Modeling and analysis of LoRa-enabled task offloading in edge computing for enhanced battery life in wearable devices

Modeling and analysis of LoRa-enabled task offloading in edge computing for enhanced battery life in wearable devices

연구 분야: Networking

학회: Cluster Computing

As wearable technologies, especially those equipped with augmented reality (AR) capabilities, become increasingly prevalent, their computational demands surge, straining the limited battery capacities. This study investigates the potential of task offloading-shifting computationally intensive tasks from the wearable device to more powerful edge computing resources-as a viable solution to extend battery life while maintaining performance. This paper examines a two-tier edge architecture improved by LoRa technology, focusing on optimizing communication between wearable devices and offloading destinations. This architecture leverages LoRa’s low power consumption and wide range connectivity to improve wearable devices’ performance and efficiency in edge computing environments. Our simulation results show the effectiveness of our approach, with notable gains in computational efficiency and energy consumption when tasks are offloaded. The study specifically highlights the benefits of switching between LoRaWAN modes in various operational scenarios, emphasizing the trade-offs between data transmission rate, range, and power consumption.