Adaptive network switching algorithm for sensor data transfer: a simulation framework integrating GSM, Wi-Fi, and LoRa networks

The growing deployment of IoT devices necessitates reliable sensor data transfer under diverse network conditions. This study introduces an adaptive network switching framework to enhance data dependability across GSM, Wi-Fi, and LoRa networks. By leveraging delay tolerant networking (DTN) principles and real-time performance metrics, the algorithm dynamically selects the optimal channel for transmitting sensor data—text, audio, image, and video—across urban, suburban, and rural settings. Simulations demonstrate an average 33% improvement in throughput, 24% reduction in latency, and 45% decrease in packet loss when using the adaptive framework compared to standalone networks. The adaptability score averaged 0.7 in rural scenarios, with peak performance scores reaching 1,000 for video data at night. A beehive monitoring case study validates these results in real-world conditions. This work contributes a robust, adaptable solution for sensor data optimization in IoT applications.