Innovative artificial intelligence and game theoretic approach for target tracking in the sensor network

The efficient tracking of targets within sensor networks is a fundamental challenge with wide-ranging applications, from surveillance and security to environmental monitoring and autonomous navigation. This research introduces a groundbreaking approach that combines innovative artificial intelligence (AI) techniques with game theory to enhance target tracking within sensor networks. Sensor networks are characterised by distributed nodes that collect and transmit data about the environment. Tracking dynamic targets within such networks requires intelligent decision-making in real-time. The proposed approach harnesses the power of AI, specifically machine learning and deep neural networks, to process sensor data and extract valuable information about target movement and behaviour. At the core of this research is the integration of game theory, a robust framework for modelling strategic interactions. Game theory enables modelling interactions among multiple entities, such as sensors and targets, as strategic games. By formulating the tracking problem as a game, the study explores optimal strategies for sensor nodes to track targets while cooperatively considering resource constraints and uncertainties. The innovative aspect of this research lies in the synergy between AI and game theory. Machine learning models, such as convolutional neural networks and Long short-term memory, are employed to analyse sensor data and predict target trajectories. These models adapt and improve their tracking performance over time, learning from historical data and sensor observations. This research represents a significant step toward enhancing the capabilities of sensor-based systems for real-world applications.