# Empowering WBANs: Enhanced Energy Efficiency Through Cluster-Based Routing and Swarm Optimization
## 4 Results and Discussion  
In this part, simulation is used to assess the effectiveness of the proposed cluster-based routing method. A desktop computer with an Intel(R) Core(TM) i7-7500U CPU operating at 2.70 GHz or 2.90 GHz and 16 GB of RAM is utilized for the research. The Windows 10 Pro operating system is employed, and Python is utilized to assess the efficiency of the suggested method. We also compare our suggested method to four cutting-edge routing and clustering protocols: MT-MAC [13], DHCO [23], ALOC [17], and M-GWO [12]. Both current algorithms are intended for use in WBAN applications. We compare the proposed ABBSOA- MSSOA method to existing algorithms regarding PDR, throughput, residual energy, AE2E, NLT, network energy use, communication overhead, and packet loss rate (PLR). A total of 500 nodes were used in the research setup to perform over 5000 rounds of testing in the wireless channel environment. The wireless channel model was employed for an information rate of 250 kbps. A total of 4 clustersformed the network and used Omni antennas for communication. During the trials, 1000 bytes was the size of each transmitted packet. Table 2 summarizes the simulation parameters.  
The experimental findings compare the ABBSOA-MSSOA algorithm's performance to those of more conventional energy optimization techniques. To assess how well the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of nodes & 500 \\ \hline Number of rounds & 5000 \\ \hline Channel model & Two-ray communication channel \\ \hline Data rates & 250 kbps \\ \hline Antenna type & Omni Antenna \\ \hline Number of clusters & 4 \\ \hline Packet size & 1000 bytes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
ABBSOA-MSSOA hybrid algorithm works, the experiments monitor latency, data transmission reliability, energy usage, and network longevity. Tracking the amount of energy each sensor node utilizes during data transmission and clustering allows us to assess energy usage. The life expectancy of a network is calculated by keeping track of how long it takes for the first node to lose power, which signifies the point at which the network can no longer function effectively. The absence of data loss or error during packet transfer from the sensor nodes to the base station is one measure of data transmission reliability. The time it takes for data to travel from the source sensor node to the destination node or base station is often called latency. These indicators provide a comprehensive view of the network's energy efficiency and performance from various configurations.  
Due to increased communication overhead and more frequent routing choices, energy consumption tends to rise as the number of nodes grows. On the other hand, the right optimization might improve energy distribution and load balance. Data transmission energy increases when route loss increases due to more complicated channel models like shadowing. This shows how crucial it is to correctly represent real-world situations using the right channel model. Increasing the communication range by increasing transmission power and energy consumption is possible. Network density and desired range determine the ideal transmission power. Energy consumption rises in response to a direct correlation between increased traffic loads and the strain on network infrastructure. Efficient routing and clustering techniques are needed to maintain energy efficiency under heavy traffic situations.