5,609 research outputs found

    ADN: An Information-Centric Networking Architecture for the Internet of Things

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    Forwarding data by name has been assumed to be a necessary aspect of an information-centric redesign of the current Internet architecture that makes content access, dissemination, and storage more efficient. The Named Data Networking (NDN) and Content-Centric Networking (CCNx) architectures are the leading examples of such an approach. However, forwarding data by name incurs storage and communication complexities that are orders of magnitude larger than solutions based on forwarding data using addresses. Furthermore, the specific algorithms used in NDN and CCNx have been shown to have a number of limitations. The Addressable Data Networking (ADN) architecture is introduced as an alternative to NDN and CCNx. ADN is particularly attractive for large-scale deployments of the Internet of Things (IoT), because it requires far less storage and processing in relaying nodes than NDN. ADN allows things and data to be denoted by names, just like NDN and CCNx do. However, instead of replacing the waist of the Internet with named-data forwarding, ADN uses an address-based forwarding plane and introduces an information plane that seamlessly maps names to addresses without the involvement of end-user applications. Simulation results illustrate the order of magnitude savings in complexity that can be attained with ADN compared to NDN.Comment: 10 page

    Input Fast-Forwarding for Better Deep Learning

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    This paper introduces a new architectural framework, known as input fast-forwarding, that can enhance the performance of deep networks. The main idea is to incorporate a parallel path that sends representations of input values forward to deeper network layers. This scheme is substantially different from "deep supervision" in which the loss layer is re-introduced to earlier layers. The parallel path provided by fast-forwarding enhances the training process in two ways. First, it enables the individual layers to combine higher-level information (from the standard processing path) with lower-level information (from the fast-forward path). Second, this new architecture reduces the problem of vanishing gradients substantially because the fast-forwarding path provides a shorter route for gradient backpropagation. In order to evaluate the utility of the proposed technique, a Fast-Forward Network (FFNet), with 20 convolutional layers along with parallel fast-forward paths, has been created and tested. The paper presents empirical results that demonstrate improved learning capacity of FFNet due to fast-forwarding, as compared to GoogLeNet (with deep supervision) and CaffeNet, which are 4x and 18x larger in size, respectively. All of the source code and deep learning models described in this paper will be made available to the entire research communityComment: Accepted in the 14th International Conference on Image Analysis and Recognition (ICIAR) 2017, Montreal, Canad

    Benets of tight coupled architectures for the integration of GNSS receiver and Vanet transceiver

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    Vehicular adhoc networks (VANETs) are one emerging type of networks that will enable a broad range of applications such as public safety, traffic management, traveler information support and entertain ment. Whether wireless access may be asynchronous or synchronous (respectively as in the upcoming IEEE 8021.11p standard or in some alternative emerging solutions), a synchronization among nodes is required. Moreover, the information on position is needed to let vehicular services work and to correctly forward the messages. As a result, timing and positioning are a strong prerequisite of VANETs. Also the diffusion of enhanced GNSS Navigators paves the way to the integration between GNSS receivers and VANET transceiv ers. This position paper presents an analysis on potential benefits coming from a tightcoupling between the two: the dissertation is meant to show to what extent Intelligent Transportation System (ITS) services could benefit from the proposed architectur

    A low-energy rate-adaptive bit-interleaved passive optical network

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    Energy consumption of customer premises equipment (CPE) has become a serious issue in the new generations of time-division multiplexing passive optical networks, which operate at 10 Gb/s or higher. It is becoming a major factor in global network energy consumption, and it poses problems during emergencies when CPE is battery-operated. In this paper, a low-energy passive optical network (PON) that uses a novel bit-interleaving downstream protocol is proposed. The details about the network architecture, protocol, and the key enabling implementation aspects, including dynamic traffic interleaving, rate-adaptive descrambling of decimated traffic, and the design and implementation of a downsampling clock and data recovery circuit, are described. The proposed concept is shown to reduce the energy consumption for protocol processing by a factor of 30. A detailed analysis of the energy consumption in the CPE shows that the interleaving protocol reduces the total energy consumption of the CPE significantly in comparison to the standard 10 Gb/s PON CPE. Experimental results obtained from measurements on the implemented CPE prototype confirm that the CPE consumes significantly less energy than the standard 10 Gb/s PON CPE
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