# Game Theoretic Solution for Power Management in IoT-Based Wireless Sensor Networks
## 1 Introduction  
In this digital era, every object is getting smart, i.e., intelligence is embedded for quick and rational decisions. Internet of things (IoT) and next-generation networks are playing vital roles in connecting, operating, and monitoring distinct objects for smart decision making [1]. The enabling underlying technology for IoT are wireless sensor networks (WSN). WSN consists of self-configuring sensor nodes capable of sensing, collecting, and forwarding data to the sink node, known as base station (BS). The sensor nodes combine sensing, computing, and communication in a single unit. The data reach to the BS through intermediate nodes called relay nodes which have routing capability. We can use WSNs in many applications, like real-time tracking, environmental monitoring, disastrous regions, industrial operations, and health care [2]. The sensor nodes are deployed in remote and hostile regions, working continuously by discharging their only sources of energy, i.e., batteries. The embedded batteries of sensor nodes cannot be recharged or replaced. It is important to mention that some of the built-in power technologies helped to improve the lifetime of the WSNs. However, nodes are deployed in tough environments for a very long time, therefore, it is necessary to use the power source efficiently, and this can be only achieved through an efficient power management scheme [3]. We know that sensor nodes do sensing, processing, and communication. They take maximum power consumptionin communication [4], therefore, researchers focus on communication by establishing some better schemes that ensure efficient energy utilization. Different energy management schemes are used to extend the lifetime of the networks such as workload management, mobile networks, clustering, optimal deployment of nodes, intelligent data gathering, and energy harvesting from the environment [5]. Some of the selfish sensor nodes drain out energy by communication, mostly keeping off their antennas. Therefore, we need some powerful tool that can evaluate and make the decisions rationally. One of the candidate schemes to optimize energy consumption is clustering. Clustering techniques are mainly focused on the communication part of the WSNs by prolonging the lifetime. It has been involved in many applications of the WSNs as well [6]. The election of the cluster head (CH) is an important issue in the overall process. In the CH selection process, many parameters are considered keeping in view the requirements and scenarios of WSNs. In this process, every node cannot be suitable to assume the role of CH due to low energy, unimportant location, and distance from BS, etc. To select the proper node as a CH, game theory (GT) has been found very suitable to make rational decisions [7]. GT is a powerful mathematical tool used to analyze and predict the decisions of individuals under conflict situations. An evolutionary game theoretic model can be used to efficiently divide the WSNs into various clusters in such a way that each node can make interactive rational decisions for their own benefits. Such mechanisms can guarantee the tradeoff between the rationality of nodes with the most efficient utilization of energy in the entire network. In evolutionary games, the nodes, referred to as players, rationally adopt a strategy set, which continuously varies with the passage of time by reading the behavior of other nodes in the network. The CH can be optimally selected by using this game model in an iterative fashion. The network evolves with the passage of time and learns the best preferences, which are ultimately used for CH selection in an efficient way. This theory is originated from the area of population dynamics, where nodes intelligently adopt the most suitable strategies to maximize their payoff [8]. This work proposes a technique for energy-efficient clustering by combing an evolutionary game with some novel additional improvements.