Cognitive Engine for IoT Device Connectivity Management
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Solution Overview
Problem
The rapid growth of Internet of Things (IoT) devices owned by individuals, each used in specific contexts, makes manual tracking and operation inefficient, leading to suboptimal usage and potential waste.
Innovation Solution
A cognitive IoT device management system that learns user interactions and contexts to optimize IoT device connectivity by generating optimal plans for device usage, including recommendations and actions, thereby reducing unnecessary device activation and improving user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual tracking and operation of IoT devices is performed, then user control and awareness of device usage is maintained, but efficiency and time consumption deteriorate
Solution Approach 1:
The cognitive engine automatically learns user preferences and device usage patterns, then autonomously manages IoT device operations without requiring manual user intervention. The system performs connectivity analysis, generates optimal plans, and executes actions automatically, allowing the system to serve itself rather than requiring continuous manual tracking and operation by the user.
2Productivity
If cognitive automation is implemented to manage IoT devices, then efficiency and user experience are improved, but system complexity increases
Solution Approach 1:
The cognitive engine serves multiple functions within a single system: it learns user preferences, detects context changes, performs connectivity analysis, generates optimal plans, and executes device management actions. By consolidating these diverse functions into one multi-functional cognitive system rather than separate specialized systems, the patent manages complexity while achieving high productivity in IoT device management.
3Reliability
If continuous monitoring of user context is performed, then device activation accuracy is improved, but energy consumption increases
Solution Approach 1:
The cognitive engine monitors user context changes periodically or event-driven rather than continuously. It detects changes in user context associated with the identified user and triggers connectivity analysis only when relevant changes occur, rather than maintaining constant monitoring. This periodic or event-driven approach maintains reliable device activation accuracy while significantly reducing energy consumption compared to continuous monitoring.
Data Source
AI summary
A method, computer system, and a computer program product for managing and optimizing connectivity of a plurality of IoT devices based on cognitive learning is provided. The present invention may include identifying a user, wherein a plurality of user context data is stored in a user profile. The present invention may then include detecting a change in at least one current user context associated with the identified user. The present invention may also include performing a connectivity and management analysis on the detected change in the at least one current user context. The present invention may then include generating at least one optimal plan for the plurality of IoT devices associated with the identified user based on the performed connectivity and management analysis. The preset invention may further include performing at least one action based on the generated at least one optimal plan.