IoT Hub Segmented Data Model Deployment
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Solution Overview
Problem
IoT devices face challenges in efficiently processing and transmitting large volumes of data across networks, leading to performance issues and increased power consumption, particularly in local networks with multiple devices connected to a hub device.
Innovation Solution
Implementing data filters at data source devices to selectively transmit only necessary data to a hub device, while deploying data processing model portions securely, allowing for efficient network performance and reduced power consumption by edge devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data from multiple IoT devices is transmitted across the network to a hub device for processing, then data analysis capability is improved, but network bandwidth consumption increases and network performance deteriorates
Solution Approach 1:
The patent segments the data processing model into multiple portions and distributes them across different edge devices rather than centralizing all processing at the hub device. This segmentation allows data to be processed locally at the edge, reducing the volume of data transmitted over the network while maintaining comprehensive data analysis capability through coordinated processing across distributed model portions.
2Productivity
If a data processing model is implemented at the hub device to analyze data from multiple IoT devices, then data analysis capability is improved, but the model may include valuable proprietary information that needs protection
Solution Approach 1:
The data processing model is divided into multiple portions and distributed across different edge devices. This segmentation ensures that no single device contains the complete proprietary model, thereby protecting intellectual property while maintaining distributed processing capability for comprehensive data analysis.
Solution Approach 2:
The patent introduces an intermediary mechanism where model portions are distributed through a controlled deployment process managed by the hub device or a central system. This intermediary approach enables secure distribution of model segments to edge devices while maintaining oversight and protection of proprietary information throughout the deployment and execution process.
3Loss of information
If edge devices transmit all collected data to the hub device, then data completeness is improved, but power consumption of edge devices increases
Solution Approach 1:
The patent applies preliminary action by performing data processing and filtering at the edge devices before transmission to the hub. Edge devices use their local model portions to pre-process data, filtering out redundant information and transmitting only essential data to the hub. This preliminary processing reduces the volume of transmitted data and conserves edge device power while maintaining data completeness through coordinated processing across the distributed system.
Data Source
AI summary
A hub device of a network receives a data model that includes a secure portion that is encrypted and one or more unsecure portions. The hub device deploys the one or more unsecure portions of the data model to respective edge devices of the network. The hub device decrypts the secure portion of the data model. The edge devices collect data (e.g., from sensors) and process the data using the unsecure portions of the data model. The edge devices send the processed data to the hub device. The hub device performs operations on the received processed data using the decrypted secure portion of the data model in a secure execution environment (e.g., a TPM or other secure module). The secure portion of the data model generates a result, which is then transmitted to an endpoint.