User-localization and positioning systems have been a core challenge in the domain of context-aware pervasive systems and applications. GPS has been the de-facto standard for outdoor localization; however, geo-satellite signals upon which GPS rely, are inaccurate in indoor environments. Therefore, various indoor localization techniques based on triangulation, scene analysis, or proximity, have been introduced. The most prominent technologies over which these techniques are applied include WiFi, Bluetooth, RFID, Infrared, and UWB. Due to the ubiquitous deployment of access points, WiFi-based localization via triangulation has emerged to be among the most prominent indoor positioning solutions. A major deployment obstacle for such systems, however, is the high-energy consumption rates of Wifi adapters in mobile devices where energy is the most valuable resource.

We propose GreenLoc, an indoor green localization system that exploits sensors prevalent in today's smart-phones in order to dynamically adapt the frequency of location updates required. Significant energy gains can, therefore, be acquired when users are not mobile. For example, accelerometers can aid in detecting different user states such as walking, running or stopping. Based on these states, mobile devices can dynamically decide upon the appropriate update frequency. We accommodate various motion speeds by estimating the velocity of the device using the latest two location coordinates, and the time interval between these two-recorded locations. We have taken the first steps towards implementing GreenLoc, based on the infamous Ekahau system. We have also conducted preliminary tests utilizing the accelerometer, gravity, gyroscope, and light sensors residing on the HTC Nexus One and IPhone4 smart-phones.

To further save energy in typical indoor environments, such as malls, schools, and airports, GreenLoc exploits people's proximity when moving in groups. Devices within short-range of each other do not necessarily require that they each be individually tracked. Therefore, GreenLoc detects and clusters users moving together and elects a reference node (RN) based on device energy levels and needs. The elected RN will then be tracked via triangulation while other nodes in the group will be tracked based on the RN's location using Bluetooth. Our initial analysis demonstrates very promising results with this system.


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