Energy-Efficient Indoor Localization WiFi-Fingerprint System: An Experimental Study

In order to apply indoor localization systems in real environments it is necessary to provide an accurate location without implying a high impact on the user’s normal behaviour. To achieve this goal, in this paper, a combination of battery saving techniques with a system based on WiFi fingerprinting...

Descripción completa

Detalles Bibliográficos
Autores: Salazar González, Jose L., Soria Morillo, Luis Miguel, Álvarez-García, Juan A., Enríquez de Salamanca Ros, Fernando, Jiménez Ruiz, Antonio R.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/215871
Acceso en línea:http://hdl.handle.net/10261/215871
Access Level:acceso abierto
Palabra clave:Indoor localization
WiFi fingerprinting
RSSI
Battery life
Descripción
Sumario:In order to apply indoor localization systems in real environments it is necessary to provide an accurate location without implying a high impact on the user’s normal behaviour. To achieve this goal, in this paper, a combination of battery saving techniques with a system based on WiFi fingerprinting is proposed. This is done by transferring the location calculation workload to the server, leaving user’s mobile devices the only responsibility of making periodic WiFi network scans at dynamic intervals based on user activity, through an application running on background. There are not many studies analyzing energy consumption of existing localization systems, even though it is an important factor in real applications. In this paper, both energy consumption and accuracy are analyzed, having an energy consumption of only 0.8 Wh (having a 3.7 V battery) during a 24-hour cycle and an average localization error of 4.51 meters. Worth to mention that as computation is done on server side the system can be expanded to multiple buildings and floors. Finally, the dataset used in this paper has been published making possible to test new algorithms in the same environment.