Routine-Aware Tracking Using Access Point Signals to Cut Power Use
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Solution Overview
Problem
Existing electronic tracking devices consume excessive power and transmit unnecessary alerts due to reliance on GPS and cellular signals, even when not needed, leading to inefficient power usage and user annoyance.
Innovation Solution
A tracking device that scans access point signals and learns movement patterns using machine learning, allowing it to predict routines and reduce power-intensive transmissions by avoiding unnecessary GPS and cellular signals, and sending notifications only when deviations from predefined routines occur.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GPS and cellular signals are used continuously for tracking, then location accuracy and real-time monitoring are improved, but power consumption increases excessively
Solution Approach 1:
The system performs preliminary actions by scanning for access point signals and storing historical data before GPS-based location determination is needed. The machine learning model is trained in advance on this historical data to recognize routines, enabling the device to avoid unnecessary GPS transmissions by predicting when location data is actually needed based on learned patterns.
Solution Approach 2:
The tracking device uses its own scanned access point signals and onboard sensors to determine location and detect routines independently, without continuously relying on external GPS or cellular infrastructure. The device serves itself by using the machine learning model to autonomously decide when GPS transmission is necessary, reducing power consumption while maintaining tracking reliability.
2Productivity
If the tracking device continuously monitors and transmits location data, then real-time tracking capability is improved, but unnecessary transmissions increase leading to higher costs and user annoyance
Solution Approach 1:
The system implements feedback by using the machine learning model to continuously compare current signal patterns against learned routines and adjust transmission behavior accordingly. The model provides feedback on whether a location update is necessary based on detected deviations from routine patterns, enabling the device to transmit only when actually needed while maintaining responsive tracking.
Solution Approach 2:
The device dynamically changes the parameter of transmission frequency based on detected routine deviations. When the machine learning model detects that the tracked subject is following an expected routine, the transmission parameter is reduced or paused. When deviations are detected, the transmission parameter is activated to send location data, optimizing the balance between tracking responsiveness and reducing unnecessary transmissions.
3Use of energy by moving object
If the tracking device uses machine learning to predict routines and avoid transmissions, then power efficiency is improved, but device complexity increases
Solution Approach 1:
The machine learning model acts as an intermediary component that processes historical signal data and generates predictions about future location needs. This intermediary layer translates complex patterns in access point signals into actionable predictions, enabling the device to make intelligent transmission decisions without requiring direct complex analysis of all possible signal combinations.
Data Source
AI summary
A method comprises accessing historical signal and other information received from a tracking device configured to scan for signals transmitted by local devices and record other data as the tracking device moves within the geographic area during each of a plurality time intervals. A training dataset is generated based on the historical signal and other information and used to train a machine learning model configured to predict tracking device movement patterns. The machine learning model is applied to current signal and other information to detect a variance from one or more predefined routines associated with the tracking device. A notification is sent to a monitoring device associated with the tracking device in response to detecting the variance from the one or more predefined routines associated with the tracking device.