How Farmo IoT Devices Use Edge Processing for Real-Time Data Reporting
Farmo IoT devices use edge processing to perform data analysis and decision-making locally—right on the device—before sending any information to the cloud. This approach improves responsiveness, reduces data load, and extends battery life.
Download the factsheet.
1. Data Acquisition and Local Processing
Sensors collect raw data (e.g., temperature, pressure, GPS position).
Instead of transmitting every reading, the onboard processor runs algorithms to:
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Detect anomalies when readings are above or below user-defined thresholds.
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Aggregate or compress readings by comparing sequential measurements to identify trends.
Example:
A water tank monitor checks if the current level breaches a threshold, and then also compares it with previous readings to detect a rapid drop. Data is sent only when relevant conditions are met, instead of continuously streaming data.
2. Reduced Latency for Real-Time Actions
By analyzing data locally, the device avoids delays caused by sending information to the cloud at set report periods.
This enables instant actions, such as:
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Sending alerts when thresholds are crossed
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Activating actuators (e.g., closing a valve if a leak is detected)
Applications:
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Flow meters – update when water flowing only
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Livestock water tanks – immediate alerts when faults occur
3. Bandwidth and Power Efficiency
Only essential or summarized data is sent, reducing bandwidth use and conserving energy—allowing devices to run for years on a single battery.
Example:
A water pressure sensor sends 4 hour summaries unless it detects a sudden spike, in which case it transmits detailed readings immediately.
4. Fail-Safe Operation When Offline
If connectivity is lost, the device will:
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Continue collecting and processing data locally
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Automatically upload missing data when the connection is restored
Example:
A rain gauge can store several days’ worth of rainfall data while offline, then sync all records once it reconnects.
💡 In short:
Edge processing transforms IoT devices from passive data collectors into active decision-makers. They provide instant insights, minimize unnecessary transmissions, and remain operational even when networks are unreliable.
