# A Lifetime Maximization Relay Selection Scheme in Wireless Body Area Networks
## 6 Performance Evaluation
### Result Analysis
As stated in Section 1, WBANs are not only specified to be used on the human body. For example, in interactive gaming, robots whose shapes are not restricted to the human form but also tree-form or ball-shaped will also need deploying WBANs to fulfill their mission in the game. Therefore, in this paper, we design a more general WBAN model according to IEEE 802.15.6 to comprehensively evaluate our proposed algorithm. In this standard, the typical network range is regulated to be less than 3 m and the maximum number of nodes in a WBAN is up to 64. The designed model is illustrated in Figure 5. In the figure, the coordinator is placed in the center of a circle whose radius is \(R\) while sensor nodes are randomly placed in the area of the circle. When the distance of a sensor node from the coordinator is less than \(R/2\), it is treated as a candidate relay node, which, in the figure, is deployed in the circle with the radius of \(R/2\). Figure 6 shows the lifetime performance with the variation of \(\sigma^{2}\) on the aforementioned WBAN general model. The \(R\) is set to be 3 m, and the number of nodes is 16 in the simulation. It is illustrated that the proposed algorithm outperforms the other three. In more detail, when \(\sigma^{2}=0\), the proposed algorithm improves network lifetime by 59%, 37%, and 0% compared with benchmark, sum-rate algorithm and maxi-rate algorithm, and gradually increases to 79.2%, 45% and 7.4% when \(\sigma^{2}\) increases from 0 to 1. The increasing gaps result from the fact that the proposed algorithm considers the residual energy condition of each node as well as the consumption rate. It is also noticed that the performance \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Performance Item** & **LMRSS** & **Maxi-Rate [24]** & **Sum-Rate [13]** & **Benchmark** \\ \hline Total energy consumption (mJ) & 0.7408 & 0.8218 & 0.7408 & 0.7408 \\ Maximum energy consumption (mJ) & 0.0814 & 0.0738 & 0.0814 & 0.0814 \\ Network lifetime & 14,194.37 & 9983.49 & 14,194.37 & 14,194.37 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance in the worst case. Figure 5: A general WBAN model specified in IEEE 802.15.6.  
of the sum-rate algorithm is not the same as the benchmark, which is different from the model in Figure 1. This is because the distance in this model is larger, and two-hop transmission can be selected in certain conditions to effectively reduce the transmission energy for some far-away nodes, resulting in a decrease of the total network energy consumption. Figure 7 shows the lifetime performance with the variation of \(R\). The number of nodes is still 16 and the \(\sigma^{2}\) is set to be 0.5 in the simulation. It is indicated from the figure that the proposed algorithm still has the best performance no matter how \(R\) varies. The performance of the sum-rate algorithm is the same as the benchmark when \(R\) is small and becomes better if \(R\) is larger than 2 m. LMRSS improves network lifetime by 14% compared with the benchmark and sum-rate algorithm and increases to 59% and 37%, respectively, when \(R\) is 3 m. Furthermore, when compared with the maxi-rate algorithm, the performance enhancement increases from 4.6% to 18% if the distance varies from 150 cm to 300 cm. It is true from the results that the advantage gap of LMRSS over the other three becomes larger with the increasing value of \(R\). The reason for this phenomenon is that with the growing network range, two-hop transmission brings about more transmission distance reduction, which leads to more transmission energy saving. Therefore, the impact of cooperative transmission and its relay selection on network lifetime becomes more and more significant. Figure 8 shows the lifetime performance under different total numbers of sensor nodes in WBANs. \(R\) is set to be 3 m and the \(\sigma^{2}\) is set to be 0.5 in this scenario.