Proximity Emergency Alerts With Predictive Geofencing
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Solution Overview
Problem
Existing systems fail to provide accurate real-time alerts for approaching emergency vehicles due to challenges in managing power and data resources, predicting entity positions, and handling distractions from non-essential software and media, especially at high speeds.
Innovation Solution
A system that predicts the position of entities using current and historical location data, verifies accuracy, adjusts alert criteria, and pauses non-essential software and media during emergencies, utilizing a computer-implemented software to generate proximity-based alerts and manage power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data transmission frequency is increased to improve prediction accuracy, then alert accuracy is improved, but power consumption increases
Solution Approach 1:
The system dynamically adjusts the data transmission frequency based on relative speed between entities. When entities are moving quickly relative to each other, the frequency increases to maintain prediction accuracy. When relative speed is low, the frequency decreases to conserve power. This dynamic adaptation resolves the contradiction between maintaining high prediction accuracy and managing power consumption effectively.
2Speed
If relative speed between entities increases, then warning time is shortened, but the system must process data faster to maintain accuracy
Solution Approach 1:
The system performs preliminary actions by continuously monitoring location data and maintaining predictive models even before critical situations arise. When entities are moving at high relative speeds, the system has already established baseline predictions and can quickly adjust alert timing. This preliminary preparation allows the system to provide accurate warnings even when warning time is dramatically shortened due to high speeds.
3Reliability
If more location data is collected to improve prediction accuracy, then alert reliability is improved, but computational burden increases
Solution Approach 1:
The system extracts only the most relevant features from location data for prediction purposes. Rather than processing all available location data in full detail, the system identifies and processes key parameters such as relative position, velocity vectors, and acceleration patterns. This selective extraction maintains alert reliability by focusing on critical predictive factors while significantly reducing the computational burden on devices.
Data Source
AI summary
An emergency alert system and method for providing proximity-based alerts in real-time is provided. The system generates a virtual perimeter around one or more entities, wherein real-time location information is used to determine whether a first entity is within the virtual perimeter generated around a second entity, and wherein the system will send an alert once the first entity enters the perimeter and terminate the alert once the first entity exits the perimeter. The system may the rate of data transmission depending on whether the first entity is registered in a database corresponding to a geographical region where the system is administered and whether the second device is in an active broadcasting mode. The system also predicts the location of the first and second entities and uses the difference between the predicted location and actual location to adjust the location and size of the perimeter to provide adequate warning.