The book tackles the challenges of MSFF under constrained network environments, especially with censored data, through several key approaches. It introduces optimal centralized and distributed filtering fusion methodologies, considering various network constraints like fading measurements, packet dropouts, and redundant channels. The Tobit Kalman filter is employed to handle censored data, which is common in low-cost sensors. The book also explores distributed filtering fusion with packet delays and transmission delays, and it designs distributed fusion estimators for systems with parametric uncertainties and measurement censoring. It further investigates protocol-based MSFF schemes under state saturation and cyber-attacks, and variance-constrained MSFF for nonlinear cyber-physical systems under stochastic communication protocols. The book provides a comprehensive framework for analyzing and designing MSFF algorithms that can effectively operate in challenging network environments with censored data.