Wireless Node Propagation Time Estimation via Predictive Models
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Solution Overview
Problem
Current data networks face inefficiencies due to high volumes of low-bandwidth devices competing for limited network resources, resulting in a low useful-to-useless data ratio, particularly in uncoordinated environments like GPS tracking, which hampers the commercial viability of large-scale sensor network deployments.
Innovation Solution
A method and system for dynamically estimating the propagation time between nodes in a wireless network by receiving timestamps, calculating time differences, and using predictive models to estimate and adaptively manage propagation times, thereby optimizing resource allocation and reducing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If devices communicate in an uncoordinated environment, then device deployment flexibility is improved, but network efficiency deteriorates due to low useful-to-useless data ratio
Solution Approach 1:
The system dynamically adjusts communication parameters including propagation time estimates and virtual preamble usage based on real-time network conditions. Nodes continuously update their transmission timing and resource allocation to adapt to changing network states, transforming static uncoordinated communication into dynamic coordinated communication that maintains flexibility while improving efficiency
Solution Approach 2:
The system implements feedback mechanisms where nodes exchange timing information and propagation delay measurements. Second nodes send feedback packets containing time difference measurements to first nodes, enabling continuous refinement of propagation time estimates and coordinated adjustment of transmission timing across the network
2Device complexity
If propagation time is not accurately estimated, then system complexity is reduced, but latency increases and network performance deteriorates
Solution Approach 1:
Nodes autonomously estimate their own propagation times by measuring time differences between transmitted and received packets. Each node independently calculates propagation delay using local timestamps and received timestamps, eliminating the need for centralized timing coordination while reducing latency through localized adaptive timing adjustments
Solution Approach 2:
The system pre-calculates and stores propagation time estimates before actual data transmission. Nodes use these pre-computed estimates to proactively adjust their transmission timing, allowing them to compensate for propagation delays before communication occurs, thereby reducing overall latency
3Ease of operation
If network resources are allocated without coordination, then resource allocation simplicity is improved, but resource utilization efficiency deteriorates
Solution Approach 1:
The system dynamically allocates virtual preambles and transmission resources based on real-time propagation time estimates and network conditions. Nodes adjust their resource usage patterns continuously, transforming static simple allocation into dynamic efficient allocation that maintains operational simplicity while improving resource utilization
Solution Approach 2:
The system maintains continuous propagation time estimation and timing synchronization across all nodes. This continuous coordination ensures that resource allocation remains optimized over time, preventing waste from outdated timing information while maintaining the simplicity of automatic resource assignment
Data Source
AI summary
Apparatuses, methods, and systems for dynamically estimating a propagation time between a first node and a second node of a wireless network are disclosed. One method includes receiving, by the second node, from the first node a packet containing a first timestamp representing the transmit time of the packet, receiving, by the second node, from a local time source, a second timestamp corresponding with a time of reception of the first timestamp received from the first node, calculating a time difference between the first timestamp and the second timestamp, storing the time difference between the first timestamp and the second timestamp, calculating a predictive model for predicting the propagation time based the time difference between the first timestamp and the second timestamp, and estimating the propagation time between the first node and the second node at a time by querying the predictive model with the time.