## 1 Introduction  
Expanding networks of Internet of Things (IoT) devices transform traditional environments into smart ecosystems in today's increasingly interconnected world. These systems, incorporating countless sensors and devices, continuously collect and transmit sensitive data across networks, enabling advanced functionalities in healthcare, smart cities, and industrial automation. However, the massive scale of IoT device deployments introduces substantial security challenges. According to the Ericsson Mobility Report [1], the number of IoT-connected devices is expected to rise sharply, driven by advancements in 5G/6G technologies and the increased adoption of new applications. This surge of devices highlights the need for robust security measures to protect sensitive data from unauthorized access. Ensuring secure data transfer in IoT systems is critical for maintaining security and enabling the full potential of these transformative technologies. In IoT networks, data transfer requires secure methods to protect sensitive information from breaches. Typical data transfer security approaches include encryption, fog computing, or software-defined networking (SDN), each applied to specific layers of the IoT system architecture [2]. Encryption is one of the most widely used methods, and it is applied primarily at the network and application layers. It ensures that data transmitted across IoT networks, if intercepted, are not readable. Symmetric and asymmetric encryption algorithms like Advanced Encryption Standard and RSA (Rivest-Shamir-Adleman) are used. However,the main drawback is the required computational overhead, which can be problematic for resource-constrained low-power IoT devices. In some cases, lightweight encryption protocols are used to balance the trade-off between security and efficiency [3; 4; 5]. Fog computing enhances IoT security by enabling real-time processing closer to the network edge, reducing latency, and allowing encryption, data filtering, and threat detection before forwarding data to centralized servers. This approach improves data transfer security, particularly in environments requiring rapid decision making; however, it also introduces complexities in managing distributed resources and ensuring security across multiple edge devices [3; 6; 7]. In addition, SDN provides a network architecture that allows centralized control of data traffic in IoT networks. By enabling network administrators to enforce security policies and dynamically adjust traffic flows in real time, SDN enhances network security and provides better defense against cyber-attacks. However, this centralized control also introduces a single point of failure, meaning that if the central controller is compromised, the entire network's security could be at risk [8]. While traditional IoT security relies heavily on encryption and network-layer protections [9], physical layer security (PLS) has emerged as a promising alternative for securing resource-constrained devices by leveraging the inherent properties of the communication medium and device-specific characteristics [10; 11; 12]. By exploiting the randomness and physical properties of the transmission channel, such as channel state information, PLS enhances security without relying on computationally intensive cryptographic operations, making it particularly suitable for IoT devices with limited processing power. Techniques such as artificial noise injection and radio frequency fingerprinting further strengthen security by disrupting eavesdroppers and authenticating devices based on unique hardware characteristics [13; 14]. Among the promising developments in PLS is the integration of chaos theory, which enhances security by leveraging the inherent unpredictability of chaotic signals. Chaos-based communication techniques offer a compelling solution for secure data transmission, particularly in IoT sensor networks, where devices have limited computational and power resources.