# Game Theoretic Solution for Power Management in IoT-Based Wireless Sensor Networks
## 3 The Proposed Mechanism
### Assessment by Nodes
In this metric GTPM outperformed all the protocols with slightly higher margins. Similarly, in Figure 8, the average throughput is shown for the various numbers of nodes. The results were recorded at time pause 90 in connection with the experiments in Figure 7. In this experiment, all the protocols gave a positive response to the increased number of nodes in the network. GTPM gave similar better results in this case too. Figure 4: Number of alive nodes with varying number of nodes (pause time = 90). Figure 5: Average energy consumed over time (total nodes = 300). ## 5 Conclusions and Future Work

Various evolutionary game-based approaches have been proposed to overcome the cluster division and CH selection issues in WSNs. The main motivation of such mechanisms is to reduce the energy consumption by balancing load among the nodes and select the most appropriate CHs. In this work,

Figure 8: Throughput with varying network size (pause time = 90). Figure 6: Average energy consumed with varying network size (pause time = 50). Figure 7: Throughput with time pauses (total nodes = 300). we have optimized the energy efficiency by incorporating some additional procedures, integrated into the evolutionary game. Initially, the clusters are divided intelligently so that each cluster is formed in its optimal position. Then, to initiate the game, some key parameters are processed to form three different sets of nodes; suitable, non-suitable, and prohibited for CH. The nodes' participation level is considered to mark the prohibited nodes. For the suitability of nodes, the energy, hop level, density, and degree of connectivity of all the nodes are considered. The evolutionary game takes both suitable and non-suitable nodes, along with incentives granted by the BS. The BS continuously monitors the network by using various nodes' and CHs' related parameters and grants incentives in the form of profit and penalties. The work is compared with some fundamental and state-of-the-art existing protocols. The analysis results indicate the excellence of our work by giving adequately better values as compared to other protocols. In future, we are planning to implement and validate our proposed mechanism using an experimental testbed of 100 nodes. Using this testbed, we will be able to get real world results using various network setups and more extensive simulations based on parameter variations. The heterogeneity of nodes can also be addressed and incorporated in the proposed system. The base protocol, DSR, can be replaced by some other protocols, along with some additional procedures. A Stackelberg game can be incorporated in the proposed mechanism by introducing the sets of leading and following nodes. [PERSON] is a Ph.D. scholar. This paper is a part of his Ph.D. research. He developed the basic idea, devised main methodology and worked on simulations. [PERSON] contributed in designing different scenarios and implementations of this research work. [PERSON] is expert in game theory. He worked in designing and implementations of game theoretic approach. [PERSON] worked on the communication and coordination of sensors. [PERSON] supervised and reviewed this work with extreme care. The authors acknowledge the support of the Hankuk University of Foreign Studies Research Fund 2019 for this work. The authors declare no conflict of interest. ## References

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