IoT Edge Inferencing Hierarchy for Low-Latency Reliable Alerts
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing IoT systems face challenges with high communication costs, latency, and reliability due to reliance on remote cloud computing, especially when devices are disconnected or require real-time data processing.
Innovation Solution
Implementing an edge compute continuum with a hierarchy-based architecture that distributes computing resources across various nodes, allowing devices to request and receive data efficiently from a localized hierarchy of computers, reducing the need for cloud communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data processing is performed at remote cloud servers, then centralized data management is achieved, but communication latency and operational costs increase
Solution Approach 1:
The patent segments the centralized cloud processing function into distributed edge computing nodes deployed throughout the network. Each edge node processes data locally, eliminating the need for all data to travel to remote cloud servers. This segmentation reduces communication latency while maintaining reliable data management through the distributed architecture.
Solution Approach 2:
The patent introduces edge computing nodes as intermediary elements between end devices and cloud servers. These intermediaries perform data processing, filtering, and preliminary analysis locally, reducing the volume of data that needs to be transmitted to the cloud and thereby reducing communication latency while maintaining system reliability.
2Measurement precision
If frequent communication with cloud servers is implemented, then data accuracy is maintained, but operational costs and bandwidth consumption increase
Solution Approach 1:
The patent implements partial communication action by having edge nodes transmit only processed results, filtered data, or anomaly detections to cloud servers rather than continuously transmitting all raw data. This maintains data accuracy for critical information while significantly reducing bandwidth consumption and operational costs.
Solution Approach 2:
The patent enables edge nodes to perform self-service data processing and filtering operations locally. Edge nodes autonomously determine which data requires cloud server communication based on predefined criteria, reducing unnecessary cloud interactions and associated costs while maintaining data accuracy through intelligent local decision-making.
3Speed
If data is processed and stored at edge nodes, then real-time data access is improved, but device complexity increases
Solution Approach 1:
The patent designs edge nodes with multi-functional capabilities that can handle diverse data processing tasks using standardized protocols and frameworks. By making edge nodes universal and multi-functional, the system achieves fast real-time data processing without proportionally increasing complexity, as the same node architecture can serve multiple purposes.
Solution Approach 2:
The patent employs configurable parameters and adjustable processing thresholds that allow edge nodes to adapt their complexity based on specific application requirements. This enables the system to optimize between processing speed and device complexity by adjusting operational parameters rather than fundamentally changing the node architecture.
Data Source
AI summary
A method is provided for performing dynamic inferencing at a node configured to communicate with other nodes of an IoT hierarchy. In the method, a schema for an asset object associated with one or more of at least one physical process and at least one physical device is received at the node. The schema is formatted according to an inferencing engine model format. An artificial intelligence model capable of being executed in an inferencing engine is received at the node. Data indicative of one or more of a current state of at least one physical process and a current state of at least one physical device is received at the node. The received data according to the schema and an inferencing engine are processed at the node. The inferencing engine generates a new predictive attribute based on the set of attributes, and the processing normalizes the received data according to the schema to generate normalized data, the normalized data includes the predictive attribute from the inferencing engine. A notification is then provided based on one or more rules for the physical process and physical device.