Published Paper Details:

Cognitive Sensor Networks (CSNs) have emerged as a critical component in the development of smart environments, enabling more intelligent and adaptive systems. Integrating advanced machine learning (ML) techniques within CSNs can significantly enhance their capabilities, leading to higher accuracy in data analysis, improved energy efficiency, and greater adaptability. This paper explores various ML techniques, including supervised learning, unsupervised learning, reinforcement learning, and deep learning, and their application in CSNs. We propose a comprehensive system architecture that leverages these techniques for efficient data processing, adaptive decision-making, and resource management. Comparative numerical analysis demonstrates the proposed system's superior performance over traditional systems, highlighting improvements in detection accuracy, energy consumption, latency, and adaptability. The results indicate that ML-enhanced CSNs can provide more efficient and responsive smart environment applications. Future research directions are discussed to further refine these techniques and explore their application in diverse real-world scenarios.