6,303 research outputs found

    CoAP congestion control for the Internet of Things

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    “© © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.” August Betzler, Javier Isern, Carles Gomez, Ilker Demirkol, Josep Paradells, "Experimental evaluation of congestion control for CoAP communications without end-to-end reliability", Ad Hoc Networks, pp. , 2016, ISSN 15708705. DOI: 10.1109/MCOM.2016.7509394CoAP is a lightweight RESTful application layer protocol devised for the IoT. Operating on top of UDP, CoAP must handle congestion control by itself. The core CoAP specification defines a basic congestion control mechanism, but it is not capable of adapting to network conditions. However, IoT scenarios exhibit significant resource constraints, which pose new challenges on the design of congestion control mechanisms. In this article we present CoCoA, an advanced congestion control mechanism for CoAP being standardized by the Internet Engineering Task Force CoRE working group. CoCoA introduces a novel round-trip time estimation technique, together with a variable backoff factor and aging mechanisms in order to provide dynamic and controlled retransmission timeout adaptation suitable for the peculiarities of IoT communications. We conduct a comparative performance analysis of CoCoA and a variety of alternative algorithms including state-of-the-art mechanisms developed for TCP. The study is based on experiments carried out in real testbeds. Results show that, in contrast to the alternative methods considered, CoCoA consistently outperforms the default CoAP congestion control mechanism in all evaluated scenarios.Peer ReviewedPostprint (author's final draft

    FavorQueue: A parameterless active queue management to improve TCP traffic performance

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    This paper presents and analyzes the implementation of a novel active queue management (AQM) named FavorQueue that aims to improve delay transfer of short lived TCP flows over best-effort networks. The idea is to dequeue packets that do not belong to a flow previously enqueued first. The rationale is to mitigate the delay induced by long-lived TCP flows over the pace of short TCP data requests and to prevent dropped packets at the beginning of a connection and during recovery period. Although the main target of this AQM is to accelerate short TCP traffic, we show that FavorQueue does not only improve the performance of short TCP traffic but also improves the performance of all TCP traffic in terms of drop ratio and latency whatever the flow size. In particular, we demonstrate that FavorQueue reduces the loss of a retransmitted packet, decreases the number of dropped packets recovered by RTO and improves the latency up to 30% compared to DropTail. Finally, we show that this scheme remains compliant with recent TCP updates such as the increase of the initial slow-start value

    Evaluation of error control mechanisms for 802.11b multicast transmissions

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    This article first presents several packet loss profiles collected during 802.11b multicast transmissions carried out under variable reception conditions (mobile and fixed receivers). Then, an original approach consisting in mapping a posteriori some error control mechanisms over these observations is presented. This approach allows to evaluate the performance of these mechanisms according to their parameters and various channel properties. It is shown in particular that relatively simple mechanisms based on retransmissions and/or error correcting codes of small length achieve very good performance in this context (92% of the best performance)

    A two-level Markov model for packet loss in UDP/IP-based real-time video applications targeting residential users

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    The packet loss characteristics of Internet paths that include residential broadband links are not well understood, and there are no good models for their behaviour. This compli- cates the design of real-time video applications targeting home users, since it is difficult to choose appropriate error correction and concealment algorithms without a good model for the types of loss observed. Using measurements of residential broadband networks in the UK and Finland, we show that existing models for packet loss, such as the Gilbert model and simple hidden Markov models, do not effectively model the loss patterns seen in this environment. We present a new two-level Markov model for packet loss that can more accurately describe the characteristics of these links, and quantify the effectiveness of this model. We demonstrate that our new packet loss model allows for improved application design, by using it to model the performance of forward error correction on such links

    Max-min Fairness in 802.11 Mesh Networks

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    In this paper we build upon the recent observation that the 802.11 rate region is log-convex and, for the first time, characterise max-min fair rate allocations for a large class of 802.11 wireless mesh networks. By exploiting features of the 802.11e/n MAC, in particular TXOP packet bursting, we are able to use this characterisation to establish a straightforward, practically implementable approach for achieving max-min throughput fairness. We demonstrate that this approach can be readily extended to encompass time-based fairness in multi-rate 802.11 mesh networks

    Profiling user activities with minimal traffic traces

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    Understanding user behavior is essential to personalize and enrich a user's online experience. While there are significant benefits to be accrued from the pursuit of personalized services based on a fine-grained behavioral analysis, care must be taken to address user privacy concerns. In this paper, we consider the use of web traces with truncated URLs - each URL is trimmed to only contain the web domain - for this purpose. While such truncation removes the fine-grained sensitive information, it also strips the data of many features that are crucial to the profiling of user activity. We show how to overcome the severe handicap of lack of crucial features for the purpose of filtering out the URLs representing a user activity from the noisy network traffic trace (including advertisement, spam, analytics, webscripts) with high accuracy. This activity profiling with truncated URLs enables the network operators to provide personalized services while mitigating privacy concerns by storing and sharing only truncated traffic traces. In order to offset the accuracy loss due to truncation, our statistical methodology leverages specialized features extracted from a group of consecutive URLs that represent a micro user action like web click, chat reply, etc., which we call bursts. These bursts, in turn, are detected by a novel algorithm which is based on our observed characteristics of the inter-arrival time of HTTP records. We present an extensive experimental evaluation on a real dataset of mobile web traces, consisting of more than 130 million records, representing the browsing activities of 10,000 users over a period of 30 days. Our results show that the proposed methodology achieves around 90% accuracy in segregating URLs representing user activities from non-representative URLs
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