X3D Sensor-based Thermal Maps
for Residential and Commercial Buildings
Data Acquisition and Representation:
Sensor data acquisition involves a memory buffer located on the end-point that collects temperature and relative humidity samples on the end-point system. The wireless sensors are producing measurement values every second, while the data buffer is consumed by the data acquisition client (DAQ) that plays the role of the master, or the data is saved on permanent storage for later use. Also, the DAQ can be connected on the Internet and thus, the data can be accessed remotely and displayed on other system using a simple browser. The buffer with data samples is circular and the values that are provided to the client for simulation purposes or storage are always the last ones stored into the buffer. Consequently, by design, different sampling rates can be implemented and a realtime X3D thermal map can be obtained.
Since the X3D primitives’ position and color/α-values can be controlled in a straight forward manner, the authors choose to model the volume in each room as a set of tangent semitransparent color spheres. The sphere’s color represents the average relative humidity/temperature at the respective location. The α-value of the sphere is set in such a way that different levels of transparency can be achieved depending on the view-point distance from the building. Values are interpolated to determine intermediary relative humidity values. Linear interpolation is used currently (depiction above), however, more complex models may accurately and better represent relative humidity/temperature values.
X3D Thermal Maps
Researchers configured a small house with the set of sensors. They considered each rectangular room as a 3D container of semitransparent, tangent spheres that illustrate the 3D relative humidity map. The sensors have been placed in the corner of each room. The walls are rendered semi-transparent as illustrated in the figures below. One can explore different viewpoints and visualize heat distribution in the house volume. As illustrated in this simulation example, the left corner of the building is overheated due to poor attic insulation or due to an insulation defect in the exterior walls.
Scaling to Large Commercial Buildings
The X3D semitransparent thermal map is scalable to large commercial buildings, however the difficulty in scaling resides in (1) the sensor placement strategy, and (2) the X3D representation. For sensor placement we consider deploying manually a large set of sensors in each room of the building and keeping one data collector for each level. Data collectors can store large sets of data and can also provide access to the collected sensor data through an existing wired/wireless network.
In terms of the X3D representation, for large commercial buildings the authors adopted several polygon reduction techniques. First, researchers replaced the spheres due to their relatively high polygonal count, with the Contact Player [BitManagement 2015] tessellation approximately 300 polygons per sphere. Furthermore, researchers investigated the Box primitive (12 polygons), a custom made Tetrahedron (4 polygons) as well as simple Billboards (1-2 polygons). Interactivity rates of 10-15 frames-per-second were obtained with Tetrahedrons as well as with Billboards.