## 2 System Model and Problem Formulation
Next, we formulate the SEE maximization problem under constraints on available transmission power and QoS requirements. SEE here serves as a unified performance metric that captures the trade-off between secure data transmission and energy consumption, which is especially critical in power-constrained IoT systems. In the context of CF m-MIMO IoT networks, SEE is defined as the ratio of the sum secrecy rate to the total power consumption across all access points and devices. A higher SEE value indicates that more confidential information is successfully transmitted per unit of energy consumed. Unlike traditional metrics that consider only energy efficiency or spectral efficiency in isolation, SEE integrates security into the resource allocation framework by incorporating the difference between the achievable rate at the legitimate receiver and the maximum rate achievable by potential eavesdroppers. This allows the system to not only optimize power usage but also actively protect against information leakage. Therefore, optimizing SEE ensures that the network uses energy in a way that maximizes confidentiality while maintaining efficiency, a key requirement for the secure deployment of large-scale IoT environments [42; 43; 44; 45]. Thus, to efficiently solve this optimization problem, we develop a hybrid DL framework that integrates CNN and LSTM networks for joint EE and security optimization. CNN is utilized for spatial feature extraction, while LSTM captures temporal dependencies, enabling more effective modeling of dynamic IoT communication patterns. Additionally, to improve the training efficiency and performance of the CNN's fully connected layers, we employ the MOIBBO algorithm for hyperparameter optimization, given its effectiveness in handling multi-objective problems with improved convergence speed. Therefore, the primary objective of this work is to enhance the SEE of the CF m-MIMO-based IoT system through optimized power allocation while satisfying the following constraints:  
* The maximum normalized transmission power of each AP \(A_{k}\) is constrained by \(\zeta\). Thus, the power control factors must satisfy: \[\mathbf{\hat{H}}_{kE_{j}}=\frac{\sqrt{\rho_{E}}\Gamma_{kE_{j}}}{\sqrt{\rho_{D} \Gamma_{kD_{i}}}}\mathbf{\hat{H}}_{kD_{i}}. \tag{9}\]1 (19)
* To ensure the QoS requirements of each IoT device \(D_{i}\), the achievable secrecy rate must exceed a predefined threshold: \[\mathbf{\hat{H}}_{kE_{j}}=\frac{\sqrt{\rho_{E}}\Gamma_{kE_{j}}}{\sqrt{\rho_{D} \Gamma_{kD_{i}}}}\mathbf{\hat{H}}_{kD_{i}}. \tag{9}\]2 (20)
* To limit the wiretapping capability of any eavesdropper \(E_{j}\), its achievable rate for wiretapping device \(D_{i}\) is restricted as follows: \[\mathbf{\hat{H}}_{kE_{j}}=\frac{\sqrt{\rho_{E}}\Gamma_{kE_{j}}}{\sqrt{\rho_{D} \Gamma_{kD_{i}}}}\mathbf{\hat{H}}_{kD_{i}}. \tag{9}\]3 (21)  
With these constraints, the SEE maximization problem is expressed as follows:  
\[\mathbf{\hat{H}}_{kE_{j}}=\frac{\sqrt{\rho_{E}}\Gamma_{kE_{j}}}{\sqrt{\rho_{D} \Gamma_{kD_{i}}}}\mathbf{\hat{H}}_{kD_{i}}. \tag{9}\]4 (22) s.t. \[\mathbf{\hat{H}}_{kE_{j}}=\frac{\sqrt{\rho_{E}}\Gamma_{kE_{j}}}{\sqrt{\rho_{D} \Gamma_{kD_{i}}}}\mathbf{\hat{H}}_{kD_{i}}. \tag{9}\]5 \[\mathbf{\hat{H}}_{kE_{j}}=\frac{\sqrt{\rho_{E}}\Gamma_{kE_{j}}}{\sqrt{\rho_{D} \Gamma_{kD_{i}}}}\mathbf{\hat{H}}_{kD_{i}}. \tag{9}\]6 (24) \[\mathbf{\hat{H}}_{kE_{j}}=\frac{\sqrt{\rho_{E}}\Gamma_{kE_{j}}}{\sqrt{\rho_{D} \Gamma_{kD_{i}}}}\mathbf{\hat{H}}_{kD_{i}}. \tag{9}\]7 (25) \[\mathbf{\hat{H}}_{kE_{j}}=\frac{\sqrt{\rho_{E}}\Gamma_{kE_{j}}}{\sqrt{\rho_{D} \Gamma_{kD_{i}}}}\mathbf{\hat{H}}_{kD_{i}}. \tag{9}\]8  
In this work, QoS-aware communication signifies the system's capability to sustain a satisfactory SR. Consequently, our primary goal in optimizing SEE is to enhance SR while adhering to a predefined energy constraint.