  A large number of sensors deployed in recent years in various setups and
their data is readily available in dedicated databases or in the cloud. Of
particular interest is real-time data processing and 3D visualization in
web-based user interfaces that facilitate spatial information understanding and
sharing, hence helping the decision making process for all the parties
involved. In this research, we provide a prototype system for near real-time,
continuous X3D-based visualization of processed sensor data for two significant
applications: thermal monitoring for residential/commercial buildings and
nitrogen cycle monitoring in water beds for aquaponics systems. As sensors are
sparsely placed, in each application, where they collect data for large periods
(of up to one year), we employ a Finite Differences Method and a Neural
Networks model to approximate data distribution in the entire volume.
