Steganography in IoT: A Comprehensive Survey on Approaches, Challenges, and Future Directions

The Internet of Things (IoT) has raised significant security concerns, especially with regard to secure data transfer among resource-constrained devices. While effective, traditional encryption techniques are often computationally expensive and easily identifiable, making them unsuitable for many IoT applications. Steganography is an intriguing approach that allows hiding sensitive information within seemingly ordinary data, preventing unauthorized parties from detecting and accessing it. Existing studies on IoT security lack a comprehensive analysis of steganographic techniques tailored for IoT-specific constraints. This study bridges this gap by providing a comprehensive review of steganographic methods for IoT, exploring approaches across many domains such as spatial, frequency, and hybrid methods used in images, videos, audio, text, network traffic, hybrid approaches, and quantum-based methods. We evaluated and validated the techniques employed using key metrics such as imperceptibility, robustness, and embedding capacity while emphasizing the computational restrictions, real-time processing requirements of IoT devices, and the vital requirement for energy-efficient algorithms. This survey also investigates the integration of steganography and cryptographic approaches, as well as advances in Machine Learning (ML), Deep Learning (DL), and quantum techniques that could revolutionize the field. The study ends with future research directions that underscore the importance of innovative steganographic techniques that strike a compromise between security, efficiency, and scalability in IoT applications.