Cognitive Edge Network for Autonomous IoT Task Coordination
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Solution Overview
Problem
Current Industrial IoT systems lack the capability for complete automation of IoT-enabled machines to operate autonomously, requiring human intervention for task assignment and knowledge of device ontology, real-time states, and capabilities, which is inefficient and prone to productivity disruptions.
Innovation Solution
A processor-implemented method and system for creating an autonomous context-aware state-exchanging cognitive edge network that receives and models state information from IoT nodes, forms hierarchical groups for task completion, updates state machines, and autonomously selects new tasks based on context, using certifying and asset nodes with varying trust groups and identity management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If centralized scheduling and human intervention are used for task assignment, then system control and coordination are maintained, but automation capability and productivity are reduced
Solution Approach 1:
The patent implements self-service through autonomous agents that automatically perform task assignment, scheduling, and coordination without human intervention. Each IoT device is equipped with an agent that can independently negotiate tasks, manage resources, and coordinate with other agents, enabling the system to serve itself and eliminating the need for centralized human control.
Solution Approach 2:
The system segments the centralized control function into distributed autonomous agents deployed at each IoT device. Instead of one central controller making all decisions, each agent handles local decision-making and task management, dividing the complex coordination problem into smaller, manageable units that operate independently but cooperatively.
2Loss of information
If complete state information is collected and modeled for all IoT nodes, then context awareness and autonomous decision-making are enhanced, but data processing overhead and system resource consumption increase
Solution Approach 1:
The patent applies local quality by creating context information models that are specific to each IoT device's local environment, capabilities, and task requirements. Instead of uniformly collecting and processing all possible state information from every device, the system tailors the information collection and modeling to what is locally relevant, reducing unnecessary data processing while maintaining complete context awareness where needed.
3Productivity
If hierarchical group structures are formed for task completion, then task coordination and resource allocation efficiency are improved, but network complexity and group management overhead increase
Solution Approach 1:
The patent implements dynamic hierarchical groups where the structure is not fixed but adapts based on task requirements, device availability, and resource conditions. Groups can be formed, modified, or dissolved dynamically as tasks are assigned and completed, allowing the system to optimize for productivity while managing complexity through flexibility rather than rigid predefined structures.
Data Source
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AI summary
This disclosure relates to a distributed state exchanging model for assets of an organization or entity for an identified task in a domain of interest. The model provides a context aware autonomous Internet of things (IoT) based cognitive edge network, wherein each node is aware of the state of the other nodes based on a subscription. The state information is seamlessly updated and maintained by a certifying node of each group. The nodes are grouped based on associated capability definitions such that at least some of the groups form a hierarchy within the cognitive edge network for completing the identified task. Since the network is context aware, a new sub-task towards completing the identified task is autonomously selected by either groups or by the nodes based on the context thereby obviating a need for centralized scheduling.