Hybrid Quantized Decision Model for IoT Edge Latency
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Solution Overview
Problem
Evaluating data at the cloud server layer in IoT solutions leads to high and unpredictable latency, increased network bandwidth demand, and issues with low latency and intermittent network connections, making it unsuitable for time-sensitive or bandwidth-constrained applications.
Innovation Solution
A hybrid quantized decision model framework that offloads intelligent decisions from the cloud layer to the edge layer, optimizing decision models through quantization to reduce memory and computational requirements, allowing for faster predictions and lower power consumption, while maintaining accuracy, and enabling edge layer predictions with cloud layer verification when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data evaluation is performed at the cloud server layer, then high confidence predictions can be achieved, but latency increases and network bandwidth consumption increases
Solution Approach 1:
The patent segments the decision-making process into two parts: a quantized decision model at the edge device for rapid initial evaluation, and a full cloud-based model for verification when needed. This segmentation allows most decisions to be made locally with low latency, while cloud verification provides high confidence for uncertain cases.
Solution Approach 2:
The quantized decision model acts as an intermediary between edge device sensing and cloud-based full model evaluation. It pre-processes and filters data locally, only transmitting uncertain cases to the cloud, thereby reducing network bandwidth consumption and latency while maintaining prediction reliability.
2Reliability
If data evaluation is performed at the cloud server layer, then accurate predictions can be made, but network bandwidth demand increases
Solution Approach 1:
The patent applies partial action by using the quantized decision model to handle the majority of evaluation cases locally at the edge device. Only when the quantized model's confidence is insufficient does the system invoke the full cloud-based model, thereby reducing network bandwidth consumption while maintaining accuracy for critical cases.
3Loss of time
If quantized decision models are deployed at the edge layer, then latency is reduced and bandwidth consumption decreases, but computational precision is reduced
Solution Approach 1:
The system implements feedback by using the quantized decision model's confidence output to determine whether cloud verification is needed. When the quantized model's confidence is below a threshold, the system feeds the data back to the cloud for full model evaluation, ensuring accuracy is maintained for uncertain predictions while keeping latency low for confident decisions.
4Reliability
If cloud-based evaluation is used, then high confidence predictions are achieved, but unpredictable latency occurs
Solution Approach 1:
The quantized decision model performs preliminary evaluation and filtering at the edge device before data is transmitted to the cloud. This preliminary action reduces the volume of data requiring cloud processing and establishes a baseline confidence level, making the overall system more predictable and manageable despite the complexity of coordinating both edge and cloud components.
Data Source
AI summary
Various examples are disclosed for hybrid alert and action solution in IoT (IoT) networks. A cloud layer decision model that generates cloud layer predictions is identified using device layer data. A quantized decision model is generated as a quantized version of the cloud layer decision model. A logical group that includes an edge device that collects a portion of the device layer data is identified. The quantized decision model is transmitted to the logical group.