Remote Terminal Channel Access Using Radio Readiness Prediction
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Solution Overview
Problem
In remote SCADA and telemetry networks, existing access protocols face limitations in throughput and latency due to the inability of access points to predict which remote data radios are ready to transmit, leading to inefficient channel allocation and reduced performance under high traffic conditions.
Innovation Solution
Implementing reinforced machine learning on the access point to process header data and predict which data radios are ready to transmit and when, allowing for a centrally coordinated access protocol that efficiently allocates channel time to radios with data, thereby increasing throughput and reducing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If a random access protocol is used for channel access, then low latency is achieved under low traffic conditions, but throughput is limited to approximately 30% of channel capacity under high traffic conditions
Solution Approach 1:
The system performs preliminary actions by having remote devices send data availability indicators before actual data transmission. The access point uses these indicators to pre-coordinate channel access, predicting which devices will have data and when, thereby avoiding the need for random access attempts and reducing latency while maximizing throughput under high traffic conditions.
2Productivity
If a centrally coordinated access protocol is used to achieve near 100% channel capacity, then throughput is maximized, but the access point cannot identify which radios are ready to transmit without additional information
Solution Approach 1:
The communication process is segmented into two distinct phases: first, remote devices send brief data availability indicators (segment 1), and second, the access point coordinates data transmission based on this information (segment 2). This segmentation allows the system to maintain centralized coordination for maximum throughput while separately gathering the necessary information about which devices have data ready.
Data Source
AI summary
Methods and systems for managing remote terminal communications in remote Supervisory Control and Data Acquisition (SCADA) and telemetry networks. Reinforced machine learning processes header data of multiple messages received from remote data radios to predict which of the data radios are currently ready with a response to transmit based on or using information associated with a learned time delay. Time on shared wireless channels is efficiently allocated to the data radios that are currently ready with the response.