Multi-Tier Sensor Activation for Low-Power Accurate Detection
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Solution Overview
Problem
Sensor devices that operate continually to detect conditions consume excessive energy and may generate false positives due to the use of motion sensors, leading to inefficient power usage and inaccurate detection.
Innovation Solution
A sensor system comprising a primary high-power device paired with secondary low-power devices that continuously collect data using machine learning models to trigger the primary device only when conditions are met, reducing power consumption and improving detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor devices operate continually to detect conditions, then detection capability is improved, but energy consumption increases excessively
Solution Approach 1:
The sensor system is divided into multiple sensor devices with different power consumption characteristics. Low-power sensor devices operate continually to detect conditions, while high-power sensor devices operate only when triggered. This segmentation allows the system to maintain detection capability while reducing overall energy consumption by having only the necessary sensors active at any given time.
Solution Approach 2:
The system dynamically adjusts the operational state of sensor devices based on detected conditions. When low-power sensor devices detect specific conditions, they trigger high-power sensor devices to activate. This dynamic state adjustment ensures that sensor devices operate at appropriate power levels for their current function, optimizing the balance between detection capability and energy consumption.
2Use of energy by stationary object
If motion sensors are used to trigger sensor devices, then power saving is achieved, but false positives increase due to inaccurate detection
Solution Approach 1:
Low-power sensor devices serve as intermediaries between the environment and the high-power sensor devices. These intermediary devices continuously monitor conditions at low power consumption and only trigger high-power devices when genuine conditions are detected, filtering out false positives before they can activate power-intensive sensors.
Solution Approach 2:
The system performs preliminary detection using low-power sensor devices before activating high-power sensor devices. This preliminary action involves low-power devices continuously monitoring for specific conditions and making initial assessments, thereby preventing false triggers of high-power devices and reducing overall power consumption while maintaining detection accuracy.
3Measurement precision
If high-power sensor devices operate continually, then detection accuracy is improved, but power consumption increases
Solution Approach 1:
High-power sensor devices operate periodically rather than continually, being activated only when triggered by low-power sensor devices. This periodic operation maintains detection accuracy for critical events while significantly reducing overall power consumption compared to continuous operation of high-power devices.
Solution Approach 2:
The system uses low-power sensor devices to monitor conditions and automatically trigger high-power sensor devices when necessary, eliminating the need for continuous high-power operation. The low-power devices essentially serve themselves by continuously operating at minimal power, while the high-power devices serve the system's critical detection needs only when required.
Data Source
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AI summary
In one embodiment, a sensor device includes a sensor configured to obtain sensor data and a communication interface configured to communicate with a set of secondary sensor devices. The set of secondary sensor devices comprise a set of secondary sensors and a set of secondary processing devices. The set of secondary sensors obtain additional sensor data. The set of secondary processing devices process the additional sensor data based on a set of secondary machine learning models, and generate activation data based on the additional sensor data. The sensor device also includes a processing device. The processing device is also configured to receive the activation data from the set of secondary sensor devices and cause the sensor to obtain first sensor data based on the activation data. The processing device is further configured to generate one or more inferences based on the first sensor data and a machine learning model.