# Game Theoretic Solution for Power Management in IoT-Based Wireless Sensor Networks
## 3 The Proposed Mechanism
### Assessment by Nodes
Our game theoretic solution for power management in wireless sensor networks is referred to as GTSPM. This work and other taken protocols were tested and compared. The results indicate that the GTSPM protocol produces comparatively better qualitative results as compared to the tested protocols. The key performance metrics in this section are (a) the number of dead nodes, (b) average energy consumption, and (c) average throughput and the number of nodes and the pause times. Figure 3 shows the number of alive nodes recorded at 100 intervals. In total, 300 nodes were taken in an area of \(1000\times 1000\) m\({}^{2}\). All the protocols were performing well until the 50 time pause point. The results show that the performance of LEACH and LEACH-C are comparatively low. The number of dead nodes in the GTSPM protocol, over the entire time, is lower than all the compared protocols. This is because multiple options for energy efficiency in the network are considered. The key improvement is obtained by considering the CNs and continuous evaluation of CHs, combined with an evolutionary game model. Figure 4 shows the analysis of all protocols with respect to the number of alive nodes against the varying number of nodes. The values were recorded at 90 pause times, for each set of nodes, in connection with the previous results. It is clear that GTSPM gives a much better performance when the network size is increased. This is because with a higher number of nodes, the number of CNs is also increased. It is possible that the higher number of CN sets suggest many suitable CHs during the selection process. The results for energy consumption can be seen in Figure 5. In this experiment, 300 nodes were taken and we can see that the evolutionary models GEEC, GTEB, and GTSPM performed well, as compared to LEACH and LEACH-C. We can conclude that energy management is much better in evolutionary game-based protocols. Due to additional approaches, our protocol, GTSPM, is slightly \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Simulation Environment & MATLAB under Windows \\ \hline Area & \(500\times 500/1000\times 1000\) m\({}^{2}\) \\ Network Type & WSN/Cluster-based \\ BS Location & Center (250,250/500,500) \\ Number of Nodes & 100 to 400 \\ Node Distribution & Random \\ Comparisons & LEACH, LEACH-C, GEEC, GTEB \\ Initial Energy & 100 J \\ Rx Power & 0.3 J \\ Tx Power & 0.6 J \\ Movement Trace & Off \\ Cluster Size (GEEC/GTEB/GTSPM) & Game-based (Varying) \\ Cluster Size (LEACH, LEACH-C) & 9 nodes \\ Traffic Source & CBR \\ Packet Protocol & TCP \\ \(\textit{CNDist}^{\textit{Thres}}\)(GTSPM) & 100 m \\ \hline \hline \end{tabular}
\end{table} 
Table 6: List of parameters. Figure 3: Number of alive nodes in different time pauses. better than other protocols after time pause 70. The energy efficiency in GTPM is also additionally added by constructing the set of prohibited nodes from cluster headship i.e., PCH. Another energy consumption analysis against the varying number of nodes is given in Figure 6. The values were recorded at time pause 50 for each set of nodes. GTPM gave similar results as those of GTEB. However, it is notable that the performance of GTPM is associated with the network size. Other protocols did not respond well to the increased number of nodes, as compared to GTPM. It can be noted that at the beginning, with fewer nodes, the performance of GTPM was lower than GTEB. As the number of nodes increased, the average remaining energy of all the nodes in GTPM protocols rose higher than GTEB. This was because of additional mechanisms introduced by GTPM. The performance metric for throughput can be seen in the Figures 7 and 8. Figure 7 shows the results for 300 nodes in an area of \(1000\times 1000\) m\({}^{2}\) over 100 time intervals. Prohibiting the selfish nodes for the CH selection process and continuous evaluation of CHs by the BS caused the improvement in throughput as compared to other evolutionary clustering algorithms.