The MSFF algorithms presented in the book have diverse applications across various fields. They contribute significantly to networked control by improving the reliability and performance of systems through optimal state estimation, even under challenging conditions like censored data and communication constraints. In sensor networks, these algorithms enhance data fusion, leading to more accurate and robust sensor readings, crucial for applications like environmental monitoring and target tracking. For data fusion, the algorithms provide a framework for integrating information from multiple sources, reducing uncertainty and improving decision-making in areas like healthcare diagnostics and autonomous vehicles. Overall, these algorithms advance these fields by offering robust, efficient, and reliable solutions to complex estimation problems.