Evolutionary game theory with deep learning-based target detection and tracking in sensor networks

Target detection and target localisation/tracking are two distinct but complementary components of target monitoring. Target tracking entails tracking these items over time to forecast their future positions. In contrast, target detection refers to the identification and localization of objects inside a sensor field UAV's range of view. Our method integrates these two essential elements by utilizing deep learning algorithms to increase predictive accuracy and game-theoretic models to optimize decision-making processes. Our research illustrates the unique challenges and approaches involved with these two processes by differentiating them. One of the famous and difficult uses of IoT networks is target tracking, which is the real-time tracking of a mobile object by determining its location and transmitting data as soon as possible. As such, tracking algorithms must be economical and energy-efficient. Optimizing the utilization of sensors by choosing a subset of all deployed sensors at each stage to carry out the tracking process is a crucial component of prediction-based target tracking approaches. Target tracking involves calculating the position of a target as it moves over time; therefore, incorporating a memory aspect into the strategy is crucial. Many publications in the literature take into account the target's current state to predict its future state; they do not take into account the environment's past states. The use of game theory, a reliable framework for simulating strategic interactions, is the basis of this study. Modeling interactions between many elements, such as targets and sensors, in strategic games is made possible by game theory. The paper investigates the best practices for nodes of sensors to track targets while cooperating in taking resource limitations and uncertainties into account by rephrasing the issue of tracking as a game. This research is novel because of how game theory and deep learning interact. The Generative Adversarial Network (GAN) is a machine learning model that analyzes sensor data and forecasts target trajectories. These models adjust and enhance their tracking abilities with time by gaining knowledge from past data and sensor readings. The present study is a noteworthy advancement in developing sensor-based systems with practical uses.