Static Leaf Node Identification for Motion Detection Systems
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Solution Overview
Problem
Motion detection systems using wireless signals face challenges in accurately determining motion and status changes due to the integration of both static and mobile leaf nodes, which can lead to poor channel information quality and system degradation.
Innovation Solution
A closed-loop continuous link health measurement and classification system is implemented to differentiate between static and mobile leaf nodes by analyzing network status reports and link quality metrics, selecting only static leaf nodes for channel information collection to improve motion detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If both static and mobile leaf nodes are integrated in the motion detection system, then the system can serve more devices and increase coverage, but the channel information quality deteriorates and system performance degrades
Solution Approach 1:
The patent segments leaf nodes into two distinct categories: static leaf nodes and mobile leaf nodes. This segmentation is achieved through a classification mechanism that evaluates link quality metrics and presence information to determine whether each leaf node is static or mobile. By separating these node types, the system can apply different handling strategies - static nodes are used for channel information collection while mobile nodes are excluded, thereby resolving the contradiction between system coverage and channel information quality.
2Quantity of substance
If channel information is collected from all leaf nodes, then the system can maximize data availability, but measurement precision decreases due to inclusion of mobile nodes
Solution Approach 1:
The patent applies local quality by assigning different roles and quality levels to different leaf nodes based on their characteristics. Static leaf nodes are identified as having high quality for channel information collection, while mobile leaf nodes are identified as having low quality. The system then selectively collects channel information only from high-quality static nodes, ensuring measurement precision is maintained while still gathering sufficient data from the appropriate subset of nodes.
3Reliability
If the system processes information from all leaf nodes, then comprehensive monitoring is achieved, but device complexity and processing overhead increase
Solution Approach 1:
The patent implements preliminary action by performing leaf node classification before channel information collection. The system evaluates link quality metrics and presence information during calibration periods to pre-categorize leaf nodes as static or mobile. This preliminary classification creates a filtered set of static leaf nodes that will be used for subsequent motion detection, eliminating the need to process information from all leaf nodes and thereby reducing processing overhead while maintaining monitoring completeness.
Data Source
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AI summary
In a general aspect, a motion detection system manages leaf nodes used for sounding by one or more access points. For example, an access point obtains presence information for a plurality of AP-leaf node links for a plurality of calibration periods. Presence activity is determined for each AP-leaf node link in each calibration period based on its respective presence information. Static leaf nodes are identified based on the presence activity for the plurality of AP-leaf node links in a calibration window, the calibration window comprising a number of the plurality of calibration periods. The motion detection system is updated to use at least one of the identified static leaf node as a sounding node for motion detection.