Mobile Device Location-Assisted Signal Prediction and Buffering
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Solution Overview
Problem
Mobile devices experience disruptions in communication services when traveling out of range of wireless network signals, leading to interruptions in important activities such as conversations or service usage.
Innovation Solution
A mobile device uses GPS and built-in sensors to predict approaching areas with weak or no network signal, proactively buffering data packets and alerting the user to allow for anticipatory actions like changing course or preparing content for continued playback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a mobile device relies on real-time network access for communication services, then service quality is maintained when signal is available, but service interruptions occur when traveling out of range of wireless network signals
Solution Approach 1:
The system performs preliminary actions by detecting when a mobile device is approaching a region with poor network signal quality using GPS location data and pre-caching data packets representing content (music, video, etc.) before the device actually enters the dead zone. This advance preparation ensures service continuity without requiring real-time network access during signal loss.
Solution Approach 2:
The system applies preliminary anti-action by proactively counteracting the harmful effect of signal loss through predictive buffering. By using location-based predictions and sensor data to anticipate entry into poor signal regions, the system pre-loads content and alerts users in advance, preventing service interruption rather than reacting to it after occurrence.
2Reliability
If the mobile device proactively buffers data packets before entering dead zones, then service continuity is improved, but device complexity increases due to location prediction and buffering mechanisms
Solution Approach 1:
The system achieves multi-functionality by combining existing mobile device components (GPS receiver, accelerometer, gyroscope, barometer, network interface) to perform multiple functions: location tracking, motion detection, signal quality assessment, and data buffering. Rather than adding dedicated hardware, the invention leverages universal components already present in modern mobile devices.
Solution Approach 2:
The system implements self-service by using the mobile device's own sensors and processing capabilities to autonomously detect approaching dead zones and trigger buffering operations without requiring external infrastructure or manual user intervention. The device monitors its own location, calculates predicted paths, and automatically manages data caching.
3Reliability
If the mobile device uses GPS and sensors to predict dead zone entry, then service interruptions are reduced, but energy consumption increases due to continuous monitoring
Solution Approach 1:
The system employs periodic action by monitoring location and sensor data at intervals rather than continuously. The mobile device periodically updates its position using GPS and sensor fusion, calculates predicted paths to potential dead zones, and triggers buffering only when prediction indicates imminent entry into poor signal regions, rather than maintaining constant high-power monitoring.
Solution Approach 2:
The system applies dynamics by adaptively adjusting monitoring intensity based on contextual factors such as device motion state, current location relative to known dead zones, and network conditions. When the device is stationary or in areas with good signal, monitoring intensity is reduced. When motion detects approaching dead zones, monitoring and buffering intensity increases dynamically.
Data Source
AI summary
A digitally stored map can indicate the signal quality for each of the map's regions. A device can determine its location, speed, and direction using global positioning system (GPS) and other sensors. Based on this information, the mobile device can predict a field of locations within which the device will probably be located within a specified future time frame. Based on both the information indicating signal quality and the probable future field of locations, the device can estimate a moment at which the device will probably begin to suffer from low-quality or absent signal. Using this prediction, the device can proactively perform a variety of anticipatory remedial actions. For example, the device can begin allocating a greater portion of currently available wireless network communication bandwidth to the reception of data packets that represent content that is being streamed to the device, so that the device can proactively buffer those packets.