Wireless Rate Inference for Environment Control Networks

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Solution Overview

Problem

Current environment control systems face inefficiencies in adapting wireless data transfer rates due to hostile conditions and spectrum competition, requiring a more sophisticated method to determine optimal transfer rates.

Innovation Solution

An inference server and environment control device utilizing a neural network engine to determine and configure optimal wireless data transfer rates based on real-time parameters, such as signal strength and error rates, through a predictive model generated by a training engine.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If a simple algorithm is used to control data transfer rate by trying the best possible rate and lowering it when unsustainable, then the implementation is straightforward, but the adaptation is too slow and inefficient for hostile conditions and spectrum competition

Engineering Contradiction:

Improvesimplicity of implementationVSAvoiddata transfer rate adaptation speed

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The patent replaces the mechanical trial-and-error algorithm with a neural network-based predictive model. The neural network processes multiple parameters (signal strength, error rates, interference levels) simultaneously to predict optimal data transfer rates, eliminating the need for iterative testing and achieving faster adaptation to changing wireless conditions.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent changes from a single-parameter approach (trying one data transfer rate at a time) to a multi-parameter approach. The neural network simultaneously analyzes signal strength, error rates, interference levels, and other wireless conditions to determine optimal data transfer rates, enabling more efficient and accurate adaptation to hostile environments.

Inventive Principle:

Principle #35Parameter changes

2Adaptability or versatility

If a set of rules is defined to select optimal data transfer rate based on current conditions, then the system can adapt to conditions, but the rules are either too simple to properly model conditions or too complicated to be designed by a human being

Engineering Contradiction:

Improveadaptation to wireless conditionsVSAvoidcomplexity of control rules

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent introduces a neural network as an intermediary between wireless conditions and data transfer rate selection. The neural network learns complex relationships between multiple parameters (signal strength, error rates, interference) and optimal data transfer rates during training, then applies this learned knowledge during operation without requiring explicit human-designed rules.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The neural network performs self-learning during a training phase by analyzing historical wireless condition data and corresponding successful data transfer rates. Once trained, the system serves itself by automatically predicting optimal data transfer rates based on current conditions without requiring ongoing human intervention or rule adjustments.

Inventive Principle:

Principle #25Self-service

3Reliability

If the driver lowers the data transfer rate when the best rate cannot be sustained, then data transmission reliability is maintained, but the data transfer rate is not optimized and remains too long to adapt

Engineering Contradiction:

Improvedata transmission reliabilityVSAvoidtime for rate adaptation

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The neural network performs preliminary analysis of multiple wireless parameters (signal strength, error rates, interference levels) before selecting a data transfer rate. This predictive approach allows the system to choose rates that are both reliable and optimized, avoiding the need for subsequent reductions and minimizing adaptation time while maintaining transmission reliability.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11277347B2Inference server and environment control device for inferring an optimal wireless data transfer rate

Publication Date: 2022.03.15 DISTECH CONTROLS

AI summary

Inference server and computing device for inferring an optimal wireless data transfer rate. The computing device determines parameters of a data transfer through a wireless communication interface of the computing device, and transmits the parameters of the data transfer to the inference server. The inference server receives the parameters of the data transfer, executes a neural network inference engine using a predictive model (generated by a neural network training engine) for inferring an optimal data transfer rate based on the parameters of the data transfer, and transmits the optimal data transfer rate to the computing device. The computing device receives the optimal data transfer rate, and configures its wireless communication interface to operate at the optimal data transfer rate. For example, the computing device consists of an environment control device (e.g. an environment controller, a sensor, a controlled appliance, and a relay).