## 1 Introduction
### Research Gaps and Motivations  
While CF m-MIMO has demonstrated significant improvements in QoS and connectivity in IoT networks, existing schemes often lack sufficient EE approaches. Most CF m-MIMO frameworks enhance performance by increasing the number of antennas in IoT APs. However, this comes at the cost of higher power consumption, which is particularly concerning for battery-operated IoT devices. As the number of antennas grows, the overall energy expenditure of the network also increases, creating a trade-off between EE and communication performance. Without proper EE optimization, the large-scale adoption of CF m-MIMO for IoT remains impractical due to its growing power demands.  
In addition to energy concerns, security vulnerabilities in CF m-MIMO-based IoT networks present another major challenge. The broadcast nature of wireless communications makes legitimate transmissions susceptible to eavesdropping attacks, where malicious entities attempt to intercept confidential data. Security has, therefore, become an unavoidable issue in wireless communication systems, requiring advanced protection mechanisms. In the past decade, PLS has gained significant attention as a complement to traditional cryptographic encryption techniques. PLS leverages signal processing and transmission design to secure data at the physical level, reducing the dependency on computationally expensive encryption methods. Interestingly, the increase in the number of antennas in CF m-MIMO networks brings more spatial diversity gain, which has the potential to enhance PLS performance by improving secure beamforming and jamming techniques. However, despite these advantages, the existing literature has not yet fully explored secure CF m-MIMO IoT architectures, leaving security concerns largely unaddressed. To comprehensively evaluate both PLS and EE in CF m-MIMO IoT networks, this paper adopts secrecy energy efficiency (SEE) [40; 41] as the key performance metric. SEE is defined as the ratio of the sum secrecy rate (SR) to total power consumption, providing a unified measure of both secure communication and energy efficiency [42; 43; 44; 45]. Taking advantage of SEE, our objective is to develop an optimization framework that jointly improves security and EE, ensuring the feasibility of CF m-MIMO IoT deployments in various practical settings. To clearly highlight the positioning of our work within the existing literature, Table 1 presents a comparative analysis of recent studies based on different metrics for designing secure and energy-efficient CF m-MIMO IoT systems, where EE represents optimization for energy efficiency, SE represents an enhancement of spectral efficiency through CF m-MIMO strategies or optimization, AB represents explicit consideration of antenna scaling and its impact on energy consumption in CF m-MIMO, PLS represents the incorporation of physical layer security techniques for secure transmission in CF m-MIMO IoT, SEE represents use of secrecy energy efficiency as a unified performance metric balancing power and security, and TO represents use of adaptive, multi-objective optimization methods to balance competing goals (e.g., security vs. efficiency).