IoT Sensor Network for User Matching and Meet-up Recommendations
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Solution Overview
Problem
Current social networking methods for online and in-person interactions are inefficient, as they rely on users finding each other's profiles and agreeing on meeting places and times, lacking accurate location detection and efficient matching of similar users.
Innovation Solution
A system utilizing IoT devices to detect user presence and profiles, employing a trained machine learning model to recommend meet-up points based on user similarity and occupancy, providing direction aids through graphical user interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manually find each other's profiles and agree on meeting places and times, then social networking can be established, but the process is inefficient and time-consuming
Solution Approach 1:
The system performs preliminary actions by automatically detecting user presence via IoT devices, retrieving user profiles in advance, and pre-calculating similarity measures before users even request a connection. This eliminates the need for users to manually search for profiles and negotiate meeting details, significantly improving efficiency and reducing time loss.
Solution Approach 2:
The system enables self-service by automatically matching users based on their profiles and occupancy data without requiring active user participation in the matching process. Users simply need to be present at a location, and the system autonomously identifies compatible matches, generates recommendations, and arranges meeting details, freeing users from manual effort.
2Measurement precision
If traditional location detection methods are used, then meeting places can be determined, but location accuracy and efficiency are insufficient
Solution Approach 1:
The system introduces IoT devices as intermediaries between users and the location detection process. These devices are placed at specific meet-up points and automatically detect user presence through device signals, providing precise location information without requiring users to manually input or share their locations. This intermediary layer enhances both accuracy and efficiency of location determination.
3Reliability
If user profiles and attributes are analyzed manually, then similar users can be identified, but the matching process is complex and computationally intensive
Solution Approach 1:
The system transforms complex profile data into simplified similarity scores through automated calculations. By converting multiple user attributes (interests, demographics, preferences) into a single comparable similarity metric, the system maintains high matching accuracy while reducing computational complexity and making the matching process more manageable.
4Reliability
If meet-up points are selected without considering occupancy, then meeting arrangements can be made quickly, but location availability cannot be guaranteed
Solution Approach 1:
The system implements feedback by continuously monitoring occupancy data from IoT devices at various meet-up points. This real-time occupancy information feeds back into the matching algorithm, allowing the system to automatically select available meet-up points that both users can attend. The feedback loop ensures reliable availability guarantees while maintaining efficient matching through automated real-time data integration.
Data Source
AI summary
Systems and methods of the present disclosure enable IoT-based social networking to detect, using IoT devices at a location, device signals associated with a first user device of a first user. A first user profile associated with the first user device is determined in response to the device signals. User attributes stored in the first user profile are accessed and extracted. A trained profile similarity model is used to determine similarity measures between the first user profile and other user profiles based on the user attributes. At least one similar user profile to the first user profile is identified based on the similarity measures. A meet-up point occupancy of each meet-up point at the location is determined based on a record of meet-up points to identify an open meet-up point. A meet-up recommendation is generated indicating the open meet-up point and the other user profiles.