IoT Device Grouping via Motion and Location Tracking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The process of manually pairing multiple IoT devices is cumbersome and time-consuming, especially in settings where rapid device pairing is required, such as emergency response teams, leading to potential safety hazards and inefficiencies.
Innovation Solution
A computer-implemented method and system that automatically groups IoT devices by identifying individual interaction zones with pre-established device mounting regions, recognizing device categories based on location and motion patterns, and dynamically reassigning devices to new users when transferred, using a stick figure model for anatomically preferred wearing locations and motion analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual pairing of IoT devices is used, then device association can be established, but the process is cumbersome and time-consuming
Solution Approach 1:
The system enables automatic device pairing through self-service mechanisms where IoT devices automatically associate with users based on detected motion patterns and interaction zones. The computer system autonomously determines device-user relationships without requiring manual intervention, thereby eliminating the cumbersome manual pairing process while maintaining accurate device associations.
Solution Approach 2:
The system performs preliminary actions by pre-establishing interaction zones and motion patterns before actual device pairing occurs. The computer system tracks device locations and motion sequences in advance, preparing the framework for automatic association. This preliminary setup enables rapid automatic pairing when devices enter designated zones, resolving the contradiction between pairing speed and process complexity.
2Loss of time
If automatic device grouping is implemented, then pairing time is reduced, but system complexity increases
Solution Approach 1:
The system replaces manual mechanical pairing operations with automated computational processes. Instead of requiring users to physically connect or configure devices manually, the computer system uses automated tracking, pattern recognition, and logical determination to establish device associations. This substitution dramatically reduces pairing time while the complexity is managed through software-based solutions rather than complex hardware interfaces.
Solution Approach 2:
The computer system acts as an intermediary between IoT devices and users, mediating the association process. The system tracks device locations, interprets motion patterns, and determines relationships without requiring direct user-device interaction. This intermediary approach simplifies the overall system architecture by centralizing the complexity in a single coordinating system rather than requiring complex protocols between each device pair.
3Measurement precision
If device tracking and motion analysis are used, then accurate device-user association is achieved, but computational requirements increase
Solution Approach 1:
The system applies partial action by tracking only the essential motion parameters needed for device-user association rather than comprehensive device monitoring. The computer system focuses on detecting specific interaction zone entries and basic motion patterns sufficient for determination, avoiding excessive computational analysis. This selective tracking approach maintains accurate device-location association while minimizing unnecessary energy consumption from over-processing data.
Data Source
AI summary
A computer automatically groups IoT devices. The computer identifies at least one individual interaction zone within an operating environment, the at least one individual interaction zone characterized by pre-established device mounting regions each associated with a device category. The computer receives a location indicating signal from an IoT device located within the at least one individual interaction zone. The computer, in response to receiving the location indicating signal and based at least in part thereon, the computer recognizes that the IoT device is occupying one of the device mounting regions. In response to the recognition, the computer determines that the IoT device belongs to the device category associated with the occupied mounting region. In response to the determination, the computer adds the device to an IoT device group associated with the individual interaction zone associated with the occupied mounting region.