Satellite Outage Prediction via Crowdsourced Data
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Solution Overview
Problem
Satellite networks face outages due to blockages, such as clouds or inoperative satellites, which can lead to service interruptions, hindering widespread adoption, especially in applications requiring high reliability.
Innovation Solution
A method is developed to predict outages using machine learning models trained with outage data, including meteorological and RF interference data, allowing for proactive rerouting of user data to terrestrial networks before outages occur, thereby preventing service disruptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If satellite networks are used to improve coverage and reduce latency, then service reliability deteriorates due to outages caused by cloud blockages and satellite failures
Solution Approach 1:
The system performs preliminary actions by predicting outages before they occur using machine learning models that analyze historical outage data, meteorological data, and satellite status. This advance prediction enables proactive rerouting of data traffic to terrestrial networks before satellite outages happen, preventing service interruptions rather than reacting to them after occurrence
Solution Approach 2:
The system dynamically changes network routing parameters based on predicted outage conditions. When outages are predicted, the system automatically switches data transmission from satellite network paths to terrestrial network paths, and vice versa when outages are not predicted, thereby adapting network behavior to anticipated conditions
2Reliability
If data is rerouted to terrestrial networks to prevent service interruptions, then network complexity increases due to multi-network management
Solution Approach 1:
The system introduces an intermediary outage prediction system that automatically monitors, predicts, and manages routing decisions between satellite and terrestrial networks. This intermediary layer handles the complexity of multi-network management by centralizing prediction logic and automated routing control, shielding end users and network operators from the underlying complexity while ensuring service continuity
Solution Approach 2:
The system implements feedback mechanisms where actual outage occurrences are fed back into the machine learning models to continuously improve prediction accuracy. The system also monitors routing effectiveness and adjusts prediction parameters based on observed performance, creating a self-optimizing system that manages complexity through learned patterns rather than rigid rules
Data Source
AI summary
This disclosure describes techniques for predicting and accommodating for outages in a satellite network using crowdsourced data. An example method includes receiving outage data indicating first outages experienced by first endpoints in a first geographical region. The first outages, for instance, include interruptions in communication between first satellites and the first endpoints. The example method further includes predicting, based on the outage data, a second outage comprising an interruption in communication between at least one second satellite and a second endpoint in a second geographical region. Further, the example method includes causing the second endpoint to transmit user data over a secondary network in advance of the second outage.