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IoT-based Edge Computing (IoTEC) for Improved Environmental Monitoring.

Javad Roostaei1, Yongli Z Wager1, Weisong Shi2

  • 1Department of Civil and Environmental Engineering, Wayne State University, 5050 Anthony Wayne Dr., Detroit, MI 48202.

Sustainable Computing : Informatics and Systems
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Summary
This summary is machine-generated.

The Internet of Things sensor network with Edge Computing (IoTEC) significantly reduces data latency and transmission, cutting costs by up to 82% for environmental monitoring. This approach also enhances power supply duration, proving its effectiveness.

Keywords:
Edge ComputingEnvironmental MonitoringInternet of Things (IoT)

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Area of Science:

  • Environmental Science
  • Computer Science
  • Engineering

Background:

  • Conventional sensor networks face challenges in data latency, energy consumption, and cost-effectiveness for environmental monitoring.
  • The integration of Edge Computing with the Internet of Things (IoT) presents a potential solution to these limitations.

Purpose of the Study:

  • To evaluate the performance of a coupled Internet of Things sensor network with Edge Computing (IoTEC) for environmental monitoring.
  • To compare IoTEC against conventional sensor monitoring methods in terms of data latency, energy consumption, and economic cost.
  • To assess the feasibility of deploying machine learning at edge servers for advanced data analysis.

Main Methods:

  • Designed and implemented two pilot applications: environmental monitoring of vapor intrusion and wastewater-based algae cultivation system performance.
  • Collected and analyzed data on data latency, energy consumption, and economic costs for both IoTEC and conventional methods.
  • Evaluated the impact of IoTEC on power supply duration and potential cost savings.

Main Results:

  • IoTEC reduced data latency by 13% and data transmission by 50% compared to conventional IoT networks.
  • The IoTEC method increased power supply duration by 130%.
  • Annual cost reductions of 55%-82% were observed for monitoring vapor intrusion, with greater savings for more houses.

Conclusions:

  • The IoTEC approach offers significant improvements in efficiency and cost-effectiveness for environmental monitoring.
  • IoTEC demonstrates feasibility for advanced data processing and analysis through machine learning deployment at edge servers.
  • This technology holds promise for more sustainable and economical environmental monitoring solutions.