Wireless Sensor Auto Routing Using Historical ML Patterns
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing sensor auto-routing systems rely solely on present system data, leading to unwanted abrupt re-routing, prolonged understanding of premises conditions, and missed critical alarms due to inefficient routing decisions.
Innovation Solution
Implementing a machine learning algorithm that incorporates historical data and current parameters to analyze sensor and RF portal patterns, creating a weightage table for optimal routing based on signal strength, sensor association, and response type, thereby avoiding abrupt re-routing and ensuring critical sensors are prioritized.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If auto routing is based only on present system data, then routing decisions can be made quickly, but unwanted abrupt re-routing occurs and critical alarms may be missed
Solution Approach 1:
The system performs preliminary analysis of historical data to establish baseline routing patterns and sensor-criticality classifications before actual routing decisions are needed. This preparation enables faster real-time decisions while maintaining reliability through pre-established patterns.
Solution Approach 2:
The system incorporates feedback loops that continuously monitor routing decisions and outcomes, comparing actual performance against historical patterns. This feedback mechanism prevents abrupt re-routing by learning from past decisions and adjusting future routing stability accordingly.
2Reliability
If historical data analysis is incorporated into routing decisions, then routing stability improves, but processing time and system complexity increase
Solution Approach 1:
The system segments historical data analysis into distinct modules: data collection, pattern recognition, criticality classification, and decision integration. Each segment handles specific aspects of historical data processing, reducing overall system complexity while maintaining comprehensive analysis.
Solution Approach 2:
Historical data is pre-processed and stored in structured formats during system operation, with key patterns and metrics extracted in advance. This preliminary action reduces the computational burden during real-time routing decisions, maintaining simplicity while improving reliability.
3Adaptability or versatility
If machine learning algorithms are deployed for sensor routing, then routing intelligence improves, but computational requirements and energy consumption increase
Solution Approach 1:
The machine learning algorithm processes only the most relevant historical data and current sensor parameters needed for routing decisions, rather than analyzing all available data. This partial action approach maintains high routing intelligence while reducing computational energy consumption to acceptable levels.
Data Source
AI summary
A system relating to using machine learning and associated technologies for auto routing of wireless sensors to one of the multiple radio frequency (RF) portals/gateways which in turn may be interfaced/wired to control panel(s). A deployed machine learning algorithm may incorporate current parameters and historical data, such as signal strength, sensor association, critical sensor response types and disruption in the network, to determine sensor routing for one of the appropriate RF portal/gateway which in turn is interfaced to control panel(s).