IoT Hydroponic Farm Zone Controllers Reduce Latency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional IoT-based hydroponic and aquaponic systems face increased server load, leading to delayed data processing and transmission latency, which can result in malnutrition, overfeeding, or extreme exposure to light for plants, impacting growth and production.
Innovation Solution
An IoT-based hydroponic farm system utilizing a cloud server with a data distribution service (DDS) domain that employs a real-time publish-subscribe protocol for wireless communication between hydroponic plant growing zones, zone controllers, farm controllers, process controllers, and user computing stations, enabling seamless integration and real-time decision-making across all stages of hydroponic production and supply chain management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional IoT-based hydroponic systems use traditional server architectures for data processing, then system functionality is maintained, but server load increases causing delayed data processing and transmission latency
Solution Approach 1:
The system segments the server architecture into distributed edge computing nodes deployed at different levels (zone controllers, farm controllers, cloud servers). This segmentation allows data processing to occur locally at each level rather than centralizing all processing at a single server, reducing the load on individual servers and enabling parallel processing of data from multiple zones, thereby decreasing transmission latency and improving reliability.
Solution Approach 2:
The patent introduces a hierarchical multi-dimensional architecture with five distinct levels: zone controller level, farm controller level, process control level, production planning level, and enterprise resource planning level. This dimensional expansion allows data to be processed at multiple levels simultaneously, with lower levels handling real-time control and higher levels managing strategic decisions, thereby distributing the processing load and reducing overall system latency.
2Productivity
If real-time monitoring and control actions are implemented, then plant growth optimization is improved, but system complexity increases
Solution Approach 1:
The control system is segmented into autonomous zone controllers that operate independently at the local level. Each zone controller has the authority to make real-time decisions based on sensor data from its specific zone without requiring constant intervention from higher-level systems. This segmentation enables real-time monitoring and control optimization while distributing complexity across multiple independent units rather than concentrating it in a single complex system.
Solution Approach 2:
The system implements multi-level feedback mechanisms where sensor data flows upward through the hierarchy and control actions flow downward. Zone controllers receive feedback from local sensors and automatically adjust conditions, while higher levels receive aggregated data and provide strategic guidance. This feedback structure enables real-time optimization of plant growth while using simple rule-based local control rather than complex centralized control algorithms.
Data Source
AI summary
An internet of things (IoT) based hydroponic farm system is described. The hydroponic farm system includes a cloud server, a plurality of hydroponic farms, and a plurality of user computing stations. The cloud server includes a data distribution service domain that includes data distribution service middleware configured to use a real-time publish-subscribe protocol to wirelessly communicate hydroponic farm information. Each of the hydroponic farms is divided into a plurality of hydroponic plant growing zones that are configured to grow at least one species of plant. Each hydroponic plant growing zone includes a zone controller. A farm controller is assigned to each of the hydroponic farms. The plurality of user computing stations are subscribed to the data distribution service middleware and are configured to communicate, through the data distribution service domain, with the zone controller, the farm controller, a process controller, a production planning controller, and an enterprise resource planning controller.