Distributed IoT Framework for Resilient M2M Management
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Solution Overview
Problem
The management of IoT systems becomes increasingly complex due to the growing number and diversity of connected devices, making it challenging to ensure scalability, resiliency, and reliability, particularly in large-scale deployments where human intervention is prohibitively complex and incremental failures can lead to system-wide issues.
Innovation Solution
A distributed framework for IoT system management that employs asset abstraction, automated asset binding, and machine learning to facilitate self-healing and self-optimization, allowing IoT applications to dynamically adapt to changes and maintain system resiliency with minimal human intervention, by treating devices as abstracted classes rather than specific entities and using global and local models to manage device performance and redeployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the number and diversity of connected devices increases, then the functionality and coverage of the IoT system improves, but the complexity of system management worsens
Solution Approach 1:
The patent segments the monolithic management system into distributed microservices that can be independently deployed and managed across multiple devices. Each microservice handles specific management functions, allowing the system to scale horizontally by adding more service instances rather than increasing the complexity of a single centralized system.
Solution Approach 2:
The patent creates universal management microservices that can handle multiple types of devices and functions through standardized interfaces and protocols. These microservices are designed to be device-agnostic, enabling them to manage diverse IoT devices without requiring device-specific management logic, thus maintaining low complexity while supporting high versatility.
2Reliability
If human intervention is increased to manage complex IoT systems, then system reliability improves, but the cost and time of deployment worsens
Solution Approach 1:
The patent implements self-service mechanisms where the management system automatically performs provisioning, configuration, monitoring, and troubleshooting of IoT devices without human intervention. The microservices autonomously detect device states, apply appropriate management actions, and adapt to changing conditions, thereby maintaining high reliability while eliminating deployment time losses.
Solution Approach 2:
The patent incorporates continuous feedback loops where microservices monitor device performance and system state, automatically adjusting management actions based on real-time data. This closed-loop control enables the system to self-correct and maintain reliability without human intervention, while the automated nature of feedback processing eliminates time delays associated with manual monitoring and response.
3Productivity
If incremental failures are allowed in large-scale deployments, then system scalability improves, but the risk of system-wide issues worsens
Solution Approach 1:
The patent segments the system into isolated microservices that can fail independently without causing system-wide cascades. Each microservice operates in an isolated container or process space, so failures are contained locally. This segmentation enables scalable deployment of numerous services while preventing any single failure from propagating across the entire system.
Solution Approach 2:
The patent implements beforehand cushioning through redundancy and failover mechanisms. Critical microservices are deployed in multiple instances with health monitoring, and automatic failover is configured in advance. When failures occur, pre-configured backup services immediately take over, cushioning the system against the impact of failures and preventing system-wide issues while maintaining scalability.
Data Source
AI summary
Data is received describing a local model of a first device generated by the first device based on sensor readings at the first device and a global model is updated that is hosted remote from the first device based on the local model and modeling devices in a plurality of different asset taxonomies. A particular operating state affecting one or more of a set of devices deployed in a particular machine-to-machine network is detected and the particular machine-to-machine network is automatically reconfigured based on the global model.