Premise Network Supervision Failure Prediction
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Solution Overview
Problem
Home automation and security networks face challenges in predicting failures in hub and sensor devices due to unique environmental factors, leading to unforeseen downtimes and inefficiencies, as existing systems lack a universal method to accurately anticipate failures in diverse settings.
Innovation Solution
A method involving a device that receives hub and sensor data, along with environmental data, to apply a prediction model that determines potential supervision failures between hub and sensor devices, adjusting the model based on environmental factors to improve prediction accuracy and reduce downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a universal failure prediction system is implemented, then prediction coverage is improved, but accuracy in unique environments deteriorates
Solution Approach 1:
The system transitions from a universal prediction model to environment-specific models by collecting training data from multiple installations with diverse environmental factors (building materials, layout, device density). Each environment develops optimized prediction parameters tailored to its local characteristics, thereby maintaining high accuracy while achieving broad coverage across different settings.
2Measurement precision
If environmental factors are incorporated into the prediction model, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
Environmental factors such as building materials, layout, and device density are collected and stored during the installation phase before failure prediction begins. This preliminary data collection creates a ready-to-use training dataset that enables the machine learning model to accurately predict failures without adding complexity during the actual prediction operation.
Solution Approach 2:
The system automatically collects environmental data, trains the machine learning model, and optimizes prediction parameters without requiring manual configuration or intervention. The self-training process adapts the model to each specific environment autonomously, reducing the operational complexity despite the sophisticated prediction capabilities.
3Reliability
If machine learning models are trained on environmental data, then prediction reliability is improved, but data processing time increases
Solution Approach 1:
The machine learning model is trained during the installation phase using collected environmental data before actual failure prediction begins. This preliminary training ensures the model is ready to provide reliable predictions immediately, avoiding time-consuming processing during critical monitoring periods while maintaining high reliability through environment-specific optimization.
Data Source
AI summary
Disclosed herein are techniques for predicting supervision failure in a premise wireless network. A device receives, from a hub device of a premise wireless network, hub wireless communication data. The device receives, from at least one sensor device of the premise wireless network and in wireless communication via the premise wireless network with the hub device, sensor wireless communication data. The device also receives environmental data. The device applies a prediction model to the hub wireless communication data, the sensor wireless communication data, and the environmental data to determine a prediction of supervision failure between the hub device and the at least one sensor device.