Random Forest Learning Based Indoor Localization as an IoT Service for Smart Buildings

Abstract

More buildings are becoming smart day by day which play a key role in development of smart cities and Internet of Things is essential in the development of such smart buildings. The incorporation of smart infrastructure is taken care right from the design phase of smart buildings itself. One of the crucial smart infrastructures in the development of smart buildings and smart cities is indoor localization. Being aware of the location or movement of people within a building can be very useful in several ways like energy management, location aware marketing services etc. and there are so many such IoT services. So the research problem here is to locate people within a building without using any additional infrastructure. Many research proposals have already been made over the last few years with the goal of predicting location in smart buildings but the real challenge lies with the accuracy of predicted location. User privacy and energy efficiency are also major challenges of indoor localization. Here we propose a random forest based machine learning algorithm that concentrates on improving the location accuracy in indoor localization as an IoT service for smart buildings. The obtained experimental results show 14% better success test prediction percentage in terms of overall deviation.