TSCH Scheduling Algorithm for Non-Deterministic Traffic Energy
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Solution Overview
Problem
Existing solutions for scheduling in IEEE 802.15.4e Time Slotted Channel Hopping (TSCH) networks are inefficient for non-deterministic traffic, failing to balance latency, reliability, and energy efficiency, particularly in industrial applications with unpredictable packet intervals.
Innovation Solution
An energy-efficient scheduling method and algorithm that dynamically allocates shared cells based on instantaneous measurements and traffic intensity, using a distributed computation model to optimize network configuration and minimize energy consumption while meeting latency and reliability constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dedicated resources are allocated to leaf nodes based on traffic intensity, then reliability is improved, but energy efficiency deteriorates for non-deterministic traffic
Solution Approach 1:
The patent implements dynamic resource allocation where the coordinator adjusts cell assignments based on instantaneous traffic measurements rather than static pre-allocated resources. The system transitions from a fixed scheduling approach to a dynamic one that adapts to actual traffic conditions, allowing resources to be reallocated based on current network state and traffic intensity patterns.
Solution Approach 2:
The system changes key parameters including traffic intensity thresholds, cell assignment configurations, and back-off parameters based on measured network conditions. By monitoring packet transmission success rates and traffic patterns, the coordinator dynamically adjusts these parameters to optimize both reliability and energy efficiency for non-deterministic traffic patterns.
2Use of energy by moving object
If back-off parameters are improved, then energy efficiency is partially increased, but productivity deteriorates due to limited efficiency gains
Solution Approach 1:
The coordinator performs preliminary measurements of traffic intensity and network conditions before making scheduling decisions. By gathering instantaneous measurements and predicting future traffic patterns, the system proactively configures optimal cell assignments and back-off parameters in advance, avoiding reactive adjustments that would reduce throughput.
Solution Approach 2:
The system implements a feedback mechanism where the coordinator continuously monitors transmission success rates, traffic patterns, and network conditions. This feedback is used to dynamically adjust scheduling decisions, cell assignments, and back-off parameters, creating a closed-loop control system that simultaneously optimizes energy efficiency and maintains high data transmission productivity.
3Adaptability or versatility
If distributed computation is used for scheduling, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent divides the scheduling functionality into two segments: a centralized coordinator that performs complex measurements, predictions, and optimization calculations, and distributed leaf nodes that execute simplified transmission protocols. This segmentation allows distributed adaptability while concentrating computational complexity in the coordinator, reducing the processing burden on individual network devices.
Solution Approach 2:
The coordinator acts as an intermediary between the central control plane and distributed leaf nodes. It collects instantaneous measurements from the network, performs complex computations for optimal scheduling decisions, and translates these into simplified configuration parameters that leaf nodes can easily implement. This intermediary role enables distributed adaptability without requiring high computational complexity at the edge devices.
Data Source
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AI summary
The invention is related to a network device and method that makes a schedule in the most efficient way and works in accordance with the IEEE 802.15.4e TSCH operation mode, which provides for fulfillment of the average latency and reliability constraints while minimizing the energy consumption for the network traffic in which the packets are sent in unpredictable and sporadic intervals.