# A Lifetime Maximization Relay Selection Scheme in Wireless Body Area Networks
## 6 Performance Evaluation
### Result Analysis
It can be seen from the figure that, except for the benchmark, which only utilizes direct one-hop transmission to communicate, the other three relay selection schemes will perform better if the total number of sensor nodes increases. The reason for this observation is that more sensor nodes in the network will provide more choices of relay nodes for the sensor nodes, which need two-hop cooperative transmission. As a result, relay selection schemes can  
Figure 6: Network lifetime performance comparison on the general WBAN model with the variation of \(\sigma^{2}\). Figure 7: Network lifetime performance comparison on the general WBAN model with the variation of \(R\). allocate more appropriate relay selection to each sensor node in WBANs, which, in turn, prolongs the network lifetime. Furthermore, in the figure, LMRSS outperforms other schemes and the gap between LMRSS and the other two scheme becomes larger with the increasing number of nodes. In particular, the gap between LMRSS and benchmark varies from 57% to 103%, 33.4% to 44.3% for the sum-rate algorithm, and 12% to 22.4% for the maxi-rate algorithm. To conclude the simulation results presented above, the advantages of LMRSS can be summarized as follows:  
* In a body-shaped WBAN application scenario, the proposed scheme can effectively improve the network lifetime of WBANs by 11.8% at least when compared with the direct transmission strategy and is also proved to be better than other existing relay selection methods. A comprehensive comparison between LMRSS, maxi-rate algorithm and sum-rate algorithm is summarized in Table 5 based on the simulation results. * As LMRSS takes battery capacity diversity into account, it selects relay nodes depending on not only the energy consumption condition but also the remaining energy of each sensor node. When serious energy inequality conditions appear in the network, the advantage of network lifetime improvement of LMRSS is more significant when compared with other schemes. * The network lifetime advantage of the proposed LMRSS is not restricted in body-shaped WBAN applications. As in a more generalized WBAN model, which is specified in the IEEE 802.15.6 standard, LMRSS still performs better than direct transmission and other existing relay selection methods in terms of network lifetime, no matter how the factors (energy inequality degree, range, number of nodes) vary. * The time complexity of LMRSS is low, which can be implemented in a real-time WBAN system. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Attribute** & **LMRSS** & **Maxi-Rate [24]** & **Sum-Rate [13]** \\ \hline Effectiveness in body-shaped model & Yes & Yes & No \\ Improvement in body-shaped model(to benchmark) & 11.8–20.7\% & 11.8–14.6\% & 0\% \\ Effectiveness in general model & Yes & Yes & Only when \(R>2\) m \\ Improvement in general model (to benchmark) & 14–103\% & 12–67\% & 0–41\% \\ Time complexity of algorithm & Low & High & Low \\ Performance in worst case (to benchmark) & No degrading & Degrading & Degrading * \\ \hline \hline \end{tabular}  
* Sum-rate algorithm will degrade the network lifetime performance when compared with the benchmark in the worst case of the general model if network range is larger than 2 m.  
\end{table} 
Table 5: Comparison of relay selection algorithms. Figure 8: Network lifetime performance comparison on the general WBAN model with a varying number of sensor nodes.