Brandon Amos

Edge Analytics in the Internet of Things

High-data-rate sensors, such as video cameras, are becoming ubiquitous in the Internet of Things. This article describes GigaSight, an Internet-scale repository of crowd-sourced video content, with strong enforcement of privacy preferences and access controls. The GigaSight architecture is a federated system of VM-based cloudlets that perform video analytics at the edge of the Internet, thus reducing the demand for ingress bandwidth into the cloud. Denaturing, which is an owner-specific reduction in fidelity of video content to preserve privacy, is one form of analytics on cloudlets. Content-based indexing for search is another form of cloudlet-based analytics. This article is part of a special issue on smart spaces.

Quantifying the Impact of Edge Computing on Mobile Applications

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 wins from edge computing for highly interactive mobile applications.

Privacy Mediators: Helping IoT Cross the Chasm

Unease over data privacy will retard consumer acceptance of IoT deployments. The primary source of discomfort is a lack of user control over raw data that is streamed directly from sensors to the cloud. This is a direct consequence of the over-centralization of todays cloud-based IoT hub designs. We propose a solution that interposes a locally-controlled software component called a privacy mediator on every raw sensor stream. Each mediator is in the same administrative domain as the sensors whose data is being collected, and dynamically enforces the current privacy policies of the owners of the sensors or mobile users within the domain. This solution necessitates a logical point of presence for mediators within the administrative boundaries of each organization. Such points of presence are provided by cloudlets, which are small locally-administered data centers at the edge of the Internet that can support code mobility. The use of cloudlet-based mediators aligns well with natural personal and organizational boundaries of trust and responsibility.

Bad Parts: Are Our Manufacturing Systems at Risk of Silent Cyberattacks?

Recent cyberattacks have highlighted the risk of physical equipment operating outside designed tolerances to produce catastrophic failures. A related threat is cyberattacks that change the design and manufacturing of a machines part, such as an automobile brake component, so it no longer functions properly. These risks stem from the lack of cyber-physical models to identify ongoing attacks as well as the lack of rigorous application of known cybersecurity best practices. To protect manufacturing processes in the future, research will be needed on a number of critical cyber-physical manufacturing security topics.

The Case for Offload Shaping

When offloading computation from a mobile device, we show that it can pay to perform additional on-device work in order to reduce the offloading workload. We call this offload shaping, and demonstrate its application at many different levels of abstraction using a variety of techniques. We show that offload shaping can produce significant reduction in resource demand, with little loss of application-level fidelity.

Early Implementation Experience with Wearable Cognitive Assistance Applications

A cognitive assistance application combines a wearable device such as Google Glass with cloudlet processing to provide step-by-step guidance on a complex task. In this paper, we focus on user assistance for narrow and well-defined tasks that require specialized knowledge and/or skills. We describe proof-of-concept implementations for four different tasks: assembling 2D Lego models, freehand sketching, playing ping-pong, and recommending context-relevant YouTube tutorials. We then reflect on the difficulties we faced in building these applications, and suggest future research that could simplify the creation of similar applications.

A Scalable and Privacy-Aware IoT Service for Live Video Analytics

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 secure approach for handling retrospective policy exceptions. Finally, we present a scalable, privacy-aware architecture for large camera networks using RTFace.

You can teach elephants to dance: agile VM handoff for edge computing

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 to 25 Mbps, VM handoff migrates a running 8 GB VM in about a minute, with a downtime of a few tens of seconds. By dynamically adapting to varying network bandwidth and processing load, VM handoff is more than an order of magnitude faster than live migration at those bandwidths.

Enabling Live Video Analytics with a Scalable and Privacy-Aware Framework

We show how to build the components of a privacy-aware, live video analytics ecosystem from the bottom up, starting with OpenFace, our new open-source face recognition system that approaches state-of-the-art accuracy. Integrating OpenFace with interframe 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 secure approach for handling retrospective policy exceptions. Finally, we present a scalable, privacy-aware architecture for large camera networks using RTFace and show how it can be an enabler for a vibrant ecosystem and marketplace of privacy-aware video streams and analytics services.

An empirical study of latency in an emerging class of edge computing applications for wearable cognitive assistance

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, including 4G LTE. We also devise a novel multi-algorithm approach that leverages temporal locality to reduce end-to-end latency by 60% to 70%, without sacrificing accuracy. Finally, we derive target latencies for our applications, and show that edge computing is crucial to meeting these targets.