## 4 Results  
This section presents the simulation results of the proposed SEEM framework, detailing the system setup, implementation framework, evaluation metrics, comparative analysis with other state-of-the-art models, and evaluating its SEE performance in comparison to different optimization strategies within a multi-device, multi-eavesdropper CF m-MIMO IoT network. The simulation environment assumes a distributed deployment of all nodes within a \(100\times 100\) m area, where each node is randomly positioned within this region. To visually represent this deployment, we illustrate the topology of the simulated system, where \(K=20\) APs, \(I=7\) IoT devices, and \(J=5\) eavesdroppers are randomly placed, as depicted in Figure 5. For consistency, our simulations adopt the same large-scale fading coefficients and specific noise power values as those outlined in [48]. The transmission power for pilot signals is set at \(P_{p}=0.3\) W for IoT devices and \(P_{E}=0.1\) W for eavesdroppers, with the respective normalized SNRs \(\rho_{p}\) and \(\rho_{E}\) being computed as the ratio of pilot power to noise power. Furthermore, the internal energy consumption of each antenna is assigned a fixed value of \(P_{\text{tc},k}=0.2\) W, while the backhaul system incurs a fixed power consumption of \(P_{0,k}=0.825\) W per AP. Furthermore, traffic-dependent backhaul energy use is defined as \(P_{\text{bt},k}=0.25\) W/(Gbits/s), which accounts for dynamic data transmission rates [63]. The system also operates with a bandwidth of \(B=20\) MHz and utilizes \(\tau_{p}=30\) pilot sequences for CSI estimation. The power amplifier efficiency per AP is set to \(\alpha_{k}=2.5\). Finally, for iterative optimization-based evaluations, the maximum number of iterations is configured as \(t_{\text{max}}=10^{2}\) to ensure algorithmic convergence. The implementation was carried out in Python 3.8, leveraging TensorFlow and Keras for DL components, ensuring efficient training and inference of the CNN-LSTM architecture. The multi-objective optimization process was conducted using the distributed evolutionary algorithms in the Python (DEAP) library, which provides robust evolutionary computation techniques tailored for multi-objective problems. To ensure computational consistency and scalability, all experiments were executed on a high-performance computing cluster equipped with an Intel Xeon processor and 128 GB RAM, providing an optimized environment for deep learning and optimization-based simulations. To assess the performance of the proposed MOIBBO-CNN-LSTM model, a comparative analysis was conducted against five widely recognized benchmark models in the field of DL and optimization-driven frameworks. The first comparative model is NSGA-II-CNN-LSTM, which integrates the nondominated sorting genetic algorithm-II (NSGA-II) with a CNN-LSTM architecture. This model was chosen because NSGA-II represents one of the most well-established multi-objective evolutionary algorithms, enabling an alternative approach to optimizing deep neural network (DNN) architectures. Another model used in the comparison is vision transformer (ViT), a DL architecture based on the self-attention mechanism, which has demonstrated remarkable success in capturing complex spatial and sequential constraints. Given its ability to extract spatial and temporal features, ViT serves as a strong competitor to hybrid CNN-LSTM architectures. Figure 5: System setup of the CF m-MIMO IoT network with 20 APs, 7 IoT devices, and 5 eavesdroppers. The deployment area is scaled within a square of 100 m length for our simulations unless otherwise stated. Additionally, the deep reinforcement learning (DRL) model was included as a reference since RL techniques are commonly used for energy efficiency and security optimization in IoT networks. DRL-based models dynamically learn optimal policies over time, making them valuable in real-time IoT environments.