EENET: Energy Efficient Detection of NETwork Changes Using a Wireless Sensor Network

Abstract

Contextual services are having increasing importance in nowadays literature due to current availability of always-connected personal devices like smartphones and tablets. It is therefore increasingly important to have an affordable and accurate way for user localization in both indoor and outdoor environments. In outdoor GPS or GLONASS are reliable, but indoor a valid and ubiquitous localization system is still under research. One of the most promising methodology is wireless fingerprinting, which exploits available access points infrastructure. It is composed of two distinct phases: one training phase during which the interesting area is monitored and recorded and a usage phase in which the recorded data is used for localization purposes. The usage phase is reliable and accurate, but the training phase is often time consuming (in particular for large areas) as it must be performed manually and may also need to be repeated in case of structural and environmental changes. In this paper we propose a novel framework which uses an appropriate Wireless Sensors Network allowing continuous training over time in order to achieve real-time updating of the fingerprinting database without any human interaction, while also aiming to reduce power consumption needed for training phase, determining a minimal set of sentinel, which are sensors able to detect network alterations and able to trigger RadioMap rescanning.