Dynamic Streaming Analytics for Edge IoT Automation
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Solution Overview
Problem
Conventional sensors and edge solutions lack the ability to efficiently integrate with systems at the edge to automatically create transactions based on IoT sensor data insights, overwhelming available resources and requiring manual logic updates.
Innovation Solution
The system incorporates edge services that allow policies to be set and distributed from the cloud, enriching raw data with business context at the edge, converting it into meaningful insights for decision-making and triggering actions without a round-trip to the cloud, using streaming analytics and dynamic rule configuration to automate processes and workflows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual logic configuration is used for edge analytics, then system reliability is maintained, but adaptability deteriorates when rules and values need to change
Solution Approach 1:
The system transitions from static manual configuration to dynamic automated configuration. Rules and values are automatically updated based on streaming analytics of sensor data, allowing the system to adapt to changing conditions without manual intervention. The configuration changes are applied in real-time without requiring system restarts.
Solution Approach 2:
The edge analytics system performs self-configuration by automatically generating and updating rules based on sensor data patterns. The system monitors its own performance and autonomously adjusts parameters, eliminating the need for manual reconfiguration while maintaining operational reliability.
2Productivity
If more intelligence is added at the edge for automated transactions, then productivity is improved, but device complexity increases
Solution Approach 1:
The system segments intelligence between edge devices and cloud infrastructure. Edge devices perform local streaming analytics and automated transactions using simplified logic, while complex rule generation and model updates are handled by cloud-based services. This distribution allows edge productivity to improve without proportionally increasing edge device complexity.
Solution Approach 2:
A streaming analytics service acts as an intermediary between sensor data and automated transactions. This service processes data streams, generates insights, and triggers actions automatically, enabling edge devices to perform complex transactions without requiring full intelligence locally, thus improving productivity while managing complexity.
3Adaptability or versatility
If conventional edge solutions are used, then ease of operation is maintained, but adaptability deteriorates due to inability to automatically create transactions
Solution Approach 1:
The system automatically creates transactions and updates configurations based on sensor data patterns without requiring manual programming. The automated rule generation and transaction creation processes maintain ease of operation while dramatically improving adaptability to changing business requirements.
Solution Approach 2:
The system implements continuous feedback loops where sensor data is analyzed, insights are generated, and transactions are automatically created or modified. This feedback mechanism enables the system to adapt to new conditions automatically while maintaining simple operation through automated decision-making processes.
Data Source
AI summary
A method includes, at an edge location, receiving one or more threshold settings associated with a sensor, configuring a rule specifying a trigger condition for a task, wherein the trigger condition is based on the one or more threshold settings, receiving a sensor data stream from the sensor, determining that the trigger condition has been satisfied, and responsive to the determination that the trigger condition has been satisfied, automatically executing the task specified by the rule.