Context-Aware Wifi Offloading in Congested Public Areas

As mobile users increase their data consumption, especially in busy public areas, mobile networks often experience congestion and a decline in users' quality of service. Offloading to wifi: wifi networks have become a preferred method for mitigating offloading problems in cellular networks, as they shift some traffic to wifi access networks. Conventional offloading methods often overlook contextual information such as user movement, application type, network traffic, congestion, and signal strength, which can degrade performance and user experience. In this Paper, we present a context-aware wifi offloading framework that makes real-time offloading decisions based on environmental and user-specific contexts. Context parameters, such as user location, Wi-Fi access points, the sensitivity of the applications in use, and user movement patterns, enable the decision-making system to determine the most appropriate network for the data session. The focus of our work is refining a specific decision-making model that seamlessly integrates with components of uninterrupted user accessibility, system functionality, overall user satisfaction, and system engagement efficiency. Across the simulated and real-world congestion scenarios used in our analyses, the results show marked improvements and significant variances in latency and energy consumption compared to traditional approaches. Our methodological design is both expert- and scalable within the domains of network optimization, congestion alleviation in high-volume user areas or hotspots, effective movement of user networks, and overall enhancement of user satisfaction.

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