UWB Sensor Event Validation for IoT Hazard Detection
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Solution Overview
Problem
Existing IoT systems for hazard detection in smart environments often provide false warnings, fail to recognize hidden objects, and do not effectively trigger actions in other devices to mitigate health hazards, especially in blind spots and areas with multiple signal interference.
Innovation Solution
A method using UWB sensors to detect events, validate them through multiple IoT devices, determine criticality, and trigger appropriate actions or recommendations to prevent health hazards, including the use of UWB sensors integrated into IoT devices or as standalone sensors to scan environments and classify events as low, medium, or high criticality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single device is used for detecting anomalies or dangerous situations, then the device complexity is reduced, but the reliability of detection decreases due to false warnings
Solution Approach 1:
The patent combines multiple IoT devices (UWB sensors, cameras, AI speakers, hazard detectors) into a unified detection system that cross-validates events. When an event is detected by one device, other devices validate it to confirm真实性, reducing false warnings while maintaining manageable complexity through coordinated operation.
Solution Approach 2:
The system implements feedback mechanisms where detected events are validated by multiple devices before triggering alerts. The validation process provides feedback to confirm or refute initial detections, ensuring high reliability by requiring consensus among multiple sensors before generating false alarms.
2Reliability
If multiple IoT devices are used for event validation, then the reliability of event detection is improved, but the device complexity increases
Solution Approach 1:
The validation system is segmented into specialized modules: UWB sensors for proximity detection, cameras for visual verification, AI speakers for audio analysis, and hazard detectors for environmental monitoring. Each segment performs a specific validation function, reducing overall system complexity through functional specialization while maintaining high reliability.
Solution Approach 2:
Multiple IoT devices serve dual purposes: their primary functions (e.g., cameras for monitoring, microphones for audio) and event validation functions. This multi-functionality reduces the need for dedicated validation hardware, managing complexity while ensuring reliable cross-device event verification.
3Reliability
If existing detection systems are used, then the ease of operation is maintained, but the ability to detect hidden objects and movements in blind spots is lost
Solution Approach 1:
The system transitions from single-point detection to multi-dimensional detection by deploying sensors throughout the environment. UWB sensors detect presence in blind spots, cameras provide visual coverage of hidden areas, and the networked architecture enables detection across multiple spatial dimensions, overcoming limitations of individual device blind spots.
4Productivity
If real-time distance calculation is performed, then the productivity of hazard detection is improved, but the computational requirements increase making it difficult to detect in real-time
Solution Approach 1:
UWB sensors serve as intermediaries for distance calculation, providing precise proximity data without requiring complex image processing or AI computation. The sensors directly measure distance through time-of-flight measurements, enabling real-time hazard detection with minimal computational power while maintaining high detection speed.
Data Source
AI summary
The embodiment, herein disclose a method of handling critical events in IoT environment comprising a plurality of IoT devices and an at least one UWB sensor. The method comprises detecting an occurrence of an event using the UWB sensor; validating the event using at least one IoT device from the plurality of IoT devices located in vicinity of the event; determining a critical level of the event based on at least one of a presence of a user or a device data of the at least one IoT device located in a vicinity of the event; generating an action recommendation for the at least one of the user or the at least one IoT device based on the criticality level of the event.