The provision of location-based services on consumer devices has moved from on-demand navigation capabilities to always-on ubiquitous location-aware tools such as weather updates, travel information, location-based reminders and many more. Background localisation is generally provided by Wi-Fi fingerprinting, since GPS does not provide service in indoor environments where we spend 80% of our time. However the power consumption of a Wi-Fi scan is proportional to the number of channels scanned, and so naive full-channel scans are inefficient. Here we describe and validate SwiftScan, an intelligent, self-training Wi-Fi fingerprinting scheme that reduces the energy consumption of periodic background Wi-Fi scanning for localisation. SwiftScan is tested with data from more than a thousand Android users over a six month time period and we show that energy savings of over 90% are possible, and that the majority of users benefit from more than a 70% reduction in the energy consumption associated with a Wi-Fi scan for localisation purposes.
['Ramsey M. Faragher', 'Andrew C. Rice']
SwiftScan: Efficient Wi-Fi scanning for background location-based services