Human Indoor Localization for AAL Applications: An RSSI Based Approach

Ambient intelligence technologies have the objective to improve the quality of life of people in daily living, by providing user-oriented services and functionalities. Many of the services and functionalities provided in Ambient Assisted Living (AAL) require the user position and identity to be known, and thus user localization and identification are two prerequisites of utmost importance. In this work we focus our attention on human indoor localization. Our aim is to investigate how Received Signal Strength (RSS) based localization can be performed in an easy way by exploiting common Internet of Things (IoT) communication networks, which could easily integrate with custom networks for AAL purposes. We thus propose a plug and play solution where the Beacon Nodes (BNs) are represented by smart objects located in the house, while the Unknown Node (UN) can be any smart object held by the user. By using real data from different environments (i.e., with different disturbances), we provide a one-slope model and test localization performances of three different algorithms.