Situation-Aware Avionics IoT Gateway Analytics for Low-Latency Alerts
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Solution Overview
Problem
Existing IIoT systems face inefficiencies due to fixed edge nodes that are not context-aware, requiring manual software changes as input data patterns change, leading to high maintenance costs and equipment downtime, and cloud-based analytics incur heavy bandwidth utilization and latency, limiting real-time data analysis and decision-making in avionics.
Innovation Solution
An adaptive edge platform with an intelligent software development kit (SDK) that enables context-based deployment of analytics at the edge node, allowing real-time data processing and alert generation, reducing reliance on cloud-based analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud-based analytics is used for data processing, then analytics capabilities are provided, but bandwidth utilization increases and latency increases
Solution Approach 1:
The system segments data processing into two parts: edge nodes perform local real-time analytics to filter and process data immediately, while only essential aggregated data or alerts are sent to the cloud for further analysis. This segmentation reduces the volume of data transmitted over the network, thereby reducing bandwidth utilization while maintaining comprehensive analytics capability.
Solution Approach 2:
Edge nodes act as intermediaries between data sources and the cloud. They receive raw data from devices, perform preliminary analytics and filtering, and then transmit only necessary information to the cloud. This intermediary role reduces the data load on network bandwidth while preserving analytical functionality.
2Reliability
If cloud-based analytics is used for data processing, then analytics capabilities are provided, but response time increases
Solution Approach 1:
The system segments data processing into two parts: edge nodes perform local real-time analytics to filter and process data immediately, while only essential aggregated data or alerts are sent to the cloud for further analysis. This segmentation reduces the volume of data transmitted over the network, thereby reducing bandwidth utilization while maintaining comprehensive analytics capability.
Solution Approach 2:
Edge nodes perform preliminary analytics actions locally before data reaches the cloud. They immediately process data for local conditions, generate alerts, and make real-time decisions without waiting for cloud processing. This preliminary action significantly reduces response time for time-critical analytics while the cloud provides comprehensive long-term analysis.
3Ease of manufacture
If fixed edge nodes are used, then deployment is simple, but adaptability to changing data patterns decreases
Solution Approach 1:
The edge nodes are designed with dynamic software update capabilities that allow them to adapt to changing data patterns and conditions. The system incorporates machine learning models that continuously learn from new data, enabling edge nodes to automatically adjust their analytics behavior without physical reconfiguration. This dynamic adaptability maintains deployment simplicity while significantly improving responsiveness to changing operational conditions.
Data Source
AI summary
Disclosed are methods, systems, and one or more computer-readable mediums for receiving, at an edge device from an external server, a rules database comprising a set of rules; receiving, by the edge device, first data from at least one device; processing, by the edge device, the received first data by comparing the received data to each rule among the set of rules; identifying a first triggering event in response to detecting a match between the received data and a rule of the rules database; and outputting an alert corresponding to the first triggering event.