Abstract

As smart home devices and applications become more common, maintaining strong and dependable internet performance enables their seamless use. However, the conventional approach to network performance monitoring does not capture the user experience in real time, which leads to user dissatisfaction and inefficient detection of performance issues. This paper describes an internet performance monitoring system tailored for smart homes that is focused on user experience. It implements a mixture of active and passive measurement techniques with behavioral context modeling to strategically deploy light sensor agents that gather fine-grained QoS and QoE data, including but not limited to latency, jitter, packet loss, throughput, and user ratings over time and across multiple applications. The system also uses machine learning to track network performance metrics with user satisfaction to enable realtime performance assessment and automated adaptive optimization recommendations. Tested in simulated and real smart homes, the system demonstrates improved detection of micro-disruption, improved device-specific problem detection, and improved estimation of quality of experience. The paper illustrates the context-aware evaluation of internet performance. It emphasizes smart home networks designed to be more adaptive and user-centered, responding not only to technical measures but also to user evaluation. The model suggested here boosts performance transparency for end-users and allows service providers to deliver proactive and tailored support.