Set-Top Box Edge AI for Low-Latency IoT Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The reliance on cloud-based solutions for controlling IoT devices introduces vulnerabilities such as network outages, privacy concerns, data security risks, and latency, which detract from user experience and increase network bandwidth demand.
Innovation Solution
Implementing edge AI on a computing device, such as a set-top box, to process user commands locally, minimizing latency and reducing internet connectivity, while maintaining user privacy and ensuring uninterrupted functionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If cloud-based solutions are used for controlling IoT devices, then centralized management and processing capabilities are improved, but network latency and dependency on internet connectivity increase
Solution Approach 1:
The patent implements a hybrid architecture where the STB performs local processing of user inputs and device control decisions using on-device machine learning models, while cloud servers provide supplementary processing power and model training. This local quality approach reduces network latency by handling time-sensitive operations locally while maintaining centralized management capabilities through cloud connectivity.
Solution Approach 2:
The system segments processing tasks between the STB and cloud servers. The STB handles real-time inference and local device control, while cloud servers handle model training, updates, and non-time-critical processing. This segmentation allows the system to achieve both low latency for control operations and centralized management for overall system coordination.
2Power
If cloud-based processing is used, then computational power and model training capabilities are improved, but data security risks and privacy concerns increase
Solution Approach 1:
The system performs preliminary processing of user inputs and device data locally on the STB before transmitting any information to cloud servers. Machine learning models are trained and updated in the cloud, but inference and control decisions are made locally. This preliminary action ensures that sensitive data remains on-device, reducing data security risks while still utilizing cloud computational power for model improvement.
Solution Approach 2:
The STB acts as an intermediary between user inputs and cloud servers. It processes and anonymizes data locally before sending only necessary information to the cloud, and receives model updates without exposing raw user data. This intermediary role protects user privacy and reduces data security risks while maintaining access to cloud-based computational resources.
3Speed
If edge AI is implemented on the STB, then processing speed and responsiveness are improved, but device complexity and resource requirements increase
Solution Approach 1:
The STB implements partial edge AI functionality by running optimized machine learning inference engines for specific IoT device control tasks rather than full-scale AI processing. The device handles only the inference portion of the AI workflow, while model training and updates occur in the cloud. This partial action approach achieves improved processing speed for control operations without requiring the STB to have full AI processing capabilities, thus managing device complexity.
Data Source
AI summary
The disclosed technology provides a system and methods for receiving, by a computing device, data indicating an action to be performed. The computing device may provide at least a portion of the data to the machine learning model configured to determine an IoT device associated with the action to be performed using the portion of the data. The computing device may receive from the machine learning model, an output from the machine learning model, the output indicating the IoT device associated with the action to be performed. The computing device may determine and/or generate a control signal configured to cause the IoT device to perform the action. The computing device may transmit the control signal to the IoT device.