Wireless Sensor Response Time Control via ML Prediction
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Solution Overview
Problem
Current object monitoring systems face a trade-off between low latency and long battery life, as high advertising frequencies for wireless sensor devices result in short response times but high power consumption, while low frequencies lead to longer latency, impacting usability and customer workflow.
Innovation Solution
A machine learning model predicts the likelihood of user interaction with wireless sensor devices, dynamically adjusting the response time by adapting advertising frequencies or reception windows based on calculated probabilities, ensuring acceptable latency while optimizing battery life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If advertising frequency is increased to reduce latency, then response time is improved, but battery consumption increases
Solution Approach 1:
The patent implements dynamic adjustment of advertising frequency based on operational context. The system transitions between different advertising modes (e.g., low frequency for normal operation, high frequency when interaction is detected or expected), allowing response time to be optimized without continuously consuming high power. This dynamic behavior resolves the contradiction by making the system adaptable to different states rather than operating at fixed parameters.
Solution Approach 2:
The system changes the advertising frequency parameter based on detected conditions such as user proximity, interaction history, or system state. By adjusting this critical parameter dynamically, the system achieves low latency when needed while maintaining low power consumption during normal operation, thus resolving the trade-off between response time and battery consumption.
2Use of energy by moving object
If advertising frequency is decreased to extend battery life, then energy consumption is reduced, but response time increases
Solution Approach 1:
The system employs periodic advertising at low frequency during normal operation to conserve battery life, then switches to more frequent periodic actions when interaction is detected or anticipated. This periodic modulation of advertising frequency allows the system to extend battery life during idle periods while maintaining acceptable response times when users actually interact with the device.
Solution Approach 2:
The system performs preliminary detection of user presence or interaction intent before increasing advertising frequency. By detecting early signs of user interaction (such as proximity detection or previous interaction patterns), the system can prepare to switch to high-frequency mode in advance, ensuring low response time is achieved only when actually needed, thus optimizing both battery life and responsiveness.
3Ease of operation
If high advertising frequency is used to improve user experience, then system responsiveness is improved, but power consumption increases
Solution Approach 1:
The system dynamically adapts its advertising frequency based on user interaction patterns and system state, providing high responsiveness when users are actively engaging with the device while maintaining low power consumption during passive periods. This dynamic behavior ensures optimal user experience without the penalty of continuously high power consumption.
Solution Approach 2:
The system uses feedback from user interactions, proximity detections, and system state to adjust advertising frequency. By monitoring whether users are actively using the device and adjusting frequency accordingly, the system maintains good user experience during interaction while conserving power during non-use periods, thus resolving the contradiction between user experience and power consumption.
Data Source
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AI summary
The present disclosure relates to an object monitoring system comprising a plurality of wireless sensor devices and at least one user device, and in particular to a method for controlling a response time of one of the wireless sensor devices in such a system. According to a first aspect, the disclosure relates to a method for controlling a response time of a wireless sensor device. The method comprises determining one or more metrics representing a present state of an object. The method further comprises a probability that one of at least one user devices will interact with the wireless sensor device within a pre-determined time period by executing a trained model with the one or more determined metrics as input and controlling the response time of the wireless sensor device based on the calculated probability. The disclosure also relates to a corresponding control unit and to a computer program for performing the proposed method.