High data rate sensors such as video cameras are becoming ubiquitous in the Internet of Things. This paper 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 demand for ingress bandwidth into the cloud. Denaturing, which is the 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.
We describe the architecture and prototype implementation of an assistive system based on Google Glass devices for users in cognitive decline. It combines the first-person image capture and sensing capabilities of Glass with cloud processing to perform real-time scene interpretation. The system architecture is multi-tiered. It offers tight end-to-end latency bounds on compute-intensive operations, while addressing concerns such as limited battery capacity and limited processing capability of wearable devices. The system gracefully degrades services in the face of network failures and unavailability of distant architectural tiers. Report Documentation PageForm Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.
In recent years, there has been a rapid and wide spread of nontraditional computing platforms, especially mobile and portable computing devices. As applications become increasingly sophisticated and processing power increases, the most serious limitation on these devices is the available battery life. Dynamic Voltage Scaling (DVS) has been a key technique in exploiting the hardware characteristics of processors to reduce energy dissipation by lowering the supply voltage and operating frequency. The DVS algorithms are shown to be able to make dramatic energy savings while providing the necessary peak computation power in general-purpose systems. However, for a large class of applications in embedded real-time systems like cellular phones and camcorders, the variable operating frequency interferes with their deadline guarantee mechanisms, and DVS in this context, despite its growing importance, is largely overlooked/under-developed. To provide real-time guarantees, DVS must consider deadlines and periodicity of real-time tasks, requiring integration with the real-time scheduler. In this paper, we present a class of novel algorithms called real-time DVS (RT-DVS) that modify the OS's real-time scheduler and task management service to provide significant energy savings while maintaining real-time deadline guarantees. We show through simulations and a working prototype implementation that these RT-DVS algorithms closely approach the theoretical lower bound on energy consumption, and can easily reduce energy consumption 20% to 40% in an embedded real-time system. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies boar this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. SOSP01 Banff, Canada ful microprocessors running sophisticated, intelligent control software in a vast array of devices including digital camcorders, cellular phones, and portable medical devices.
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. CCS CONCEPTS • Human-centered computing → Empirical studies in ubiquitous and mobile computing; Ubiquitous and mobile computing systems and tools; • Software and its engineering → Distributed systems organizing principles; • Networks → Wireless access points, base stations and infrastructure; Mobile networks; Network measurement; • Computer systems organization → Real-time system architecture;
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