Developing a Scalable IoT Architecture for Precision Agriculture Using Edge Computing

Summary:

IoT and edge computing can be combined to improve precision agriculture. The main problem is that farms generate a huge amounts of data from sensors like moisture meters, weather monitors, and crop health cameras, and sending all that data to the cloud creates delays and bandwidth issues. Architecture processes most of the data at edge nodes located near the fields, which reduces latency and improves real-time decision-making (like irrigation timing). Tests of the system using real agricultural datasets found that it handles scaling pretty well, up to 200 devices, while keeping latency reasonably low. Insight accuracy for things like crop health and soil moisture was also pretty high.

Key Points:

  1. Precision agriculture benefits from IoT but suffers from big-data and scalability issues.
  2. Edge computing reduces latency, bandwidth use, and privacy risks.
  3. Demonstrated:
    • Latency ~118 ms
    • High scalability up to 200 devices
    • Soil moisture insight accuracy 92%, weather prediction 95%
  4. Resource usage remains efficient (CPU 60%, memory 55%).

Images

PrecisionFlowChartMethodology
Figure 1: Flowchart for Proposed Methodology





Number of Devices # Processing Time (s) Throughput (requests/s)
150 60 160
200 85 140
Figure 2: SYSTEM SCALABILITY
PrecisionAccuracyOfInsights
Figure 3: Shows Accuracy of Insights

Bibliography Citation:

P. V. Rao, N. Varshney, N. K. Sundaram, B. Kishore, S. O. Husain and N. Kanys,
"Developing a Scalable IoT Architecture for Precision Agriculture Using Edge Computing,"
2024 International Conference on IoT, Communication and Automation Technology (ICICAT), Gorakhpur, India, 2024,
pp. 976–980, doi: 10.1109/ICICAT62666.2024.10923487.