# Game Theoretic Solution for Power Management in IoT-Based Wireless Sensor Networks
## 3 The Proposed Mechanism  
We know that game theory is the study of mathematical models of planned interaction between rational decision-makers. We found its application in almost all areas of social sciences, as well as in computer science too. Especially in the design of WSNs, game theory has been proven very suitable. More precisely, the EGT encompasses a dynamic process and the replicator dynamics [32]. However, a model based on the EGT considers the devising of a traditional game by using population. Individual players can be of any number in such model and they can adopt strategies against other players in the same population. The EGT has now been extensively used and its applications are to predict the variable tendency of a population's behavior. The proposed work uses an updated evolutionary game model for energy optimization and an efficient CH selection process in cluster-based WSNs. The main theme of the evolutionary game in this work is inspired by GEEC [30]. The work assumes that all the nodes are intelligent and they can perform according to their preferences. However, the system has the authority to mark the nodes according to their performance before considering them for taking the responsibility of cluster-headship. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Key Feature** & **Advantages** & **Disadvantages** \\ \hline Distributed clustering with & Multi-hop and intra-cluster & The need for centralized stations for cluster CH formation is eliminated & Selfishness of nodes is ignored. \\ \hline Cognitive radio WSN (CR-WSN) [12, 13, 14, 15] & Utilization of increased spectrum & Extends WSNs lifetime & Clustering in CR-WSNs has not yet been matured. \\ \hline Energy conservation via & Nodes of clusters made rich & & \\ efficient routing algorithms & in information about & Extend WSNs’ lifetime & Selfishness of the node is ignored. \\ like LEACH, LEACH-C [16], PEGASIS [17, 18] & & \\ \hline One Hop Cluster-Head & & Prolonging the network lifetime and reducing the network data latency & Ignoring the distance factor as nodes get away and deplete energy quickly \\ \hline Dynamic network state & Hidden Markov model & Outperformed in terms of buffer cost, holding cost, overflow, energy consumption, and bandwidth usage & Other optimization approaches are ignored to compare. \\ \hline Noncooperative game & Dependability assessment & Reliability and availability measures for susceptible sensor nodes & Not all possible security measures considered. \\ clustering [22] & & & improved \\ \hline Coalitional game theory [24] & Based on topological structure & Extend WSNs’ lifetime via finding the cheapest route & How to choose corresponding leaders is not mentioned in the work. \\ \hline Bayesian game [25] & Bayesian game to form static game & Extend WSNs’ lifetime & Imperfect information. \\ \hline Game theory [28] & A trade-off between energy conservation and network throughput, double-time Nash equilibrium & Balances energy consumption & Selfishness of nodes are ignored. \\ \hline Game theory-based energy & The proposed mechanism is & & No guarantee of the connectivity and robustness of the network. \\ efficient clustering routing protocol (GEEC) [30] & LEACH-C & & \\ \hline An evolutionary game [31] & Combined the evolutionary game with the classical GT. & Extend WSNs’ lifetime and provides a better delivery ratio. & Selfishness of nodes are ignored. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of advantages and disadvantages of related work. Our work is divided into various segments. In Section 3.1, we discuss the main assumptions and the fundaments associated with this work. Some key terminologies are discussed and highlighted according to our work. In the Section 3.2, the network deployment and cluster division of the entire network is discussed. The cluster sizes are kept dynamic and the whole process purely focuses on the efficiency and optimization of the entire network. Section 3.3 is about game formation. How can the work be aligned to a game model?