Mobile QoS Optimizer Predicting Service Degradation
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Solution Overview
Problem
Wireless service providers face challenges in maintaining high quality of service for mobile devices due to perceived delays and changes in network availability, which can lead to customer dissatisfaction and retention issues.
Innovation Solution
The system predicts future quality of service for mobile devices based on their location and network availability, allowing for proactive actions such as caching content, degrading data quality, or warning users of impending service issues, by analyzing individual and aggregate user data and network conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system proactively caches content and degrades data quality before service degradation occurs, then quality of service is maintained, but system complexity and computational resources increase
Solution Approach 1:
The system performs preliminary actions by predicting future quality of service conditions based on mobile device location and network availability data. Before service degradation occurs, the system proactively caches content and degrades data quality in advance, preventing the harmful effect of service interruption while maintaining reliability during predicted poor service conditions
Solution Approach 2:
The system applies beforehand cushioning by preparing mitigation strategies in advance of predicted service degradation. Content is cached and data quality is degraded before the actual service issue occurs, creating a buffer that protects users from experiencing service interruptions or significant quality drops
2Reliability
If real-time network monitoring and prediction algorithms are implemented, then service quality is improved, but energy consumption and processing power increase
Solution Approach 1:
The system uses prediction algorithms to determine future quality of service conditions based on mobile device location and historical network availability data. By performing the analysis in advance rather than in real-time during service degradation, the system reduces peak energy consumption while maintaining the ability to proactively mitigate service issues
Solution Approach 2:
The system leverages existing mobile device location data and network availability information that is already being collected for other purposes. By reusing this existing data infrastructure, the system avoids duplicating energy-intensive monitoring functions while still achieving accurate quality of service predictions
Data Source
AI summary
A method, system, and medium are provided for improving communication between a mobile device and a wireless network in embodiment of the invention. Based in part on expected locations for a mobile device and network availability, predictions can be made regarding the future quality of service available for the mobile device. This prediction can allow actions to be taken to mitigate any change in the quality of service.