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Image-based environmental monitoring sensor application using an embedded wireless sensor network.

Jeongyeup Paek1, John Hicks2, Sharon Coe3

  • 1Department of Computer Information Communication Engineering, Hongik University, Sejong 339-701, Korea. jeongyeup.paek@hongik.ac.kr.

Sensors (Basel, Switzerland)
|August 30, 2014
PubMed
Summary
This summary is machine-generated.

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This study developed image-based environmental monitoring systems using embedded wireless sensor networks. The system efficiently collected large image datasets for ecological research, proving valuable for biologists.

Area of Science:

  • Environmental Science
  • Sensor Technology
  • Ecology

Background:

  • Environmental monitoring often requires robust data collection systems.
  • Embedded wireless sensor networks offer potential for remote data acquisition.
  • Image-based sensing presents unique challenges in data transmission and storage.

Purpose of the Study:

  • To detail the development and deployment of two image-based environmental monitoring applications.
  • To evaluate the effectiveness of an embedded wireless sensor network for high-volume image data collection.
  • To assess the utility of near-real-time image access for biological research.

Main Methods:

  • Utilized low-power image sensors and the Tenet sensing system for tiered wireless sensor networks.
  • Implemented mote-side image compression to manage high data rates.

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  • Deployed pitfall trap and bird nest monitoring applications in a reserve setting.

    • Main Results:

    • Successfully deployed and operated image-based monitoring systems in variable environmental conditions.
    • Collected over 100,000 images from a 19-camera network in a three-month bird nest study.
    • Demonstrated reliable delivery of high-rate sensing data and scalability of the Tenet system.

    Conclusions:

    • Embedded wireless imaging systems are feasible for large-scale environmental monitoring.
    • Mote-side compression and reliable data delivery are crucial for success.
    • Near-real-time image access significantly aids biological data analysis and research questions.