Distributed Occupancy Detection via Multi-Node Probability
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Solution Overview
Problem
Existing occupancy detection systems rely on single sensors, leading to a trade-off between accuracy and sensitivity, resulting in either false-positive or missed detections due to varying threshold settings for infrared radiation detection.
Innovation Solution
A distributed occupancy detection system with multiple node devices that communicate with each other to determine the probability of an object's presence, using occupancy sensors to sense presence characteristics like infrared radiation, motion, or changes in light, and adjust probabilities based on multi-device confirmations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the threshold level of detected infrared radiation is increased to ensure fewer false-positive detections, then detection accuracy is improved, but sensitivity deteriorates resulting in missed detections
Solution Approach 1:
The patent divides the detection system into multiple independent sensor nodes distributed throughout the space. Each node operates independently with its own threshold, and the system aggregates results across nodes. This segmentation allows the system to maintain high thresholds at individual nodes for accuracy while achieving high sensitivity at the system level through cumulative detection evidence.
Solution Approach 2:
The patent combines the detection results from multiple sensor nodes to make the final occupancy determination. By merging the probabilistic detection signals from multiple nodes, the system achieves both high accuracy (through individual node thresholding) and high sensitivity (through system-level aggregation of detection evidence).
2Reliability
If the threshold level of detected infrared radiation is lowered to reduce missed detections, then sensitivity is improved, but false-positive detections increase
Solution Approach 1:
The system segments the detection function across multiple nodes, allowing each node to use lower thresholds for sensitivity while the distributed architecture provides redundancy. False positives at individual nodes are diluted by the presence of many nodes, and only consistent detections across multiple nodes trigger system-wide occupancy alerts.
Solution Approach 2:
The system uses probabilistic feedback mechanisms where each node continuously reports detection probabilities to the system controller. This feedback loop allows the system to dynamically adjust and verify detections across nodes, filtering out false positives through cross-validation while maintaining high sensitivity through cumulative probability assessment.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the accuracy of occupancy detection, reduces false positives and missed detections, and allows for more intelligent lighting control by confirming object presence through networked sensor collaboration.
Implementation Method 1
a passive infrared sensor can detect when a person has walked into a field of view of the sensor
Data Source
AI summary
A distributed occupancy detection system includes plural networked node devices configured to be spatially distributed throughout a structure. Each node device includes an occupancy sensor that senses a presence characteristic indicative of an object being in a monitored area of the structure that is associated with the occupancy sensor. Each node device also includes one or more processors that determine a probability that the object is or was located in the structure based on the presence characteristic sensed by the occupancy sensor of a first node device and based on the presence characteristic sensed by the occupancy sensor of one or more neighboring node devices. The one or more processors determine whether the object is in the structure based on the probability.