Edge Attention Encoding for Efficient M2M Sensor Transmission
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing M2M communication systems face inefficiencies in processing and transmitting raw data from multiple sensors, leading to suboptimal utilization of generative pretrained AI models for tasks requiring limited data.
Innovation Solution
Implement encoder networks at the device to determine embeddings and attentions from sensor data, transmitting these attentions to the cloud for processing, which then provides instructions for actions to be executed by the device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If raw sensor data is transmitted to the cloud for processing, then the generative pretrained model can process the data, but the data transmission volume increases and processing efficiency decreases
Solution Approach 1:
The patent extracts only the most critical information from raw sensor data by generating embeddings and attentions at the edge device before transmission. This selective extraction ensures that only essential data features are sent to the cloud, reducing transmission volume while maintaining processing effectiveness for the generative pretrained model.
Solution Approach 2:
The patent performs preliminary processing of sensor data at the edge device by generating embeddings and attentions before transmission to the cloud. This preliminary action transforms raw data into compressed, meaningful representations that reduce the amount of data needing transmission while preserving the information needed for effective model processing.
2Productivity
If encoder networks are implemented at the device to process data locally, then data processing efficiency improves, but device complexity increases
Solution Approach 1:
The patent segments the data processing function into two parts: a lightweight encoder network implemented at the edge device for local embedding and attention generation, and a generative pretrained model at the cloud for high-level processing. This segmentation allows the device to handle only the computationally intensive embedding tasks while offloading complex generative processing to the cloud.
Solution Approach 2:
The patent introduces an intermediary layer of embeddings and attentions between the sensor data and the generative pretrained model. This intermediary representation serves as a compressed summary that bridges the edge device and cloud, enabling efficient communication while distributing computational complexity appropriately across the system.
Data Source
AI summary
Aspects of the present application relate to a UE transmitting, to a cloud, one or more attentions rather than transmitting raw sensor information. To allow the UE to transmit the attentions, the UE implements an encoding network. The UE may then employ the encoding network to determine embeddings encoded by the raw sensor information, both spatial and temporal, collected at a plurality of sensors. On the basis of the embeddings, the UE may then determine the attentions, e.g., self-attention matrices and/or cross-attention matrices. The UE may then transmit, to the cloud, the attentions. At the cloud, the attentions may be processed to obtain actions. The cloud may then transmit, to the UE, instructions for carrying out the actions. Upon receipt of the instructions, the UE may carry out the actions.