IoT Location Learning with Stationary Detection and Clustering
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Solution Overview
Problem
Existing IoT/M2M solutions struggle to automatically learn and identify locations of interest, particularly for moving devices like commercial vehicle fleets, where determining popular spots for services or traffic management is challenging.
Innovation Solution
A computer-implemented method and system that uses IoT devices to learn and store location information, detect stationary locations over a predetermined duration, classify these locations as interests based on predefined criteria, and cluster them into larger groups using a clustering engine.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated learning of locations is implemented using IoT devices, then productivity is improved, but device complexity increases
Solution Approach 1:
The system performs automated learning of locations of interest using IoT device data without requiring manual human input. The processor automatically detects stationary locations, determines if they meet predefined criteria for locations of interest, and clusters them into groups, enabling the system to serve itself in identifying and organizing location data.
Solution Approach 2:
The location learning process is divided into distinct functional modules: a detector that identifies stationary locations, a determiner that evaluates predefined criteria, and a clustering engine that groups locations. This segmentation allows each component to handle specific tasks independently, improving overall system efficiency while managing complexity through modular design.
2Measurement precision
If real-time data processing is performed to identify locations, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The system processes location data by detecting periods of stationarity rather than continuously analyzing all movement data. The processor identifies locations where no movement has occurred over a predetermined duration, converting continuous monitoring into periodic assessment based on time intervals, thereby reducing energy consumption while maintaining detection precision.
3Loss of information
If clustering is performed to group locations, then loss of information is reduced, but device complexity increases
Solution Approach 1:
The clustering engine combines multiple individual location data points into larger grouped clusters based on spatial proximity and predefined criteria. This merging process consolidates scattered location information into organized groups, preserving the essential characteristics of each location while reducing data fragmentation and information loss.
Data Source
AI summary
In one example embodiment, a computer-implemented method and system for learning places of interest are disclosed. The method includes learning and storing location information of at least one mobile device; detecting a location where no movement of the at least one mobile device has occurred over a pre-determined duration of time; determining whether the detected location is classified as a location of interest based on a predefined criteria; and clustering the learned location of interest into bigger groups based on location information of the learned location of interest using a pre-defined criteria.