Vision in Versatility: Dual CCD-CMOS Imaging with Compressed Sensing for Sustainable IoT Surveillance Drones

Abstract

In the evolving IoT technology landscape, deploying surveillance drones with advanced imaging for security and efficiency is crucial, particularly in low-light conditions. Traditional imaging relies on either Charge-Coupled Device (CCD) sensors for their low-light prowess or Complementary Metal-Oxide-Semiconductor (CMOS) sensors for their energy efficiency. Our research introduces a novel approach by combining CCD and CMOS sensors into a single hardware platform. This allows for smart switching based on ambient light, optimizing energy use and improving image quality in varied environments. We tackle the high energy use of CCD sensors and the inconsistent performance of CMOS sensors under different lighting by applying compressed sensing (CS) techniques. These are designed to lower energy, bandwidth, and storage needs, making CCD sensors more efficient in the dark. Additionally, we've developed optimized sparse reconstruction algorithms to enhance this dual-sensor system's performance in IoT networks, ensuring high image quality with less resource use. This dual-sensor approach is a breakthrough in using CCD sensors for night surveillance, supporting sustainable IoT goals by saving energy and extending drone lifespans. Our research, backed by theoretical analysis, simulations, and empirical tests, proves our algorithms' ability to reconstruct high-quality images efficiently. Introducing this dual-sensor solution represents a significant step in sustainable IoT surveillance, offering potential for widespread use in various settings. © 2024 IEEE.