Hybrid Kalman Filter and Optimization-Based Routing for Energy Efficiency in Heterogeneous Wireless Sensor Networks

Several Significant research has been done in the areas of distributed applications, database management systems, and information collecting in computer science concerning data mining and processing for wireless sensor networks (WSNs).As a result of WSNs' limited computation, networking, and data mining capabilities, the primary objective of creating WSN-based applications has proven to be extremely challenging: making decisions in real-time. As such, typical data mining techniques are difficult to apply to sensor data due to their nature, peculiarities, and the constraints of wireless sensor networks. This work introduces a novel method for data mining and gathering and noise removal in wireless sensor networks (WSNs), dubbed KF-BA. This methodology increases network lifetime and efficiency by combining the Kalman Filter (KF) with the Bat Algorithm (BA). The recommended methodology and BA are contrasted with the provided techniques for raising energy consumption and prolonging network lifetime. The BA algorithm is not as effective as the KF-BA approach. The suggested strategy yields network longevity that is almost (57%) longer than BA in this instance, and that is after 2,000 packets are sent to two sensors spread over the network.