GTTR: A Game Theory-based TOPSIS Optimized Routing Protocol for WSNs

Wireless Sensor Networks (WSNs) are widely used in challenging environments, making it crucial to optimize the energy consumption of sensor nodes. One way to achieve energy efficiency in WSNs is through clustering techniques. Most existing protocols consider critical parameters like remaining energy, distance to the base station (BS), and node degree but do not consider another impacting factor, the history of a node as a cluster head (CH). This paper presents a novel approach combining game theory and a fitness function to select optimal CHs based on desired qualities such as high remaining energy, low intra-cluster communication distance (IAD), and low history of acting as a CH. The proposed method selects temporary CHs (TCHs) using game theory by considering parameters like remaining energy, IAD, node degree, and node history. The final CHs are determined using a fitness function that calculates the payoff for each node based on three metrics - remaining energy, node history, and intra-cluster communication distance. Furthermore, a multi-criteria decision-making technique known as technique for order of preference by similarity to ideal solution (TOPSIS) is utilized to carry out the cluster formation by selecting the most preferred ideal CH to be joined by the sensor nodes. Additionally, TOPSIS is utilized for creating a multi-hop intra-cluster communication (IACC) path between member nodes (MNs) and CH inside a cluster. The proposed Game Theory-based TOPSIS optimized routing protocol (GTTR) outperforms existing methods such as LEACH, HSCR LEACH, CT-RPL, and GTFR regarding network lifetime and energy consumption in three different network scenarios.