IoT Error Correction via Cloud Inference Models
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Solution Overview
Problem
IoT devices often experience errors such as connectivity issues, firmware update failures, and permission problems, requiring cumbersome human intervention for troubleshooting, leading to inefficient device downtime and scalability challenges in large networks.
Innovation Solution
Implementing a system where IoT devices generate logs that are analyzed by a cloud-based troubleshooting service using inference models, such as artificial neural networks, to identify errors and automatically execute corrective actions like firmware updates or digital certificate installations, with the option to use a proxy device for local analysis or cloud-based processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual troubleshooting processes are used for IoT device errors, then human expertise can address complex issues, but device downtime increases and scalability is limited
Solution Approach 1:
The system enables IoT devices to automatically diagnose and correct their own errors through self-service mechanisms. Error logs are automatically generated, analyzed by machine learning models, and corrected through automated remediation actions, eliminating the need for manual human intervention in the troubleshooting process.
Solution Approach 2:
The patent replaces the mechanical human troubleshooting process with an automated electronic system. Machine learning models and automated analysis systems substitute for human experts, processing error logs and determining corrections electronically without requiring physical human presence or manual intervention.
2Productivity
If automated troubleshooting systems are implemented, then device downtime is reduced and scalability improves, but system complexity increases
Solution Approach 1:
The system introduces intermediary components such as log analysis services, machine learning models, and automated remediation systems that mediate between the IoT device and the troubleshooting process. These intermediaries handle the complexity of error analysis and correction, allowing the core IoT device to remain relatively simple while benefiting from sophisticated automated troubleshooting capabilities.
3Reliability
If extensive human involvement is required for error correction, then complex technical issues can be addressed with domain expertise, but the process becomes cumbersome and does not scale
Solution Approach 1:
The system enables automated self-diagnosis and self-correction capabilities, where IoT devices generate error logs that are automatically analyzed and corrected without requiring human operators. This maintains correction accuracy through sophisticated algorithms while dramatically simplifying the operational process.
Solution Approach 2:
The system implements feedback loops where error logs are continuously monitored, analyzed, and used to trigger automated corrections. The system learns from past errors and continuously improves its troubleshooting capabilities, maintaining high accuracy while keeping the process simple and automated.
Data Source
AI summary
A method and system for correcting embedded device errors. The method may include receiving a first log generated by a first device though a first channel, receiving a second log generated by the first device that identifies a malfunction of the first device through a second channel, determining a corrective action to cause the first device to cease the malfunction, based at least in part on the second log and an inference model, and sending a message to a second device based at least in part on the corrective action.