IoT Lifecycle Management via Network Usage Pattern Analysis
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Solution Overview
Problem
Managing the lifecycle of IoT devices using cellular/wireless networks is complex and time-consuming, especially for inactive devices, as it is difficult for humans to detect network usage patterns across a large number of devices, leading to inefficiencies and increased costs due to unnecessary subscription fees.
Innovation Solution
A method and system that utilize machine learning algorithms to analyze network usage patterns, determine inactivity periods, and automatically recommend and implement lifecycle changes, such as suspension or deactivation, for IoT devices that have not used cellular or wireless network services for a predetermined time, thereby optimizing subscription management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual monitoring of network usage information is used for IoT devices, then human operators can detect device activity, but the process becomes complex and time-consuming especially for large numbers of devices
Solution Approach 1:
The system automatically monitors network usage information, determines inactivity periods, and recommends lifecycle changes without requiring human operators to manually track each device. The server autonomously collects usage data, analyzes patterns, and generates recommendations, allowing the system to serve itself rather than relying on human intervention for routine monitoring tasks.
Solution Approach 2:
Manual human monitoring and analysis of network usage patterns is replaced with an automated server-based system that collects, stores, and analyzes usage information programmatically. The mechanical process of human operators reviewing device status is substituted with automated computational analysis that processes network usage data efficiently at scale.
2Loss of information
If continuous monitoring of all IoT devices is performed, then accurate network usage patterns are captured, but subscription costs increase for maintaining active monitoring on inactive devices
Solution Approach 1:
The system performs monitoring at different levels of intensity based on device status. Instead of continuously monitoring all devices with the same level of detail, the system adjusts monitoring based on detected activity patterns, reducing the level of monitoring for devices that show consistent inactivity while maintaining detailed monitoring for active devices. This partial action approach captures necessary usage patterns while reducing unnecessary monitoring costs.
Solution Approach 2:
The system changes the monitoring parameters dynamically based on device behavior. When a device transitions from active to inactive status, the monitoring frequency and depth are adjusted accordingly. The server modifies monitoring parameters such as check intervals and data collection depth based on the device's operational state, optimizing the balance between information accuracy and resource consumption.
3Ease of manufacture
If static threshold parameters are used to determine inactivity periods, then implementation is simple, but the system cannot adapt to varying device usage patterns
Solution Approach 1:
The system transitions from static threshold parameters to dynamic, learned inactivity periods that adapt to individual device usage patterns. Instead of applying a fixed time threshold to all devices, the server learns the specific usage patterns of each device type and determines customized inactivity periods based on historical data. This allows the system to adapt to varying usage patterns while maintaining automated decision-making capabilities.
Solution Approach 2:
The system implements feedback loops where historical network usage information is continuously analyzed to refine and update inactivity period determinations. The server learns from past device behavior patterns and adjusts future monitoring and recommendation thresholds based on this feedback. This creates a self-improving system that becomes more accurate over time while maintaining implementation simplicity through automated learning processes.
Data Source
AI summary
A computer-implemented method and system for managing IoT device lifecycle for IoT devices using cellular or wireless networks are disclosed. The method includes receiving network usage information for one or more devices operating on a cellular network; storing network usage information for the one or more devices operating on a cellular network; analyzing the stored data network information for the one or more devices as historical network usage information for each of the one or more devices operating on a cellular network; determining which of the one or more devices have not used cellular network for a pre-determined period of time, where the pre-determined period of time is learned based on the historical network usage information, or provided as one or more static threshold parameters; and recommending lifecycle changes for each of the one or more devices that have not used cellular network for the pre-determined period of time.