6,702 research outputs found

    Saving Energy in Mobile Devices for On-Demand Multimedia Streaming -- A Cross-Layer Approach

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    This paper proposes a novel energy-efficient multimedia delivery system called EStreamer. First, we study the relationship between buffer size at the client, burst-shaped TCP-based multimedia traffic, and energy consumption of wireless network interfaces in smartphones. Based on the study, we design and implement EStreamer for constant bit rate and rate-adaptive streaming. EStreamer can improve battery lifetime by 3x, 1.5x and 2x while streaming over Wi-Fi, 3G and 4G respectively.Comment: Accepted in ACM Transactions on Multimedia Computing, Communications and Applications (ACM TOMCCAP), November 201

    Seamless Dynamic Adaptive Streaming in LTE/Wi-Fi Integrated Network under Smartphone Resource Constraints

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    Exploiting both LTE and Wi-Fi links simultaneously enhances the performance of video streaming services in a smartphone. However, it is challenging to achieve seamless and high quality video while saving battery energy and LTE data usage to prolong the usage time of a smartphone. In this paper, we propose REQUEST, a video chunk request policy for Dynamic Adaptive Streaming over HTTP (DASH) in a smartphone, which can utilize both LTE and Wi-Fi. REQUEST enables seamless DASH video streaming with near optimal video quality under given budgets of battery energy and LTE data usage. Through extensive simulation and measurement in a real environment, we demonstrate that REQUEST significantly outperforms other existing schemes in terms of average video bitrate, rebuffering, and resource waste.Peer reviewe

    Hybrid Building/Floor Classification and Location Coordinates Regression Using A Single-Input and Multi-Output Deep Neural Network for Large-Scale Indoor Localization Based on Wi-Fi Fingerprinting

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    In this paper, we propose hybrid building/floor classification and floor-level two-dimensional location coordinates regression using a single-input and multi-output (SIMO) deep neural network (DNN) for large-scale indoor localization based on Wi-Fi fingerprinting. The proposed scheme exploits the different nature of the estimation of building/floor and floor-level location coordinates and uses a different estimation framework for each task with a dedicated output and hidden layers enabled by SIMO DNN architecture. We carry out preliminary evaluation of the performance of the hybrid floor classification and floor-level two-dimensional location coordinates regression using new Wi-Fi crowdsourced fingerprinting datasets provided by Tampere University of Technology (TUT), Finland, covering a single building with five floors. Experimental results demonstrate that the proposed SIMO-DNN-based hybrid classification/regression scheme outperforms existing schemes in terms of both floor detection rate and mean positioning errors.Comment: 6 pages, 4 figures, 3rd International Workshop on GPU Computing and AI (GCA'18

    QoE de streaming de vídeo em redes veiculares com multihoming

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    With the ever-increasing interest and availability of vehicular networks, it is important to study the Quality-of-Experience provided by these networks, which ultimately determines the general public perception and thus the overall user adoption. The broad Internet access, the evolution of user equipment, such as smartphones, tablets and personal computers, and the appearance of services like Youtube and Netflix, is leading the user content consumption to be more and more in the form of video streaming. Either motivated by safety or commercial applications, video streaming in such highly mobile environments offers multiple challenges. This dissertation evaluates the QoE of a multihoming communication strategy, supported simultaneously byWAVE and Wi-Fi, for increasing the reliability and performance of video streams in these environments. Furthermore, it also investigates how distinct network functionalities, such as multihoming load balance, buffering, and network metrics such as throughput and latency affect the overall QoE observed. The results obtained led to the proposal of a multihoming load balance policy for video applications based on access technologies, aiming to improve QoE. The overall results show that QoE improves by 7.5% using the proposed approach.Com o aumento contínuo do interesse e disponibilidade de redes veiculares, é importante agora estudar a Qualidade de Experiência fornecida por estas redes, que fundamentalmente determina a opinião e a percepção do público geral sobre um dado serviço. O vasto acesso à Internet, a evolução dos equipamentos, como os telemóveis atuais, tablets e computadores pessoais, e o aparecimento de serviços como o YouTube e o Netflix, está a fazer com que o conteúdo mais consumido seja cada vez mais em forma de streaming de vídeo. Quer seja motivado por aplicações de segurança ou comerciais, o streaming de vídeo em ambientes altamente móveis levanta vários desafios. Esta dissertação avalia a Qualidade de Experiência de técnicas de multihoming, permitindo o uso de diferentes tecnologias de comunicação, como o WAVE e o Wi-Fi, para aumentar a fiabilidade e desempenho de streams de vídeo nestes ambientes. Para além disso, investiga também como é que diferentes mecanismos de rede, como o balanceamento, multihoming e o buffering, e métricas como a taxa de transferência e latência, afetam a QoE observada. Os resultados obtidos levaram à proposta de uma política de divisão de tráfego para aplicações de vídeo baseada em tecnologias de acesso para situações de multihoming, visando uma melhoria da QoE do utilizador. Utilizando o método proposto, os resultados mostram que a experiência do utilizador tem uma melhoria de 7,5%.Mestrado em Engenharia de Computadores e Telemátic

    Dashbell: A Low-cost Smart Doorbell System for Home Use

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    Smart doorbells allow home owners to receive alerts when a visitor is at the door, see who the guest is, and communicate with the visitor from a smart device. They greatly improve people's life quality and contribute to the evolution of smart homes. However, the commercial smart doorbells are quite expensive, usually cost more than 190 US dollars, which is a substantial impediment on the pervasiveness of smart doorbells. To solve this problem, we introduce the Dashbell-a budget smart doorbell system for home use. It connects a WiFi-enabled device, the Amazon Dash Button, to a network and enables the home owner to answer the bell triggered by the dash button using a smartphone. The Dashbell system also enables fast fault detection and diagnosis due to its distributed framework.Comment: Accepted by IEEE PerCom 201
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