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

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.