Hybrid IoT Alert Evaluation Across Edge and Fog Layers
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Solution Overview
Problem
Current IoT architectures face high and unpredictable latency, as well as increased network bandwidth demand, when evaluating data and triggering alerts at a cloud server layer, which limits the effectiveness and scalability of IoT systems.
Innovation Solution
A hybrid IoT evaluation framework that distributes data evaluation, alert analysis, and action execution across local fog devices and edge devices, reducing the burden on cloud servers and minimizing latency by using a four-tier architecture that includes device, edge, fog, and cloud layers, allowing for real-time processing and reduced data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If data evaluation and alert analysis are performed at the cloud server layer, then centralized processing capability is improved, but latency and network bandwidth demand increase
Solution Approach 1:
The patent segments the centralized cloud processing function into distributed edge computing nodes deployed at multiple locations. Each edge node performs local data evaluation and alert analysis, eliminating the need for all data to travel to a central cloud server. This segmentation reduces latency by processing data closer to its source while maintaining the benefits of distributed processing capability.
Solution Approach 2:
The patent introduces a spatial dimension to the processing architecture by deploying edge computing nodes across multiple geographic locations rather than concentrating all processing in a single cloud data center. This dimensional change allows data to be processed at the nearest edge node, reducing the physical distance data must travel and thereby reducing latency while maintaining processing power through the collective capability of multiple nodes.
2Measurement precision
If all data is transmitted to the cloud server for evaluation, then comprehensive analysis capability is improved, but network bandwidth demand increases
Solution Approach 1:
The patent extracts the data evaluation and alert analysis functions from the central cloud server and places them at distributed edge computing nodes. This extraction allows each edge node to perform comprehensive local analysis of data received from nearby sensors, eliminating the need to transmit all raw data to the cloud. Only processed results or aggregated data need to be transmitted, significantly reducing network bandwidth demand while maintaining comprehensive analysis capability at the edge.
Solution Approach 2:
The patent implements partial action by having edge computing nodes perform only the necessary data evaluation and alert analysis locally, rather than transmitting all data to the cloud for complete analysis. Each edge node processes data to the extent needed for local decision-making, sending only essential information to the cloud, thereby reducing bandwidth usage while maintaining sufficient analysis capability for local operations.
3Loss of time
If edge devices perform data evaluation locally, then latency is reduced, but computing resource requirements at edge devices increase
Solution Approach 1:
The patent merges multiple edge computing nodes into a distributed network, where each node performs local data evaluation to reduce latency. By combining the processing power of multiple edge nodes rather than requiring a single powerful device, the system achieves low-latency local processing without overburdening individual edge devices. The distributed architecture allows computing resources to be shared across the network, reducing the burden on each individual node.
Data Source
AI summary
Various examples are disclosed for hybrid alert and action solution in Internet-of-Things (IoT) networks. A multi-edge alert definition specifies a plurality of IoT devices that communicate through edge devices. The multi-edge alert definition is decomposed decompose into individual edge alert sub-definitions. Each of the individual edge alert sub-definitions is evaluated using data from individual ones of the edge devices. An alert is triggered based on one of the individual edge alert sub-definitions being met.