Chaotic Compressed Sensing for Secure Image Transmission in LoRa IoT Systems

Transmitting image data reliably over long distances with low cost and minimal storage consumption is critical for LoRa-enabled IoT devices. Conventional methods often rely on high-power consumption or computationally intensive hardware, rendering them unsuitable for cost-sensitive and resource-limited IoT deployments. This paper presents a hybrid compressed sensing approach designed for efficient image transmission in LoRa-based IoT systems. The proposed method utilizes a chaotic map-based sensing matrix to enhance randomness and incoherence in the sampling process, which also serves as an encryption key to secure the transmitted data. While wavelet transform is combined with Total Variation (TV) minimization to accurately recover high-quality images from the sparse measurements on the reconstruction side. The system is implemented on low-power development boards, with the ESP32-CAM used for image capture and initial compression, and the CubeCell-AB01 handling LoRa-based wireless transmission. Experimental results demonstrate significant reductions in data size and transmission cost, while preserving image fidelity and enhancing data security, making the proposed method well-suited for resource-constrained IoT applications.