A Data-Driven Algorithm for Indoor/Outdoor Detection Based on Connection Traces in a LTE Network

• Published in: IEEE access Vol. 7; pp. 65877 - 65888
• Main Authors: Bejarano-Luque, Juan L., Toril, Matias, Fernandez-Navarro, Mariano, Acedo-Hernandez, Rocio, Luna-Ramirez, Salvador
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Base stations; Context; Estimation; Geology; Indoor detection; Indoor environments; logistic regression; Long Term Evolution; LTE; Model testing; Quality of experience; Statistical analysis; Statistical models; Temperature sensors; traces; User satisfaction; Wireless communications; Wireless networks
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2917592

Environmental factors have a strong impact on the satisfaction of mobile users. Thus, estimating the context of a session is key to evaluating the end-user experience in mobile network management. Such a context is mainly defined by user location. In most cases, user location is derived from network measurements in the absence of handset measurements. Unfortunately, the current geolocation techniques do not have enough accuracy to detect if the user was indoor or outdoor. In this paper, a data-driven statistical model is proposed to detect if a cellular connection is originated in an indoor location based on the traffic attributes of the connection. Unlike the state-of-the-art approaches, based on application-level data, the proposed model is developed by logistic regression on data from radio connection traces stored in the network management system. The model is tested with a large trace dataset from a live Long Term Evolution (LTE) network.