Coded Address Wireless Sensing in Network of Networks
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Solution Overview
Problem
Existing wireless sensing technologies face challenges in efficiently utilizing multiple IoT devices for wireless sensing, particularly in environments with rich scattering conditions, such as indoors or metropolitan areas, where understanding human activities' impact on wireless signal propagation is crucial.
Innovation Solution
The method involves using coded addresses to identify originating devices in a network of networks. Type1 devices, which are heterogeneous wireless devices, are associated with a Type2 device. The Type2 device transmits time series of wireless trigger signals to the Type1 devices, triggering wireless sounding. Each Type1 device responds with a wireless sounding signal embedding the coded address of the Type2 device, allowing the Type2 device to identify the originating device and assemble channel information for wireless sensing tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple heterogeneous wireless devices are used for wireless sensing in rich scattering environments, then sensing capability and measurement precision are improved, but device complexity and difficulty of detecting and measuring increase
Solution Approach 1:
The patent segments wireless devices into two functional categories: Type1 devices (sensing devices that transmit sounding signals) and Type2 devices (coordination devices that manage sensing operations). This segmentation allows each device type to have specialized functionality, reducing overall system complexity while enabling precise multi-device sensing coordination in rich scattering environments
Solution Approach 2:
The patent introduces coded addresses as an intermediary mechanism to identify and associate Type1 devices with Type2 devices. This intermediary coding system simplifies device identification and association processes, making it easier to manage multiple heterogeneous devices while maintaining precise measurement capabilities through structured device relationships
2Productivity
If multiple heterogeneous wireless devices are used for wireless sensing, then sensing capability and productivity are improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The patent implements preliminary device association and coded address assignment before sensing operations begin. Type2 devices pre-configure coded addresses for Type1 devices, establishing identification mappings in advance. This preliminary setup enables efficient multi-device coordination during sensing operations, improving productivity while reducing real-time measurement difficulty through pre-established device relationships
Solution Approach 2:
The patent changes the identification parameter from traditional MAC addresses to coded addresses that encode device type and association information. This parameter transformation enables easier detection and measurement by providing structured identification data that simplifies device recognition and association during sensing operations, thereby improving productivity
3Ease of operation
If coded addresses are used to identify originating devices, then ease of operation and device identification are improved, but loss of information may occur if addressing is not properly configured
Solution Approach 1:
The patent designs coded addresses to serve multiple functions simultaneously: identifying device type (Type1 or Type2), establishing device associations, and enabling directional communication. This multi-functionality reduces the need for separate identification mechanisms, improving ease of operation while minimizing information loss by consolidating multiple identification requirements into a single coded address structure
Data Source
AI summary
Wireless sensing using classifier probing and refinement is described. In one example, a described method comprises: computing output analytics by a classifier based on input data constructed based on raw measurement data; mapping each output analytics to a respective mapped outcome; identifying at least one reference input data each associated with a reference output analytics and a reference mapped outcome; each reference input data being one of the input data for the classifier for which a respective reference outcome is available and is different from the reference mapped outcome; and for each reference input data for the classifier: computing a respective plurality of perturbed output analytics by the classifier, generating selected perturbed input data based on the plurality of perturbed output analytics; and re-training the classifier based on the selected perturbed input data and the associated reference outcome.