A Framework for Adaptive Adjustment in BLE-Based Low-Power IoT Vision

Bluetooth-low-energy (BLE)-based low-power cameras have expanded the applications of battery-powered low-power Internet of Things (IoT) vision systems. The adaptive tuning of connection and image encoding settings plays a vital role in optimizing the efficiency of wireless vision systems. This article introduces a framework for adaptive adjustment in BLE-based low-power vision systems. To reduce power consumption, we model BLE power usage and design a dynamic BLE parameters and resolution adjustment method for low-power IoT vision system. Additionally, we design an edge-end combined approach that delegates the task of key region prediction to the receiving device to achieve adaptive intraframe JPEG encoding. Our approach leverages commercially available BLE devices without requiring physical layer modifications, ensuring compatibility with standard devices. We design a wireless hardware prototype using off-the-shelf CMOS sensors and BLE Systems on a Chip to evaluate our framework. Extensive experiments demonstrate a 66.9% increase in energy efficiency compared to traditional fixed parameter methods.