Routine-Aware Tracking Using Wi-Fi Scans 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, which are not always available, leading to inefficient power usage and user annoyance.
Innovation Solution
A tracking device that scans access point signals, learns movement patterns using machine learning, and adjusts power consumption based on detected routines, reducing unnecessary transmissions and conserving battery life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS and cellular signals are used for tracking, then location accuracy is improved, but power consumption increases
Solution Approach 1:
The system uses periodic Wi-Fi scanning at intervals instead of continuous GPS tracking, reducing power consumption while maintaining adequate location accuracy through opportunistic updates when Wi-Fi signals are available
Solution Approach 2:
The system replaces expensive, power-intensive GPS cellular transmissions with cheaper, lower-power Wi-Fi scanning and opportunistic Bluetooth Low Energy (BLE) communications, using multiple lower-cost signaling methods instead of relying on high-power GPS
2Reliability
If continuous GPS tracking is performed, then location monitoring reliability is improved, but power consumption increases
Solution Approach 1:
The system uses on-device machine learning models that autonomously analyze Wi-Fi and sensor data to detect routine patterns and predict locations, eliminating the need for continuous cloud communication and high-power GPS while maintaining reliable tracking through intelligent local processing
Solution Approach 2:
The system performs preliminary machine learning training and routine detection during periods when the device is stationary or low-power, preparing prediction models in advance that enable accurate location estimation without continuous active tracking
3Measurement precision
If frequent location transmissions are sent, then tracking accuracy is improved, but transmission costs increase
Solution Approach 1:
The system uses feedback from detected routine patterns to dynamically adjust transmission frequency, sending location updates only when deviations from predicted routines are detected, thereby reducing unnecessary transmissions while maintaining accurate tracking of significant location changes
4Reliability
If unnecessary alerts are sent to users, then safety monitoring is improved, but user experience deteriorates
Solution Approach 1:
The machine learning model autonomously distinguishes between routine and non-routine locations, automatically filtering out false alarms from predictable locations while maintaining safety monitoring for genuine deviations, eliminating unnecessary user alerts
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.