Deep Learning Challenges and Prospects in Wireless Sensor Network Deployment

This paper explores the transformative integration of deep learning applications in the deployment of Wireless Sensor Networks (WSNs). As WSNs continue to play a pivotal role in diverse domains, the infusion of deep learning techniques offers unprecedented opportunities for enhanced data processing, analysis, and decision-making. The research problem addressed in this paper revolves around navigating the challenges associated with incorporating deep learning into WSN deployment. The methodology involves an extensive literature review, highlighting the increasing role of deep learning in addressing WSN challenges. Key findings underscore the potential improvements in energy efficiency, data processing speed, and accuracy achieved through deep learning-empowered WSNs. The implications of this research extend to diverse applications, including environmental monitoring, healthcare, industrial systems, and smart agriculture. As we delve into the future research agenda, the paper identifies the need for further exploration in areas such as adaptability to dynamic environments, privacy-preserving optimizations, and scalable deep learning models tailored to the unique constraints of WSNs.