With the widespread use of the Internet of Things (IoT), it is possible to continuously and accurately monitor different phenomena in the environment. For example, indoor environment detection of smart buildings, pollution source monitoring of smart cities, etc. This paper focuses on indoor environmental monitoring, using intelligent decives to collect indoor environmental data, analyzing the spatial correlation of data, using Gaussian Process (GP) to achieve spatial field reconstruction, and data prediction at any position in space. Most importantly, this method has practical significance in energy conservation and emission reduction and safety monitoring methods. The proposed approach was implemented in Intel Berkeley Lab, where the obtained results are highly promising.