Comprehensive Performance Evaluation of Wireless Sensor Networks Using Statistical Modeling and Visual Analytics

Wireless Sensor Networks (WSNs) are very crucial for making smart, connected systems that are needed for real-time tracking applications including healthcare surveillance, industrial automation, and environmental monitoring. Network speed has a big effect on how reliable certain apps are. This study thoroughly evaluates wireless sensor networks (WSNs) through statistical modelling and visual analytics applied to a dataset including 1,000 observations. Latency, packet loss ratio, energy consumption, network lifetime, node density, and packet loss indicator (RSSI) are the main indicators for QoS and energy. Statistical analysis shows that network performance is stable because the average end-to-end latency is$\mathbf{1 2 0. 3 8 ~ m s}$, the average volatility is$\mathbf{1 5. 3 5}$ms, and the packet loss rate is 2.73 %. A −65.39 dBm RSSI and an average throughput of 449.50 kbps show that data is sent reliably across different levels of network density. Energy-efficient practices are very important for making cities and communities more sustainable since energy use has a big effect on the network's lifespan, which is an average of 1193.35 hours. Visual analytics can show how node density, QoS parameters, and performance trends are related to each other. The results show how dependability, energy efficiency, and scalability are all affected. The framework enhances healthcare monitoring systems by providing dependable real-time medical surveillance, hence establish good health and well-being.