Server proximity detection using travel radius estimation
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Solution Overview
Problem
Current proximity detection systems in computing devices are energy inefficient due to the frequent transmission of location information, which can unnecessarily drain batteries, as they require devices to repeatedly send their GPS coordinates at short intervals to maintain up-to-date location data.
Innovation Solution
Implementing a method where computing devices transmit location information at longer intervals and estimate potential movements based on previous data, allowing a server to determine if other devices are within proximity by calculating a travel radius, and only querying those likely to be nearby for updated information, thus reducing unnecessary location updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If devices transmit location information frequently at short intervals, then proximity detection accuracy is improved, but battery consumption increases
Solution Approach 1:
The system performs preliminary actions by estimating the travel radius of the second device based on previously received location information and device movement characteristics. This preliminary estimation allows the server to determine which devices are likely to be within proximity before actually querying for updated location data, thereby reducing unnecessary frequent transmissions and preserving battery life while maintaining detection accuracy.
Solution Approach 2:
The system changes the parameter of location update frequency dynamically. Instead of using a fixed short interval for all devices, the server adapts the query frequency based on the estimated travel radius and proximity probability of each device. Devices with smaller travel radii or lower probability of being in proximity are queried less frequently, optimizing the balance between detection accuracy and energy consumption.
2Use of energy by moving object
If devices transmit location information at longer intervals, then battery consumption is reduced, but proximity detection accuracy deteriorates
Solution Approach 1:
The server performs preliminary estimation of the travel radius using previously received location information and device movement patterns before determining whether to query for updated location data. This preliminary action enables the system to maintain accurate proximity detection by identifying devices that are likely to be within proximity even when location updates occur at longer intervals, thus preserving detection accuracy while reducing energy consumption.
Solution Approach 2:
The concept of travel radius acts as an intermediary between the device's last known location and its current position. By estimating the travel radius based on device characteristics and movement patterns, the server can infer whether a device is likely within proximity without requiring frequent exact location updates, thereby maintaining detection accuracy while extending the interval between transmissions.
3Reliability
If the server queries all devices for updated location information, then proximity detection completeness is improved, but network traffic and processing load increase
Solution Approach 1:
The system extracts and focuses only on devices that are likely to be within proximity by estimating their travel radius and comparing it with the proximity radius of the first device. Instead of querying all devices, the server selectively queries only those with overlapping travel and proximity radii, thereby maintaining detection completeness for relevant devices while significantly reducing network traffic and processing load.
Solution Approach 2:
The system applies different query strategies to different devices based on their local characteristics. Devices with larger travel radii or higher probability of being in proximity are queried more frequently, while devices with smaller travel radii are queried less frequently. This localized quality approach ensures proximity detection completeness for devices that matter while minimizing unnecessary network traffic.
Data Source
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AI summary
In general, aspects of the present disclosure may be directed to techniques for efficient proximity detection. A server device may receive from a first computing device current location information associated with the first computing device. The server device may estimate a travel radius of a second computing device based at least in part on location information previously received from the second computing device, and may determine that the travel radius of the second computing device overlaps a proximity radius of the first computing device. The server device may query the second computing device for updated location information. The server device may receive the updated location information for the second computing device, and may determine that the second computing device is physically located substantially within the proximity radius of the first computing device based at least in part on the updated location information. In response, the server device may send to the first computing device an identification of the second computing device.