Energy-Aware Persistent Storage Optimization for Mobile Embedded Edge IoT Platforms
: This paper presents the design and evaluation of a heterogeneous mobile edge node based on the STM32F103VET6 microcontroller, optimized for efficient data collection in Smart City vehicular environments. The challenge of high CPU overhead, energy consumption and latency caused by frequent write operations to mass storage via SPI is addressed through a temporary storage management architecture employing batching and double-buffering techniques with non-blocking DMA control. The system was evaluated using BLE, 802.15.4, and WiFi communication protocols under intermittent connectivity scenarios. Experimental results show that by using an optimal buffer size, the edge node reduces SPI bus usage for reading and writing data to the SD card from 38.0% to 3.1%. This significantly minimizes delays caused by frequent SD card access and also lowers the edge node’s energy consumption by 35.3%. Additionally, a 100% data recovery rate was verified during network intermittency. This research contributes to remote monitoring in smart cities using low-cost, low-power VANET networks, providing an efficient edge computing solution under strict storage and energy constraints.