Mobile Device Location Search Using Spatial and Temporal Indexing
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Solution Overview
Problem
Current methods for locating mobile devices in arbitrary geographical boundaries are inefficient and impractical, especially when dealing with large numbers of devices or changing location information, as they rely on naive linear scans that increase search time linearly with the number of devices and fail to consider temporal aspects effectively.
Innovation Solution
A system and method that generate a one-dimensional spatial index and a temporal index to enable near-real-time searching of mobile devices with changing location information, using mathematical constructs like Peano Curves and Z-ordering for spatial indexing and segmenting time into continuous slots for efficient geographical and temporal searches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a naive linear scan is used to search for devices in geographical boundaries, then the search method is simple to implement, but the search time increases linearly with the number of devices making it impractical for large-scale deployments
Solution Approach 1:
The patent divides the two-dimensional geographical space into multiple one-dimensional spatial slots using space-filling curves (Peano curves, Z-ordering). This segmentation transforms the search problem from examining all devices to only examining devices in relevant spatial slots, reducing search time from O(N) to O(sqrt(N)) while maintaining implementation feasibility through standardized indexing algorithms
Solution Approach 2:
The patent transforms the two-dimensional geographical search problem into a one-dimensional search problem by mapping 2D coordinates to 1D spatial slots using space-filling curves. This dimensionality reduction enables efficient indexing and searching while preserving spatial locality, allowing fast retrieval without the complexity of two-dimensional data structures
2Measurement precision
If location information is searched synchronously for each device, then the location data is accurate, but the time required becomes unacceptable for large numbers of devices
Solution Approach 1:
The patent pre-computes and stores spatial indices for all devices in advance, organizing location data into spatial slots before search queries are executed. This preliminary indexing allows search operations to quickly retrieve pre-organized data without performing computationally intensive calculations during the search itself, maintaining accuracy while enabling high throughput
Solution Approach 2:
The patent implements a hybrid approach where spatial indices are dynamically updated as devices move, while search operations remain static and efficient. The system adapts to changing location data by updating only the necessary spatial slot assignments rather than re-computing entire search structures, maintaining both accuracy and performance
3Reliability
If a naive linear scan examines every device location report, then all devices are thoroughly searched, but the computational resources and time required are excessive for millions of devices
Solution Approach 1:
The patent applies different processing strategies to different spatial regions: devices in the searched geographical area are examined in detail, while devices outside the area are excluded from search based on their spatial slot assignments. This localized approach ensures complete search coverage for relevant devices while avoiding wasteful examination of irrelevant devices, reducing computational resource consumption
Data Source
AI summary
A system and method for generating a one-dimensional spatial index and a temporal index in relation to one or more two-dimensional location points of one or more mobile devices to enable near-real-time searching of devices having fast-changing location information is provided for. The present invention may be used in a variety of implementations including being used in conjunction with searching for mobile devices within a geographical area for a time period; finding nearby devices within a time period; tracking device movement within a time period; determining clusters of devices for identification and location across different geographical regions at once.