Environmental Sensor Localization for Attached Wireless Objects
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Solution Overview
Problem
Existing wireless communication devices struggle with inaccurate localization of attached objects due to issues such as users not hearing the emitted ringtone or imprecise positioning, especially in certain environments.
Innovation Solution
The method employs environmental sensors to measure various properties like temperature, pressure, humidity, gas, and light, using a processor to determine location categories through a trained machine learning model, and optionally transmit this information to an external device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional localization methods (ringtone playback, trilateration) are used, then the device can provide basic location functionality, but the localization accuracy is insufficient and users cannot reliably determine the object's position
Solution Approach 1:
The patent changes the parameters used for localization from spatial coordinates (trilateration) or acoustic signals (ringtone) to environmental parameters measured by sensors (temperature, humidity, pressure, light, gas composition). These environmental parameters serve as fingerprints to identify location categories, fundamentally changing how location is determined and achieving both higher precision and reliability.
Solution Approach 2:
The patent replaces mechanical/acoustic localization methods (ringtone playback requiring user auditory confirmation, trilateration requiring multiple external devices) with an environmental sensing system that passively measures and categorizes location based on physical and chemical properties of the environment, eliminating the need for user interaction and external infrastructure.
2Measurement precision
If environmental sensors and machine learning models are added to determine location categories, then localization accuracy is significantly improved, but the device complexity increases
Solution Approach 1:
The patent segments the complex task of precise localization into a two-stage process: first, multiple environmental sensors independently measure different physical and chemical parameters; second, a machine learning model processes these segmented measurements to classify the location category. This segmentation allows each component to remain relatively simple while achieving high overall accuracy.
Solution Approach 2:
The patent makes the environmental sensors serve multiple functions: they not only provide data for location categorization but also enable detection of environmental conditions, potential hazards, and context information. This multi-functionality justifies the added complexity by extracting maximum value from each sensor component.
3Loss of energy
If the device uses environmental sensors to determine location categories, then additional positioning modules are unnecessary saving energy and costs, but the power consumption of environmental sensors must be managed
Solution Approach 1:
The patent implements periodic sampling of environmental parameters rather than continuous monitoring. The processor periodically activates environmental sensors to collect data for location categorization, then enters low-power states between measurements. This periodic operation significantly reduces average power consumption while maintaining effective location tracking capability.
Solution Approach 2:
The patent enables the environmental sensors to serve the dual purpose of both location determination and environmental monitoring. The same sensors that detect temperature, humidity, pressure, light, and gas for location categorization also provide valuable environmental context information, eliminating the need for separate dedicated sensors and reducing overall system power requirements.
Data Source
AI summary
The wireless communication device includes a plurality of environmental sensors configured to measure various environmental properties of an environment surrounding the object. The method comprises the steps, performed by a processor, of: receiving measured environmental data from the plurality of environmental sensors measuring the various environmental properties; and determining location information of the object using the measured environmental data.