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Author

Yu Xiao

Bio: Yu Xiao is an academic researcher from Aalto University. The author has contributed to research in topics: Mobile computing & Mobile device. The author has an hindex of 22, co-authored 93 publications receiving 2081 citations. Previous affiliations of Yu Xiao include Carnegie Mellon University & Helsinki University of Technology.


Papers
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Journal ArticleDOI
TL;DR: GigaSight is described, an Internet-scale repository of crowd-sourced video content, with strong enforcement of privacy preferences and access controls, and 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.
Abstract: 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.

406 citations

Proceedings ArticleDOI
25 Jun 2013
TL;DR: The experiments reveal the bottlenecks for video upload, denaturing, indexing, and content-based search and provide insight on how parameters such as frame rate and resolution impact scalability.
Abstract: We propose a scalable Internet system for continuous collection of crowd-sourced video from devices such as Google Glass. Our hybrid cloud architecture, GigaSight, is effectively a Content Delivery Network (CDN) in reverse. It achieves scalability by decentralizing the collection infrastructure using cloudlets based on virtual machines~(VMs). Based on time, location, and content, privacy sensitive information is automatically removed from the video. This process, which we refer to as denaturing, is executed in a user-specific VM on the cloudlet. Users can perform content-based searches on the total catalog of denatured videos. Our experiments reveal the bottlenecks for video upload, denaturing, indexing, and content-based search. They also provide insight on how parameters such as frame rate and resolution impact scalability.

170 citations

Journal ArticleDOI
TL;DR: This paper proposes Folo, a novel solution for latency and quality optimized task allocation in vehicular fog computing (VFC), and proposes an event-triggered dynamic task allocation framework using linear programming-based optimization and binary particle swarm optimization.
Abstract: With the emerging vehicular applications, such as real-time situational awareness and cooperative lane change, there exist huge demands for sufficient computing resources at the edge to conduct time-critical and data-intensive tasks. This paper proposes Folo, a novel solution for latency and quality optimized task allocation in vehicular fog computing (VFC). Folo is designed to support the mobility of vehicles, including vehicles that generate tasks and the others that serve as fog nodes. Considering constraints on service latency, quality loss, and fog capacity, the process of task allocation across stationary and mobile fog nodes is formulated into a joint optimization problem. This task allocation in VFC is known as a nondeterministic polynomial-time hard problem. In this paper, we present the task allocation to fog nodes as a bi-objective minimization problem, where a tradeoff is maintained between the service latency and quality loss. Specifically, we propose an event-triggered dynamic task allocation framework using linear programming-based optimization and binary particle swarm optimization. To assess the effectiveness of Folo, we simulated the mobility of fog nodes at different times of a day based on real-world taxi traces and implemented two representative tasks, including video streaming and real-time object recognition. Simulation results show that the task allocation provided by Folo can be adjusted according to actual requirements of the service latency and quality, and achieves higher performance compared with naive and random fog node selection. To be more specific, Folo shortens the average service latency by up to 27% while reducing the quality loss by up to 56%.

159 citations

Journal ArticleDOI
TL;DR: This article introduces the terminologies and describes the power modeling and measurement methodologies applied in model-based energy profiling, classify the profilers according to their implementation and deployment strategies, and compare the profiling capabilities and performance between different types.
Abstract: Software energy profilers are the tools to measure the energy consumption of mobile devices, applications running on those devices, and various hardware components. They adopt different modeling and measurement techniques. In this article, we aim to review a wide range of such energy profilers for mobile devices. First, we introduce the terminologies and describe the power modeling and measurement methodologies applied in model-based energy profiling. Next, we classify the profilers according to their implementation and deployment strategies, and compare the profiling capabilities and performance between different types. Finally, we point out their limitations and the corresponding challenges.

