A platform for power management based on indoor localization in smart buildings using long short-term neural networks

Location-based services (LBSs) have drastically changed the way smart cities and smart buildings operate, giving a new dimension to the life of citizens. LBS have various applications ranging from power management, marketing, vehicle to everything communication to social networking, and many other applications. The concept of LBS relies on the estimation of a mobile device location either inside a city or a building. In this work, we present our three-layer collaborative LBS platform for various types of users and buildings. The first of these layers is the hardware layer consisting of the hardware used for location estimation inside smart buildings. On top of the hardware layers exists the indoor localization layer, at which we implement our indoor localization algorithms using long short-term neural networks for location estimation. Finally, the collaborative LBS system lies on top of other layers. The novelty of our approach stems from providing a complete layered architecture for LBS usage for power management. The proposed system offers collaborative features and services, that is, input to power management system that optimizes power usage in smart buildings based on user locations.