440,887 research outputs found

    Crypton: CRYptographic Prefixes for Route Optimization in NEMO

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    Proceedings of: 2010 IEEE International Conference on Communications (ICC 2010), 23-27 May, 2010, Cape Town, South AfricaThe aviation community is in the process of designing the next generation Aeronautical Telecommunications Network (ATN), based on Internet standards, to provide airground communications for the aircraft. Support for mobile networks in the current Internet architecture is provided by the NEtwork Mobility (NEMO) protocol. As currently defined, NEMO Basic Support protocol lacks of Route Optimization support which is an essential requirement for its adoption as part of the next generation ATN. This paper presents a novel security tool, the Crypto Prefixes, and their application to the Route Optimization in Nemo (CRYPTRON). The Crypto Prefixes are IPv6 prefixes with embedded cryptographic information that enable the Mobile Network Prefix proof-of ownership without any centralized trust infrastructure. In CRYPTRON, the Crypto Prefixes are used to protect the establishment of the bindings on the Correspondent Nodes for the whole Mobile Network PrefixEuropean Community's Seventh Framework ProgramPublicad

    Extending Ambient Intelligence to the Internet of Things: New Challenges for QoC Management

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    International audienceQuality of Context (QoC) awareness is recognized as a key point for the success of context-aware computing solutions. At a time where the Internet of Things, Cloud Computing, and Ambient Intelligence paradigms bring together new opportunities for more complex context computation, the next generation of Multiscale Distributed Context Managers (MDCM) is facing new challenges concerning QoC management. This paper presents how our QoCIM framework can help application developers to manage the whole QoC life-cycle by providing genericity, openness and uniformity. Its usages are illustrated, both at design time and at runtime, in the case of an urban pollution context- and QoC-aware scenario

    CloVR: A virtual machine for automated and portable sequence analysis from the desktop using cloud computing

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    Next-generation sequencing technologies have decentralized sequence acquisition, increasing the demand for new bioinformatics tools that are easy to use, portable across multiple platforms, and scalable for high-throughput applications. Cloud computing platforms provide on-demand access to computing infrastructure over the Internet and can be used in combination with custom built virtual machines to distribute pre-packaged with pre-configured software. We describe the Cloud Virtual Resource, CloVR, a new desktop application for push-button automated sequence analysis that can utilize cloud computing resources. CloVR is implemented as a single portable virtual machine (VM) that provides several automated analysis pipelines for microbial genomics, including 16S, whole genome and metagenome sequence analysis. The CloVR VM runs on a personal computer, utilizes local computer resources and requires minimal installation, addressing key challenges in deploying bioinformatics workflows. In addition CloVR supports use of remote cloud computing resources to improve performance for large-scale sequence processing. In a case study, we demonstrate the use of CloVR to automatically process next-generation sequencing data on multiple cloud computing platforms. The CloVR VM and associated architecture lowers the barrier of entry for utilizing complex analysis protocols on both local single- and multi-core computers and cloud systems for high throughput data processing.https://doi.org/10.1186/1471-2105-12-35

    Performance analysis of probabilistic packet marking in IPv6

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    Probabilistic packet marking (PPM) has received considerable attention as an IP traceback approach against distributed Denial-of-Service attack, which is one of the most challenging security threat in the Internet. PPM is a technique that seeks to identify the source of such attacks by marking individual packets with portion of the attack path, and then relies on the volume of attack traffic generated to ensure that the whole path can be reconstructed. However, modifying the identification field in the IPv4 packet header to mark packet incurs backward incompatibility for IP fragmented packets. In this paper, we address this issue and analyze the viability of PPM under the next-generation Internet Protocol, IPv6. In doing so, we consider the flaws inherent to IPv4 implementations that limit their backward compatibility, and demonstrate how these shortcomings can be avoided in IPv6. We show that the Flow Label field in the IPv6 datagram header can be safely and effectively overloaded to implement PPM schemes, and present simulation results verifying the applicability and efficiency of this approach. © 2007 Elsevier B.V. All rights reserved

    Rendering Secure and Trustworthy Edge Intelligence in 5G-Enabled IIoT using Proof of Learning Consensus Protocol

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    Industrial Internet of Things (IIoT) and fifth generation (5G) network have fueled the development of Industry 4.0 by providing an unparalleled connectivity and intelligence to ensure timely (or real time) and optimal decision making. Under this umbrella, the edge intelligence is ready to propel another ripple in the industrial growth by ensuring the next generation of connectivity and performance. With the recent proliferation of blockchain, edge intelligence enters a new era, where each edge trains the local learning model, then interconnecting the whole learning models in a distributed blockchain manner, known as blockchain-assisted federated learning. However, it is quiet challenging task to provide secure edge intelligence in 5G-enabled IIoT environment alongside ensuring latency and throughput. In this paper, we propose a Proof-of-Learning (PoL) consensus protocol that considers the reputation opinion for edge blockchain to ensure secure and trustworthy edge intelligence in IIoT. This protocol fetches each edge's reputation opinion by executing a smart contract, and partly adopts the winner's learning model according to its reputation opinion. By quantitative performance analysis and simulation experiments, the proposed scheme demonstrates the superior performance in contrast to the traditional counterparts

    Harnessing Technology: preliminary identification of trends affecting the use of technology for learning

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    Mobile services in Estonia

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