Edge Computing for Agricultural Monitoring: A Bandwidth-Efficient IoT Approach for Real-Time Sensor Data Analysis

This paper presents an edge computing architecture for agricultural monitoring systems that addresses bandwidth constraints while maintaining real-time alerting capabilities. Traditional cloud-centric approaches to agricultural IoT often struggle with network limitations, particularly in rural environments with unreliable connectivity. Our implementation leverages Node-RED as an edge processing platform to filter, aggregate, and selectively transmit sensor data from temperature, soil moisture, and humidity sensors. Through a 24-hour simulation of typical agricultural conditions, we demonstrate a 77% reduction in data transmission requirements compared to traditional cloud-based approaches, transmitting only 1,984 of 8,641 total data points while preserving all critical information. The system maintains responsive alert detection for potentially harmful conditions including excessive temperatures and inadequate soil moisture levels. Rolling averages implemented at the edge effectively reduce noise while maintaining trend visibility and enabling meaningful pattern analysis. The architecture balances local processing with selective cloud transmission, creating a highly efficient monitoring system suitable for bandwidth-constrained agricultural environments. This approach demonstrates the practical value of edge computing for making smart farming technologies more accessible and cost-effective, particularly in regions with limited connectivity options.