Aerial Data Relay for Edge-Cloud Computing in Network-Constrained Farms
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Solution Overview
Problem
Farmers face challenges in connecting data from farm machinery and in-field sensors to cloud analytics systems due to poor network connectivity, especially in rural areas, leading to unreliable data transfer and loss, which hampers real-time decision-making and overall crop yield.
Innovation Solution
A method and system that enable data-centric computing by integrating device, edge, and cloud computing infrastructure for reliable data transfer and processing in digital agriculture systems. This involves standardizing IoT data ingestion and normalization, dividing and distributing data-processing workloads, automatically scaling data-processing tasks, and simplifying AI/ML inferencing deployments to edge devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If cloud-based analytics systems are used for processing agricultural data, then data processing capability is improved, but network connectivity requirements increase
Solution Approach 1:
The system segments computing resources into edge computing nodes deployed at farm locations and cloud-based analytics systems. Edge nodes process data locally using embedded processors, while cloud systems handle complex analytics when connectivity is available. This segmentation allows the system to maintain data processing capability without requiring continuous reliable network connectivity.
Solution Approach 2:
The patent introduces data storage intermediaries (local storage devices at farm locations) that buffer data between field sensors and cloud analytics systems. These intermediaries store data when network connectivity is unavailable and transmit it when connectivity is restored, decoupling the reliability of data collection from network connectivity.
2Reliability
If data is stored locally using USB sticks, then network connectivity requirements are reduced, but data loss risk increases
Solution Approach 1:
The system creates multiple copies of data across distributed storage locations including local edge devices, farm-level storage, and cloud systems. This redundancy ensures that data loss at any single location does not result in complete data loss, while still maintaining network independence for critical local operations.
Solution Approach 2:
The patent implements different storage quality levels at different locations: critical data is stored with high redundancy locally at farm locations using robust storage media, while less critical data uses standard cloud storage. This local quality differentiation reduces overall data loss risk while maintaining network independence for essential operations.
3Device complexity
If manual data collection methods are used, then infrastructure complexity is reduced, but productivity decreases
Solution Approach 1:
The system implements automated data collection through sensors embedded in farm machinery that self-transmit data to edge computing nodes without manual intervention. The system automatically manages data storage, processing, and transmission, eliminating the need for manual USB stick handling while maintaining simple infrastructure at individual farm locations.
Data Source
AI summary
The present invention is for an autonomous aerial vehicle that enables near real-time and offline data processing among heterogenous devices that are in unreliable or unconnected network service areas, wherein the heterogenous devices are associated with heavy industrial systems. The autonomous aerial vehicle may obtain data from a first physical asset, and segment the obtained data as suitable for a local area compute node and/or a cloud compute node. The autonomous aerial vehicle may identify a location associated with the one or more destination devices and may compute a flight path to the destination location. The aerial device may hereafter travel to the destination location and upload relevant data to the at least one destination upon arrival.