Publications

Wearable cognitive assistance applications can provide guidance for many facets of a user’s daily life. This thesis targets the enabling of a new genre of such applications that require both heavy computation and very low response time on inputs from mobile devices.  The core contribution of this thesis is

Accurate, up-to-date maps of transient traffic and hazards are  invaluable to drivers, city managers, and the emerging class of  self-driving vehicles. We present LiveMap, a scalable, automated system  for acquiring, curating, and disseminating detailed, continually-updated  road conditions in a region. LiveMap leverages in-vehicle cameras,  sensors, and processors to crowd-source hazard

VM handoff enables rapid and transparent placement changes to  executing code in edge computing use cases where the safety and  management attributes of VM encapsulation are important. This versatile  primitive offers the functionality of classic live migration but is  highly optimized for the edge. Over WAN bandwidths ranging from 5

An emerging class of interactive wearable cognitive assistance  applications is poised to become one of the key demonstrators of edge  computing infrastructure. In this paper, we design seven such  applications and evaluate their performance in terms of latency across a  range of edge computing configurations, mobile hardware, and wireless  networks,

Computer vision based technologies have seen widespread adoption over  the recent years. This use is not limited to the rapid adoption of  facial recognition technology but extends to facial expression  recognition, scene recognition and more. These developments raise  privacy concerns and call for novel solutions to ensure adequate user  awareness,

Situational awareness involves the timely acquisition of knowledge about  real-world events, distillation of those events into higher-level  conceptual constructs, and their synthesis into a coherent  context-sensitive view. We explore how convergent trends in video  sensing, crowd sourcing and edge computing can be harnessed to create a  shared real-time information system

We present OpenFace, our new open-source face recognition system that  approaches state-of-the-art accuracy. Integrating OpenFace with  inter-frame tracking, we build RTFace, a mechanism for denaturing video  streams that selectively blurs faces according to specified policies at  full frame rates. This enables privacy management for live video  analytics while providing a

As video cameras proliferate, the ability to scalably capture and search their data becomes important. Scalability is improved by performing video analytics on cloudlets at the edge of the Internet, and only shipping extracted index information and meta-data to the cloud. In this setting, we describe interactive data exploration (IDE)

Industry investment and research interest in edge computing, in which computing and storage nodes are placed at the Internet's edge in close proximity to mobile devices or sensors, have grown dramatically in recent years. This emerging technology promises to deliver highly responsive cloud services for mobile computing, scalability and privacy-policy

The convergence of mobile computing and cloud computing enables new mobile applications that are both resource-intensive and interactive. For these applications, end-to-end network bandwidth and latency matter greatly when cloud resources are used to augment the computational power and battery life of a mobile device. This dissertation designs and implements

Computational offloading services at the edge of the Internet for mobile devices are becoming a reality. Using a wide range of mobile applications, we explore how such infrastructure improves latency and energy consumption relative to the cloud. We present experimental results from WiFi and 4G LTE networks that confirm substantial