The proliferation of location-based services, applications, and social networks calls for the provisioning of location service as a first class system component. Such location service should have short response time (better instantly available), provide high location accuracy and work seamlessly across outdoor and indoor environments. To achieve these goals, we seek to push down the energy envelop and enable continuous localization in the background, to explore the infrastructure intelligently.
WheelLoc: Enabling Continuous Location Service on Mobile Phone for Outdoor Scenarios
We design and implement a continuous system location service - WheelLoc - on mobile phone for instantaneous, energy-efficient location provisioning for outdoor scenarios. Unlike previous localization efforts that try to directly obtain a point location fix, WheelLoc adopts a detoured approach: it seeks to capture a user mobility trace first and to obtain any point location by time- and speed-aware interpolation or extrapolation. Unlike previous work that explores an accuracy-energy tradeoff between sensors that are either energy efficient or costly, WheelLoc avoids expensive sensors completely and solely relies on commonly available cheap sensors such as accelerometer and magnetometer. With a set of novel techniques and the leverage of publicly available road maps and cell tower information, WheelLoc is able to meet those requirements of a first class component. In particular, we study reliable and accurate capture of user mobility trace using only sensors on mobile phone for driving and cycling cases. We perform map-matching to fix captured traces to the map using a novel HMM model. Extensive experiment results confirmed the effectiveness of uLoc. It can return a location estimate within 40ms with an accuracy about 40 meters, consumes only 240mW energy, and effectively strikes a better energy-accuracy tradeoff than GPS duty-cycling.
* Joint work with my interns He Wang (now at Duke), Zhiyang Wang (now at UCLA) and colleague Fan Li (now at Pearson.com).
FeetLoc: Crowdsourcing-friendly Indoor Mapping and Localization System using Mobile Phones
Accurate and cheap indoor localization is one of the holygrails of mobile computing, as it is the key to enabling indoor location-based services. The problem has attracted significant interest in both academia and industry. One important element in indoor localization, which has so far been largely overlooked or just taken for granted (with assumptions), is the availability of the suitable indoor map, which associates any location to the physical layout inside the building. This is one of the major reason accounting for the ironic facts that so many algorithms were proposed but none become reality. FeetLoc project seeks to attack this fundamental problem by enabling a crowdsourcing way of indoor mapping, including pathway mapping and also the automatic establishment of a radio map that can be readily used for localization. Feetloc requires no prior knowledge of the building, except the mere existence of the WiFi infrastructure. We are working on how to further dismiss this assumption on the existence of the WiFi infrastructure.
* Joint work with my interns Zhuo Chen (now at CMU), Peichao Zhang (now at SJTU), and colleague Yongguang Zhang.
I/ODetector: A Generic Service for Indoor/Outdoor Detection
The location and context switching, especially the indoor/outdoor switching, provides essential and primitive information for upper layer mobile applications. We design and implement I/ODetector: a lightweight sensing service which runs on the mobile phone and detects the indoor/outdoor environment in a fast, accurate, and efficient manner. Constrained by the energy budget, I/ODetector primarily leverages lightweight sensing resources including light sensors, magnetism sensors, celltower signals, etc. I/ODetector is designed to work independently using on-board sensors and provide an instant indoor/outdoor detection. We do not need to fingerprint the environment to acquire a priori knowledge. Being such a generic and lightweight service component, I/ODetector greatly benefits many location-based context-aware applications. We explore some exampler applications such as energy saving for GPS and WiFi.
* Joint work with Dr. Mo Li (Nanyang Tech Univ, Singapore) and his team.