Network Gateway Predictive Traffic Prioritization
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Solution Overview
Problem
Wireless networking service providers face challenges in maintaining consistent quality of service due to varying coverage areas and network outages, leading to service disruptions as users move between coverage areas or when networking components go online or offline.
Innovation Solution
A system and method that predictively prioritize data traffic and route selection to avoid areas of poor coverage by using cell coverage data, location, speed, and direction of devices, allowing for seamless switching between wireless networks to maintain connectivity and quality of service.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users move between coverage areas or network components go online/offline, then network connectivity changes, but quality of service deteriorates below acceptable threshold
Solution Approach 1:
The system performs preliminary actions by predicting future coverage areas and proactively switching networks before quality of service deteriorates. The network gateway receives location data, predicts upcoming coverage changes, and initiates network switching procedures in advance, ensuring continuous high-quality service without waiting for degradation to occur.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual quality of service metrics and comparing them against acceptable thresholds. When degradation is detected or predicted, the system adjusts routing decisions and network selection accordingly, creating a closed-loop control system that adapts to real-time network conditions.
2Reliability
If the system proactively predicts and switches networks to maintain quality, then service reliability improves, but system complexity increases
Solution Approach 1:
The network gateway performs self-service by autonomously making routing decisions based on predicted coverage changes. It independently evaluates network conditions, selects optimal paths, and executes switching operations without requiring continuous manual intervention or complex centralized control, thereby maintaining reliability while managing complexity through automation.
Solution Approach 2:
The system manages complexity by dynamically changing operational parameters such as routing paths, network selection criteria, and switching timing based on predicted conditions. Rather than maintaining a fixed complex management architecture, the system adapts its parameters in response to predicted network changes, simplifying the overall control logic.
3Loss of time
If the system waits for quality degradation before switching networks, then response time is reduced, but service disruption occurs
Solution Approach 1:
The system performs preliminary network switching actions based on predicted coverage changes before quality degradation actually occurs. By using location data and network information to forecast future conditions, the system proactively initiates switching procedures that complete before service disruption would happen, eliminating the trade-off between rapid response and continuous service.
Data Source
AI summary
A core provider predicts that a device will enter an area of poor coverage by a wireless network provider. Based on the prediction, one or more rules are applied to prioritize data traffic to be received prior to entering the area of poor coverage. The prediction that the device will enter the area of poor coverage may be based on cell coverage data received from the wireless network provider, connection quality data received from other devices, a location of the device, a speed of the device, a direction of the device, or any suitable combination thereof. A route of the device may be changed to avoid or minimize an amount of time in the area of poor coverage. As another alternative, the device may be switched from the wireless network provider to another wireless network with better coverage.