# An Energy-Efficient Game-Theory-Based Spectrum Decision Scheme for Cognitive Radio Sensor Networks
## 4 Performance Evaluation  
We evaluated the performance of EGSD-R and EGSD-G and compared the results with MVSD (see Section 2). The tool we used is MATLAB with the simulation settings presented in Table 1. The network topology is shown in Figure 7. We selected two evaluation parameters: network lifetime, defined as the time until half of the sensor nodes are alive; and overhead, defined as number of time-slot spends for coordination divided by the total number of time slots. Figure 8 shows the network lifetime results with a varied number of maximum PUs. The lifetimes of EGSD-G and EGSD-R are relatively consistent, whereas the lifetimes of MVSD increase with an increasing maximum number of PUs. These results show the merit of a centralized algorithm where the central entity has global knowledge of the network and is therefore able to optimize network performance even when the number of PUs increases. However, both EGSD-G and EGSD-R outperform MVSD. The reason is that MVSD requires multiple control-packet exchanges to the sink for each frame, while EGSD requires fewer transmissions of control packets, especially in the D mode. On average, EGSD-R and EGSD-G have 25.5% and 19.8% longer lifetimes, respectively, compared with MVSD. The best performance is obtained when the number of PUs is 30. Despite the additional complexities of EGSD-G com-pared with EGSD-R, the performance of the latter turned out to be better. EGSD-R outperforms EGSD-G by 4.6%, on average. We found out that this occurred because the PUs were also selecting their channels randomly. Random channel selection of PUs renders prediction by a Markov chain less optimal. Therefore, we include another scenario where the PUs have a certain favorite channel and would likely select it as their operating channel. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Network area & 300 m \(\times\) 300 m \\ Number of sensor nodes & 30 nodes \\ Installation method & Random \\ Number of sink & 1 \\ Location of sink & 150 m, 150 m \\ PU protection range & 50 m \\ PU active probability = PU passive probability & 0.5 \\ PU location mobility = PU channel mobility & 0.5 \\ Number of licensed channel & 29 channels \\ Number of unlicensed channel & 29 channels \\ Maximum noise level & 10 \\ Maximum peak interference & 14 \\ \(SNR^{\prime}_{\textit{trus}}\) & 5 \\ Common control channel frequency & 474 MHz \\ Licensed channel frequencies & 482–546 MHz (bandwidth 8 MHz) and 536–787 MHz (bandwidth 13 MHz) \\ Unlicensed channel frequencies & ISM 24 GHz and 5 GHz \\ Power supply of sensor nodes & 3960 \(\times\) 2[48,49] \\ Sensing range & 50 m \\ Transmission range (initial) & 100 m \\ Interference range & 150 m \\ Maximum limit of random delay & 25 time slots \\ Residual energy levels & 5 levels \\ \(P_{\textit{trus}}=P_{\textit{trus}}\) & 0.5 \\ \(C  
Notice that this selection is not fixed but probabilistic, i.e., the PUs do not always select the favorite channel on each occasion. We consider that this assumption is acceptable especially if the PUs are TV viewers, who may have some preferred TV shows during certain times. The results of the network lifetime where the PUs have favorite channels are shown in Figure 9. We expected the performance of EGSD-G to be better than the others; however, the simulation results did not fully support our expectation. When the numbers of maximum PU are 30 and 90, the EGSD-G reached almost the same lifetime as EGSD-R, with a minor difference of two and seven frames, respectively. However, when the number of PUs is 60, EDSG-G had 3.2% longer lifetime compared with EGSD-R. Another observation is that the last sensor node depletes its energy after a longer time (14.2% longer) in EGSD-G than in EGSD-R. Therefore, we conclude that the number of PUs affects the performance of EGSD-G relative to EGSD-R. When the number of PUs is low, the prediction by the Markov chain did not perform optimally because there was not enough data.