Data Classification and Compression for Efficient Sensor-Cloud Communication

The Wireless Sensor Network (WSN), which consists of a collection of specialized sensors connected by a communication infrastructure for monitoring and managing conditions in many places, is a relatively new technology that is gaining in popularity. Furthermore, cloud computing is a kind of high-performance computing that makes use of a network of distant servers to store, manage, and analyze data instead of a local server or personal computer. By integrating the capabilities of both ends, sensor-cloud architecture is also delivering excellent services. To offer such services, a significant volume of sensor network data must be transmitted to a cloud gateway, which necessitates a considerable quantity of bandwidth and time. In this article, we present an effective sensor-cloud communication method that uses statistical classification based on machine learning, as well as compression utilizing the deflate technique with minimum information loss, to reduce the huge bandwidth and time requirements. The experimental findings describe the suggested method's overall efficiency in comparison to conventional and related research.