Pre-processing Framework for Distributed Intelligence in LLNs
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Solution Overview
Problem
Low Power and Lossy Networks (LLNs) face challenges such as lossy links, low bandwidth, and limited resources, making it difficult to implement effective routing, Quality of Service (QoS), security, network management, and traffic engineering, especially with the large number of nodes and changing conditions, where traditional approaches are inefficient and human data processing is impractical.
Innovation Solution
A pre-processing framework component for distributed intelligence architectures, including a State Tracking Engine (STE) and Metric Computation Engine (MCE), which defines classes of elements and network metrics, provides APIs for tracking and accessing network states and metrics, and enables customizable data gathering and processing, allowing for real-time tracking of key performance metrics and dynamic network control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional routing and network management approaches are used in LLNs, then network functionality can be provided, but the system becomes inefficient and unmanageable due to the large number of nodes and limited resources
Solution Approach 1:
The patent segments network management functions by introducing a hierarchical architecture with network controllers that manage subsets of nodes. This divides the complex task of managing thousands of nodes into manageable segments, where each controller handles a localized portion of the network, reducing overall system complexity while maintaining scalability.
Solution Approach 2:
The patent introduces network controllers as intermediary entities between the core network and individual nodes. These controllers act as mediators that aggregate information from multiple nodes and provide coordinated management, reducing the complexity burden on individual nodes and enabling more efficient network-wide management.
2Reliability
If more data is collected and processed to understand network behavior, then better network control decisions can be made, but bandwidth consumption and processing requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-defining a standardized set of network states and metrics that are universally tracked across the network. This pre-established framework allows nodes to collect and aggregate only the necessary pre-specified data, avoiding the need to gather all possible data and reducing bandwidth consumption while maintaining sufficient information for accurate network control decisions.
Solution Approach 2:
The patent merges data collection and aggregation functions into network controllers that consolidate information from multiple nodes. By combining data at intermediate aggregation points rather than transmitting every individual node's complete state, the system reduces total bandwidth consumption while preserving the essential information needed for network-wide control decisions.
3Adaptability or versatility
If static rules are used for network control, then implementation is simple, but the system cannot adapt to changing conditions and requirements
Solution Approach 1:
The patent implements feedback mechanisms where network controllers continuously monitor network states, compare them against desired conditions, and automatically adjust control decisions. This closed-loop feedback enables the system to adapt to changing conditions dynamically while using standardized control logic that prevents excessive complexity.
Solution Approach 2:
The patent introduces dynamic adaptability by allowing network controllers to adjust their behavior based on real-time network conditions and learned patterns. The system can dynamically modify routing decisions, resource allocation, and control parameters in response to changing requirements, while maintaining a structured control framework that manages complexity.
Data Source
AI summary
In one embodiment, a state tracking engine (STE) defines one or more classes of elements that can be tracked in a network. A set of elements to track is determined from the one or more classes, and the set of elements is tracked in the network. Access to the tracked set of elements then provided via one or more corresponding application programming interfaces (APIs). In another embodiment, a metric computation engine (MCE) defines one or more network metrics to be tracked in the network. One or more tracked elements are received from the STE. The one or more network metrics are tracked in the network based on the received one or more tracked elements. Access to the tracked network metrics is then provided via one or more corresponding APIs.