113 citations

Journal ArticleDOI
TL;DR: The characteristics of MCS are analyzed, its security threats are identified, and essential requirements on a secure, privacy-preserving, and trustworthy MCS system are outlined.
Abstract: With the popularity of sensor-rich mobile devices (e.g., smart phones and wearable devices), mobile crowdsourcing (MCS) has emerged as an effective method for data collection and processing. Compared with traditional wireless sensor networking, MCS holds many advantages such as mobility, scalability, cost-efficiency, and human intelligence. However, MCS still faces many challenges with regard to security, privacy, and trust. This paper provides a survey of these challenges and discusses potential solutions. We analyze the characteristics of MCS, identify its security threats, and outline essential requirements on a secure, privacy-preserving, and trustworthy MCS system. Further, we review existing solutions based on these requirements and compare their pros and cons. Finally, we point out open issues and propose some future research directions.

108 citations


Cited by
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Journal ArticleDOI
TL;DR: This paper provides an up-to-date picture of CloudIoT applications in literature, with a focus on their specific research challenges, and identifies open issues and future directions in this field, which it expects to play a leading role in the landscape of the Future Internet.

1,880 citations

Journal ArticleDOI
TL;DR: A five-video playlist demonstrating proof-of-concept implementations for three tasks: assembling 2D Lego models, freehand sketching, and playing Ping-Pong is demonstrated.
Abstract: 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 enforcement for the Internet of Things, and the ability to mask transient cloud outages. The web extra at www.youtube.com/playlist?list=PLmrZVvFtthdP3fwHPy_4d61oDvQY_RBgS includes a five-video playlist demonstrating proof-of-concept implementations for three tasks: assembling 2D Lego models, freehand sketching, and playing Ping-Pong.

1,690 citations

Journal ArticleDOI
TL;DR: This paper analyzes the MEC reference architecture and main deployment scenarios, which offer multi-tenancy support for application developers, content providers, and third parties, and elaborates further on open research challenges.
Abstract: Multi-access edge computing (MEC) is an emerging ecosystem, which aims at converging telecommunication and IT services, providing a cloud computing platform at the edge of the radio access network MEC offers storage and computational resources at the edge, reducing latency for mobile end users and utilizing more efficiently the mobile backhaul and core networks This paper introduces a survey on MEC and focuses on the fundamental key enabling technologies It elaborates MEC orchestration considering both individual services and a network of MEC platforms supporting mobility, bringing light into the different orchestration deployment options In addition, this paper analyzes the MEC reference architecture and main deployment scenarios, which offer multi-tenancy support for application developers, content providers, and third parties Finally, this paper overviews the current standardization activities and elaborates further on open research challenges

1,351 citations

Proceedings ArticleDOI
04 Nov 2009
TL;DR: TailEnder is developed, a protocol that reduces energy consumption of common mobile applications and aggressively prefetches several times more data and improves user-specified response times while consuming less energy.
Abstract: In this paper, we present a measurement study of the energy consumption characteristics of three widespread mobile networking technologies: 3G, GSM, and WiFi. We find that 3G and GSM incur a high tail energy overhead because of lingering in high power states after completing a transfer. Based on these measurements, we develop a model for the energy consumed by network activity for each technology.Using this model, we develop TailEnder, a protocol that reduces energy consumption of common mobile applications. For applications that can tolerate a small delay such as e-mail, TailEnder schedules transfers so as to minimize the cumulative energy consumed meeting user-specified deadlines. We show that the TailEnder scheduling algorithm is within a factor 2x of the optimal and show that any online algorithm can at best be within a factor 1.62x of the optimal. For applications like web search that can benefit from prefetching, TailEnder aggressively prefetches several times more data and improves user-specified response times while consuming less energy. We evaluate the benefits of TailEnder for three different case study applications - email, news feeds, and web search - based on real user logs and show significant reduction in energy consumption in each case. Experiments conducted on the mobile phone show that TailEnder can download 60% more news feed updates and download search results for more than 50% of web queries, compared to using the default policy.

1,239 citations