Location-based services such as targeted advertisement, geo-social networking and emergency services, are becoming increasingly popular for mobile applications. While on-board sensors on the smartphones such as GPS are able to provide accurate outdoor locations, accurate indoor localization schemes now still require either additional infrastructure support (e.g., ranging devices) or extensive training before system deployment (e.g., WIFI signal fingerprinting). In this talk, I will talk about our latest work that uses wireless access point sequence as a new metric for fingerprinting-based indoor localization systems. This metric is resilient to time varying WIFI signal changes, heterogeneous devices and dynamic power control of wireless access points, while is able to achieve very good system performance. Based on this metric, we designed the first signal-fingerprinting indoor localization system that is able to automatically construct the finger-print map and completely eliminate the heavy training.
Another topic I will cover in this talk is large-scale battery management. Large-scale batteries have been widely adopted in applications such as electric vehicles and energy storage in power grids. While the improvement of the battery energy density is relatively slow in the past decade, in this talk, I will discuss how the exploration of battery cell reconfigurations at large-scale battery systems can benefit from real-time dynamic controls for both discharging and charging processes. The experimental results with commercial battery cells on our customized testbed, as well as EV-trace driven emulations demonstrate significantly improved energy efficiency of our proposed designs.
Finally, I'll briefly talk about the convergence of mobile device energy management and battery energy management, and how battery-aware energy management can help prolong the mobile device lifetimes in realistic environments.