Blue-Fi: enhancing Wi-Fi performance using bluetooth signals

Mobile devices are increasingly equipped with multiple network interfaces with complementary characteristics. In particular, the Wi-Fi interface has high throughput and transfer power efficiency, but its idle power consumption is prohibitive. In this paper we present, Blue-Fi, a sytem that predicts the availability of the Wi-Fi connectivity by using a combination of bluetooth contact-patterns and cell-tower information. This allows the device to intelligently switch the Wi-Fi interface on only when there is Wi-Fi connectivity available, thus avoiding the long periods in idle state and significantly reducing the the number of scans for discovery. Our prediction results on traces collected from real users show an average coverage of 94% and an average accuracy of 84%, a 47% accuracy improvement over pure cell-tower based prediction, and a 57% coverage improvement over the pure bluetooth based prediction. For our workload, Blue-Fi is up to 62% more energy efficient, which results in increasing our mobile device's lifetime by more than a day.