted moving averages with many gain constants. An event detector sends a message to the base-station laptop if it catches fire.

Reliable Data Transmission and Time Synchronization

A reliable data-collection protocol is implemented to acquire buffered data from the nodes in 256 bytes blocks. They are tagged with time stamps and sequence numbers. During transmission, a sensor node splits each block into many chunks. 

Functional Principle

A sensor station comprises an array of closely-spaced wired sensors or a single sensor. Larger networks are integrated with many stations. Sensor data can be transferred to an observatory located tens of kilometers from the volcano through telephone links or long-distance radio. Data is displayed using revolving paper helicorders and is then digitized for processing. 

A volcanic eruption is associated with gas emissions. The monitoring aims to measure the concentration of sulfur dioxide upon volcanic eruption.

The requirements for volcano monitoring are as follows:

  • During operation, the system does not require human monitoring
  • The nodes are controlled by base stations
  • Accurate measurement results are obtained
  • Before measurement, sensor nodes are calibrated
  • Transmitted data is accurately tested to prevent corruption or loss of data
  • Sensor nodes are located in an appropriate place according to the wind strength
  • The system uses an error log to record errors during operations

Case Study

Volcán Reventador is situated in northern Ecuador. In 2002, Reventador volcano erupted suddenly, with a massive force, and from a dormant state. A pyroclastic flow resulted in the displacement of an oil pipeline and flattened the forests. Reventador’s seismic activity consisted of tremors, explosion earthquakes, and shallow-rock-fracturing earthquakes related to magma migration within the volcano.

Here, a pelican case – watertight and weatherproof, was installed with an interface board, a single sensor network node, and a battery holder. The pelican case was used for installing all environmental connectors: attaching the cables to an antenna and external sensors without disturbing the equipment and opening the case. Rocks prevented the components from being exposed to direct sunlight while tightly anchoring the case, therefore forming a station. Seismometers were buried, and a microphone was placed on an antenna pole.

A rough and linear configuration was used for installing the station. This produced an aperture of about 3km and radiated away from the vent of the volcanoes. A long-distance reliable radio link was established between an observatory laptop and a sensor network using three freewave radio modems. The sensor network was deployed at Volcán Reventador for more than three weeks acquiring seismoacoustic signals. The sensor network recorded 107 Mbytes of data and about 230 eruptions and other volcanic events. This demonstrated that global and local event detectors are working properly.

Future Research and Conclusion

Wireless sensor networks find interesting applications in seismology. Low-power, wireless sensors are generally used for increasing the signal-to-noise ratio and spatial resolution. The deployment of sensor networks to monitor volcanoes is essential and could help in developing more sophisticated tools for volcanic monitoring in the coming years. The following video presented by Washington State University researchers, US Geological Survey, and NASA's Jet Propulsion Lab demonstrates clearly how research in this field is investigating the use of smart sensor networks to provide real-time information on volcano status. The spider sensors in this video are airdropped, multi-legged robots, investigating mapped test sites around volcanos.

Spider Sensors Helping Predict Volcanic Activity

Future work in the field will focus on expanding the sensor array and sensor distribution across a broader aperture, thus enhancing the resolution of volcano instrumentation. Adding seismic sensors to the array could provide a multimodal view of volcanic activity.

There are also plans to develop distributed algorithms to help achieve source back-projection and schemes that refine acoustic and seismic signals. To benefit research groups working on volcanic studies, a permanent, reprogrammable sensor is expected to be deployed on Volcan Tungurahua. A high-level language framework is also likely to be generated to reprogram sensor arrays.

Sources and Further Reading

  • Zhang. Y, Wireless sensor network for volcano monitoring, 2005, pp 1-49.
  • Werner-Allen. G, Lorincz.K, Welsh.M, Marcillo. O, Johnson.J, Ruiz.M, Lees.J, Deploying a WirelessSensor Network on an Active Volcano, IEEE Computer Society, 2006, pp 18-25.
  • Werner-Allen. Johnson.J, Ruiz.M, Lees.J, Welsh.M, Monitoring Volcanic Eruptions with a Wireless Sensor Network, pp 1-13.

This article was updated on 17th February, 2020.


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