Child-Initiated DAG Topology Optimization for Future Time Instances
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Solution Overview
Problem
Conventional approaches to generating a Directed Acyclic Graph (DAG) topology in Low-power and Lossy Networks (LLN) do not allow for dynamic optimization of network resources based on future needs, as they rely on objective functions specified by the root device during initial network formation, failing to reserve resources for future events effectively.
Innovation Solution
A child network device can request the generation of an optimized tree-based topology for a future time instance using a child-selected objective function, which can be distinct from the original objective function, allowing for proactive optimization and resource allocation tailored to specific future requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the root device specifies the objective function during initial network formation, then the DAG topology is established with a fixed routing structure, but the network cannot dynamically optimize resources for future needs
Solution Approach 1:
The patent enables child devices to proactively request optimized topologies for future time instances before those instances arrive. The root device computes and reserves optimized DAG topologies in advance based on predicted future conditions (energy availability, traffic patterns, network disruptions), allowing the network to adapt to future requirements without reacting to them when they occur.
Solution Approach 2:
The patent introduces dynamic topology optimization by allowing the objective function to change over time. Different objective functions can be applied for different future time instances (e.g., minimizing energy consumption during low-traffic periods, prioritizing reliability during high-traffic periods). The system dynamically selects and applies appropriate objective functions based on predicted future network conditions and device states.
2Productivity
If the network uses a fixed objective function from network formation, then implementation is simple and consistent, but resource allocation cannot be tailored to specific future requirements
Solution Approach 1:
The patent changes the parameters of the objective function over time. The root device maintains multiple objective functions with different parameters (weightings for latency, energy, hop count, reliability) and selects appropriate parameters for future time instances based on predicted conditions. For example, during energy-constrained periods, the objective function parameters prioritize energy efficiency; during critical periods, parameters prioritize reliability or low latency.
Solution Approach 2:
The system performs preliminary computation of optimized topologies for future time instances. The root device receives requests from child devices specifying future time instances and computes the appropriate objective function parameters and optimized DAG topology in advance, allowing efficient resource allocation when the future time instance arrives without requiring complex real-time decisions.
3Use of energy by moving object
If the network topology is optimized for future time instances, then energy can be conserved and disruptions handled dynamically, but processing power and memory requirements increase
Solution Approach 1:
The patent enables child devices to self-initiate optimization requests for their future needs. Instead of the root device continuously computing and pushing optimizations to all devices, child devices autonomously determine when they need optimized topologies for future instances and request them. This self-service approach reduces unnecessary processing at the root device and allows optimizations to be computed only when actually needed by specific devices.
Solution Approach 2:
The system performs topology optimization computations in advance of when they are needed, during periods when network conditions are favorable and processing resources are available. By computing optimized topologies before future time instances arrive, the system avoids the need for complex real-time processing when actual network operations are critical, thereby reducing peak processing power requirements while still achieving energy efficiency and disruption handling benefits.
Data Source
AI summary
In one embodiment, a method comprises receiving, by a network device within a tree-based topology rooted by a root network device, a request message from a child network device for generating an optimized tree-based topology for a future use by the child network device at a future time instance; the network device executing an objective function for generating the optimized tree-based topology for the future use by the child network device; and the network device providing network communications, for the child network device, at the future time instance using the optimized tree-based topology.