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 …

Deep learning has become a predominant method for solving data analysis problems in
virtually all fields of science and engineering. The increasing complexity and the large …

Wireless sensor networks (WSNs) are typically used with dynamic conditions of task-related
environments for sensing (monitoring) and gathering of raw sensor data for subsequent …

Abstract Wireless Sensor Networks (WSNs) are widely studied for their data collection and
monitoring capabilities across diverse applications. However, the limited energy resources …

Before discovering meaningful knowledge from big data systems, it is first necessary to build
a data-gathering infrastructure. Among many feasible data sources, wireless sensor …

Wireless Sensor Network (WSN), which are enablers of the Internet of Things (IoT)
technology, are typically used en-masse in widely physically distributed applications to …

Wireless sensor networks (WSNs) have become widely ubiquitous deployed in many
application domains over the past few decades. Classical approaches configure WSNs …

Wireless sensor networks (WSNs) monitor dynamic environments that change rapidly over
time. This dynamic behavior is either caused by external factors or initiated by the system …

Industrial Wireless Sensor Networks (WSNs) are becoming increasingly popular due to their
enhanced scalability and low cost of deployment. However, they also present new …

With the advent of the Internet of Things (IoT) concept and its integration with the smart city
sensing, smart connected health systems have appeared as integral components of the …