## 1 Introduction
### Related Works  
The rapid growth of IoT has led to significant research in CF m-MIMO to enhance network scalability, energy efficiency (EE), and security. One of the primary challenges in CF m-MIMO IoT systems is ensuring efficient power usage while maintaining high QoS. The authors in [28] analyzed a wirelessly powered IoT system using CF m-MIMO, where IoT sensors harvest energy from distributed APs during the downlink phase and use the power harvested for uplink transmission. Their optimization strategy minimized total transmit energy while meeting signal-to-interference-plus-noise ratio (SINR) constraints, demonstrating that CF IoT significantly outperforms collocated m-MIMO and small-cell IoT in energy efficiency. Similarly, [PERSON] et al. [29] investigated CF m-MIMO in low-power IoT networks and proposed an energy-efficient power control scheme that significantly reduces the transmission power of IoT devices while maintaining connectivity. Their work demonstrated power savings of 90% but highlighted challenges in balancing spectral efficiency (SE) and power consumption. [PERSON] et al. [30] extended this work by developing a scalable CF m-MIMO IoT system that utilizes optimal power control strategies and neural network-based solutions to improve EE. Their results indicated multifold EE improvements, but security considerations were not explicitly addressed. Despite advances in EE, security remains a critical challenge in CF m-MIMO IoT due to the broadcast nature of wireless transmissions. Unauthorized eavesdropping poses a severe threat to confidential communications. [PERSON] et al. [31] explored a non-orthogonal multiple access (NOMA)-based CF m-MIMO IoT system and derived closed-form SE and EE expressions under pilot contamination and interference conditions. However, while optimizing power control for NOMA users, their work did not incorporate physical layer security (PLS) techniques for the enhancement of secrecy rate. Similarly, [PERSON] et al. [32] examined pilot contamination in CF m-MIMO IoT, an issue exacerbated by the inability to assign orthogonal pilots to a massive number of IoT devices. They proposed an optimal linear minimum mean-square-error (LMMSE) channel estimation method to mitigate interference and improve uplink throughput, indirectly improving security but without explicitly considering PLS. Beyond EE and security, recent studies have focused on improving network reliability and localization in CF m-MIMO IoT. [PERSON] et al. [33] proposed a fingerprint-based channel estimation and localization framework where location awareness was leveraged to enhance channel estimation accuracy. Their framework introduced a two-phase localization approach and a pilot reassignment scheme to improve positioning accuracy and channel quality. Similarly, [PERSON] et al. [34] introduced a RIS-assisted CF m-MIMO framework for IoT networks, optimizing power control, precoding, and RIS phase shifts to improve the sum rate (SR) and EE. Their work demonstrated that RIS could significantly enhance CF m-MIMO performance, although security issues were not addressed. [PERSON] et al. [35] extended this by focusing on user-centric CF m-MIMO in highly dynamic IoT environments. They studied the effects of imperfect channel state information (CSI), channel aging, and non-line-of-sight (NLoS) conditions, proposing a soft handover scheme to enhance mobility support. Their findings emphasized the importance of preconfigured pilot overhead and CSI estimation in mitigating performance degradation. [PERSON] et al. [36] investigated CF m-MIMO for indoor factory environments in industrial IoT, analyzing the effects of centralized and distributed AP cooperation on spectral efficiency. Their proposed AP selection and pilot assignment schemes reduced pilot contamination while maintaining connectivity in highly dense deployments. [PERSON] et al.