Dynamic eSIM Configuration for IoT Devices via Activity Context Prediction
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Solution Overview
Problem
Current eSIM management for IoT devices is inefficient, particularly in dynamic environments where devices are reused for different activities, leading to potential network connectivity issues due to manual reconfiguration and lack of adaptive network selection based on activity context.
Innovation Solution
A computer-implemented method using a convolutional neural network (CNN) and AI to dynamically adjust eSIM configurations by predicting future activities and selecting appropriate network connections based on context, capability, and activity-specific requirements, ensuring seamless connectivity across different tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual reconfiguration of eSIM settings is used for different activities, then device complexity is reduced, but network connectivity reliability deteriorates due to potential configuration errors and delays
Solution Approach 1:
The system performs self-service by automatically detecting activity context through sensors and AI, selecting appropriate network providers, and reconfiguring eSIM settings without manual intervention. The device monitors its own state, predicts future activities, and autonomously adjusts network configuration to maintain optimal connectivity.
Solution Approach 2:
The system dynamically changes network configuration parameters based on detected activity context. Different activity types (indoor/outdoor, stationary/moving) trigger different eSIM profile selections and network parameter adjustments, optimizing connectivity for each specific context automatically.
2Adaptability or versatility
If automated AI-based dynamic reconfiguration is implemented, then network connectivity reliability is improved through adaptive selection, but device complexity increases due to AI components and sensors
Solution Approach 1:
The IoT device incorporates multi-functionality by integrating diverse sensors (accelerometer, GPS, microphone, camera) and AI processing capabilities into a single platform. These components serve multiple purposes: activity detection, context prediction, network selection, and configuration management, reducing the need for separate specialized systems.
Solution Approach 2:
The system performs preliminary action by training the AI model in advance with historical activity data and network performance information. The trained model is then deployed to predict future activities and pre-select optimal network configurations before connectivity issues arise, enabling proactive rather than reactive adaptation.
3Productivity
If multiple IoT devices are reused for different activities, then device versatility is improved, but network connectivity stability deteriorates due to manual reconfiguration delays
Solution Approach 1:
The system implements dynamics by continuously monitoring real-time sensor data and activity context, dynamically switching between different eSIM profiles and network providers based on current and predicted future activities. This dynamic adaptation ensures stable connectivity as devices transition between different usage scenarios without manual intervention.
Data Source
AI summary
An approach for the capability of managing IoT devices (including eSIMs) and dynamically switch those IoT devices to different network/wireless carriers based on the activity performed is disclosed. The approach can identify poor network connectivity problems with the IoT devices while performing the activities or switching/moving from one activity to another activity and can predict the context of performing the activities with various IoT devices based on identified information. The approach can dynamically adjust the configuration setting of those IoT devices to connect to the appropriate network service provider based on the prediction context of those activities.