# Game Theoretic Solution for Power Management in IoT-Based Wireless Sensor Networks
## 2 Related Work  
Game theory (GT) is a mathematical construct that defines the conditions of cooperation, non-cooperation, and repetition among rationally independent decision-makers. In the recent literature, this theory has been proven by many researchers as one of the best tools in wireless ad hoc networks for various purposes. GT can be used in WSNs for a variety of purposes including security, clustering,routing, load balancing, quality of service (QoS), power control, intrusion detection, selfish node management, and efficient resource management [9]. Energy management in WSNs through clustering techniques and GT have been considered for many years. This has opened a rich area for researchers in the field of WSNs. However, there are some open problems to be settled. We briefly summarize some of the major contributions in the field. It is important to mention that some works have focused only on the clustering process and have improved the lifetime of WSNs successfully. On the other hand, others focused on the cluster size with taking two parameters for measuring the lifetime of a sensor node, namely residual energy and cluster size. Clustering has been also used in area coverage awareness using residual energy and location [4]. Clustering has two types, namely centralized and distributed. In the first type, the BS is responsible for all types of cluster formations, while in distributed clustering the hierarchical structure with low energy models is generally used to form clusters and select CHs. The drawback of centralized clustering is due to its centralized implementation and complex design. This is not quite useful for sensor networks containing a huge number of nodes. Moreover, this mechanism is dependent upon the position of nodes and CHs, and respective cluster distribution may not be equal. The data fusion technique for distributed clustering is more energy-efficient protocol. This has decreased the importance of CHs and cluster creation by centralized stations. The data fusion models use low energy and hierarchical structure for cluster formation and CHs selection. The ICCBP (inter-cluster chain-based protocol) is a clustering algorithm that utilizes multi-hop and intra-cluster communication to replace CHs when the existing CHs run out of their energy [10]. The hop distance and residual energy are the CH selection parameters. Similarly, in Ref. [11] an energy-efficient multi-hop routing is controlled using clustering for WSNs with the help of two parameters, namely residual energy and distance. We found in the literature a new approach called cognitive radio WSN (CR-WSN) [12; 13; 14; 15]. CR-WSN has received significant attention. This scheme exploits the spectrum resource efficiently for huge traffic. The mechanism is capable of reducing packet loss and power waste. It can also manage a high degree of buffer and communication quality. This approach enhances the spectrum utilization, network efficiency, life time of WSNs, and the end-to-end goal is achieved. On the other hand, it is well known that power efficiency is increased by having distant but densely populated hops. This fact can help in power conservation in CR-WSNs. However, WSN clustering methods cannot be implemented directly CR-WSNs. The reason is communication-based decision making for CH selection. In the conclusion, CR-WSN is still an open area of research that needs due attention. However, game theoretic approaches for spectrum utilization, packet arrival prediction, energy-efficient routing, media access control (MAC) protocols, and design of cross-layer algorithms for better power efficiency have already been introduced. Clustering is simple and improved scalability suitable for WSN has the advantage of low energy consumption and is good in many energy-efficient routing algorithms like LEACH, LEACH-C [16], and PEGASIS [17].