# A Distributed Energy-Throughput Efficient Cross-Layer Framework Using Hybrid Optimization Algorithm
## 4 Performance Evaluation  
The performance of the DECMN algorithm is evaluated and compared with other algorithms, specifically DEAP, DECN [25], and DECEN [26], which are traditional layered protocol systems. The evaluation is conducted using MATLAB version R2021a, and the simulation settings include the same system and resource characteristics for all algorithms. The energy consumption per bit, average bit rate, and transmit power are assessed in relation to different MI transmission ranges while considering interactions among physical layer features (such as power control architecture, channel coding schemes, and modulation techniques). Two fixed modulation/FEC combinations (BFSK/BCH(63,57,1) and BPSK/No FEC), as well as a cross-layer design that offers centralized power control, are used in the comparison for DEAP, DECN, and DECEN. The conductivity \(\sigma\) is set to 0.01 S/m for the dry soil. Other simulation parameters include the coil radius, 15 cm; the number of turns, 5; and the operating frequency, 7 MHz. The number of sensors is considered to be 10-50. The transmission range is set to 1-15 m, and the packet length is set to 100 Bytes. Table 1 provides the simulation parameters used for the simulations performed. The transmissions must comply with the following QoS requirements: for a packet length of 100 Bytes, the maximum allowable packet error rate is \(10^{-4}\); the outage delay probability must be less than 0.8; and the predicted hop latency is 0.9 s. The high interference scenario is concerned with 0.02 W noise with MAI power level. Additionally, the application's weight vectors are set to \(w_{1}=0.7\) and \(w_{2}=0.3\) (Equation (10)) for energy consumption and link throughput, respectively. DECMN, DEAP, DECN, and DECEN are evaluated in terms of their performance in MI communication and MI waveguide with and without 3D coil scenarios. The simulation parameters used for DECMN, DECEN, and DECN algorithms include a population size of \(NP=20\), a maximum number of iterations of \(t_{max}=100\), and a problem dimension of \(D=2\). The simulation results demonstrate that DECMN outperforms DEAP, DECN, and DECEN in terms of energy savings and higher throughput. This makes DECMN particularly suitable for WUSNs, where energy efficiency and reliable data transmission are crucial. The comparison highlights the advantages of the DECMN algorithm over traditional layered protocol systems, showcasing its potential for optimizing resource usage and achieving high system performance in WUSNs. Tables 2 and 3 demonstrate that centrally regulated power consumption in WUSNs can achieve more network information and consume less energy compared with the proposed distributed power management method. However, these assumptions are based on theoretical calculations that involve a significant exchange of control signals, which is impractical in real-world WUSN scenarios. DECMN, on the other hand, enables efficient power management by considering the multiuser access interference power level and received noise at the receivers as necessary information for the transmitter. This approach allows for practical centralized energy usage and even achieves efficiency over extended transmission distances. It considers the limitations of real-world WUSNs, such as the inaccuracy of coil angles and the impracticality of multilayer arrangements. In the context of MI-based WUSNs, the use of MI waveguides and 3D MI coils addresses challenges such as large antenna size, dynamic channel conditions, and limited communication range. These technologies prove to be more suitable for underground communication scenarios. Furthermore, the cross-layered architecture, as employed by DECMN, has demonstrated superiority over traditional layered protocol architectures in terms of energy savings and link reliability. By considering interactions and optimization across multiple layers, cross-layer designs can effectively address the specific requirements and constraints of WUSNs, leading to improved performance and resource utilization.