IEEE Transactions on Pattern Analysis and Machine Intelligence

A review of smart home applications based on Internet of Things

As user demand scales for intelligent personal assistants (IPAs) such as Apple's Siri, Google's Google Now, and Microsoft's Cortana, we are approaching the computational limits of current datacenter architectures. It is an open question how future server architectures should evolve to enable this emerging class of applications, and the lack of an open-source IPA workload is an obstacle in addressing this question. In this paper, we present the design of Sirius, an open end-to-end IPA web-service application that accepts queries in the form of voice and images, and responds with natural language. We then use this workload to investigate the implications of four points in the design space of future accelerator-based server architectures spanning traditional CPUs, GPUs, manycore throughput co-processors, and FPGAs. To investigate future server designs for Sirius, we decompose Sirius into a suite of 7 benchmarks (Sirius Suite) comprising the computationally intensive bottlenecks of Sirius. We port Sirius Suite to a spectrum of accelerator platforms and use the performance and power trade-offs across these platforms to perform a total cost of ownership (TCO) analysis of various server design points. In our study, we find that accelerators are critical for the future scalability of IPA services. Our results show that GPU- and FPGA-accelerated servers improve the query latency on average by 10x and 16x. For a given throughput, GPU- and FPGA-accelerated servers can reduce the TCO of datacenters by 2.6x and 1.4x, respectively.

http://www.crosslayers.org/sirius-clone/wp-content/papercite-data/pdf/hauswald15asplos.pdf

Sirius: An Open End-to-End Voice and Vision Personal Assistant and Its Implications for Future Warehouse Scale Computers

The Internet of Things (IoT) is an ecosystem that integrates physical objects, software and hardware to interact with each other. Aging of population, shortage of healthcare resources, and rising medical costs make IoT-based technologies necessary to be tailored to address these challenges in healthcare. This systematic literature review has been conducted to determine the main application area of IoT in healthcare, components of IoT architecture in healthcare, most important technologies in IoT, characteristics of cloud-based architecture, security and interoperability issues in IoT architecture and effects, and challenges of IoT in healthcare. Sixty relevant papers, published between 2000 and 2016, were reviewed and analyzed. This analysis revealed that home healthcare service was one of the main application areas of IoT in healthcare. Cloud-based architecture, by providing great flexibility and scalability, has been deployed in most of the reviewed studies. Communication technologies including wireless fidelity (Wi-Fi), Bluetooth, radio-frequency identification (RFID), ZigBee, and Low-Power Wireless Personal Area Networks (LoWPAN) were frequently used in different IoT models. The studies regarding the security and interoperability issues in IoT architecture in health are still low in number. With respect to the most important effects of IoT in healthcare, these included ability of information exchange, decreasing stay of hospitalization and healthcare costs. The main challenges of IoT in healthcare were security and privacy issues.

The application of internet of things in healthcare: a systematic literature review and classification

HTTP streaming has become a cost-effective means for multimedia delivery nowadays. For adaptivity to networks and terminals, a provider should generate multiple representations of an original video as well as the related metadata. Recently, there have been various adaptation methods to support adaptive HTTP streaming. In this paper, we investigate typical adaptation methods in the context of live video streaming. We first discuss the trade-off among typical adaptation methods. The evaluation and comparison are then carried out not only in terms of bitrate and buffer behaviors but also in terms of the perceptual impact on end users. It is found that the perceptual impact depends not only on adaptation method but also on the content itself. We also show that the preparation of representation sets may affect the behaviors of some adaptation methods.

An Evaluation of Bitrate Adaptation Methods for HTTP Live Streaming

Currently, more and more 360-degree videos (or 360 videos for short) are being provided via the Internet. This kind of videos can render a virtual reality (VR) environment via a head-mounted display (HMD). However, understanding the quality of experience (QoE) of 360 videos is a big challenge because user experience in VR is a very complex phenomenon. In this paper, the QoE of 360 videos is considered in terms of four aspects, namely perceptual quality, presence, acceptability, and cybersickness. Subjective tests are designed to investigate the influences of important factors including encoding parameters, content characteristics, and device types on QoE aspects. In addition, a comparison of perceptual quality and acceptability between VR and non-VR rendering modes, which refer to watching 360 videos with and without using an HMD respectively, is also made in this study. To the best of our knowledge, this is the first study that covers these four QoE aspects and a large number of influence factors.

A subjective study on QoE of 360 video for VR communication

360 videos are becoming more and more popular on video streaming platforms. However, a good quality metric for 360 videos is still an open issue. In this work, we investigate both objective and subjective quality metrics for 360 videos. The goals are to understand the perceived quality range provided by existing mobile 360 videos and, especially, to identify appropriate objective quality metrics for 360 video communications. To that end, a subjective test is conducted in this study. Then, the relationship between objective quality and subjective quality is investigated. Especially, ten objective quality measures are computed, considering the coding distortion measurement, cross-format distortion measurement, and end-to-end distortion measurement. It is found that most of the objective quality measures are well correlated with subjective quality. Also, among the evaluated quality measures, PSNR is shown to be the most appropriate for 360 video communications.

An evaluation of quality metrics for 360 videos

Live and on-demand video streaming has recently contributed a significant part of the Internet traffic. In order to guarantee the best possible user experience, multimedia service is required to support large fluctuations in different network conditions. One of the cost effective means for multimedia delivery nowadays is HTTP streaming. The key challenge of this technology is how to change adaptively video bitrates according to network variation and client buffer while bestowing upon users the best possible video quality. In this paper, we propose a novel rate-adaptation method on client side, which allows clients to estimate buffer level in the future, and thus achieving a balance between the need for high video quality and buffer stability. The experiments show that our method performs well under varying network conditions.

Buffer-based bitrate adaptation for adaptive HTTP streaming

Modeling and experimenting combined smart sleep and power scaling algorithms in energy-aware data center networks

In the context of HTTP Adaptive Streaming (HAS), a main challenge is how to evaluate the quality of experience (QoE) of a session. In general, the QoE of a streaming session is affected by three key factors, namely initial delay, varying perceptual quality, and interruptions. In this paper, in order to investigate the impacts of quality variations and interruptions on the QoE of a session, we propose to use histograms, which represent the frequency distributions of these recurrent factors in a session. The QoE of a session is modeled by pooling the statistics of the recurrent factors and the initial delay. Evaluation results show that the proposed model has a high prediction performance. Especially, the proposed model provides insights into the impacts of different influence factors, thus leading to suggestions to improve the quality of streaming video.

Nam Pham Ngoc

Papers

A subjective study on QoE of 360 video for VR communication

An evaluation of quality metrics for 360 videos

Buffer-based bitrate adaptation for adaptive HTTP streaming

Modeling and experimenting combined smart sleep and power scaling algorithms in energy-aware data center networks

A Multi-Factor QoE Model for Adaptive Streaming over Mobile Networks