Database-driven quality prediction for mobile wireless networks in smart home environments

In this paper we propose a database-driven quality prediction system for mobile wireless networks in smart home environments. Smart handheld devices obtain location information from smart home tags such as Bluetooth Low Energy and Near Field Communication tags, and collect various quality measurement of mobile wireless networks. The measurement reports are uploaded to the database server periodically or on demand. By applying machine learning techniques which use the measurement reports as the training data, the quality in the smart home environments could be predicted.