Maximizing the stable throughput of heterogeneous nodes under airtime fairness in a CSMA environment

The stability region of non-persistent CSMA is analyzed in a general heterogeneous network, where stations have different mean packet arrival rates, packet transmission times probability distributions and transmission probabilities. The considered model of CSMA captures the behavior of the well known CSMA/CA, at least as far as stability and throughput evaluation are concerned. The analysis is done both with and without collision detection. Given the characterization of the stability region, throughput-optimal transmission probabilities are identified under airtime fairness, establishing asymptotic upper and lower bounds of the maximum achievable stable throughput. The bounds turn out to be insensitive to the probability distribution of packet transmission times. Numerical results highlight that the obtained bounds are tight not only asymptotically, but also for essentially all values of the number of stations. The insight gained leads to the definition of a distributed adaptive algorithm to adjust the transmission probabilities of stations so as to attain the maximum stable throughput

Adaptive conflict-free optimization of rule sets for network security packet filtering devices

Packet filtering and processing rules management in firewalls and security gateways has become commonplace in increasingly complex networks. On one side there is a need to maintain the logic of high level policies, which requires administrators to implement and update a large amount of filtering rules while keeping them conflict-free, that is, avoiding security inconsistencies. On the other side, traffic adaptive optimization of large rule lists is useful for general purpose computers used as filtering devices, without specific designed hardware, to face growing link speeds and to harden filtering devices against DoS and DDoS attacks. Our work joins the two issues in an innovative way and defines a traffic adaptive algorithm to find conflict-free optimized rule sets, by relying on information gathered with traffic logs. The proposed approach suits current technology architectures and exploits available features, like traffic log databases, to minimize the impact of ACO development on the packet filtering devices. We demonstrate the benefit entailed by the proposed algorithm through measurements on a test bed made up of real-life, commercial packet filtering devices

Design and analysis of a beacon-less routing protocol for large volume content dissemination in vehicular ad hoc networks

Largevolumecontentdisseminationispursuedbythegrowingnumberofhighquality applications for Vehicular Ad hoc NETworks(VANETs), e.g., the live road surveillance service and the video-based overtaking assistant service. For the highly dynamical vehicular network topology, beacon-less routing protocols have been proven to be efficient in achieving a balance between the system performance and the control overhead. However, to the authors’ best knowledge, the routing design for large volume content has not been well considered in the previous work, which will introduce new challenges, e.g., the enhanced connectivity requirement for a radio link. In this paper, a link Lifetime-aware Beacon-less Routing Protocol (LBRP) is designed for large volume content delivery in VANETs. Each vehicle makes the forwarding decision based on the message header information and its current state, including the speed and position information. A semi-Markov process analytical model is proposed to evaluate the expected delay in constructing one routing path for LBRP. Simulations show that the proposed LBRP scheme outperforms the traditional dissemination protocols in providing a low end-to-end delay. The analytical model is shown to exhibit a good match on the delay estimation with Monte Carlo simulations, as well

On the time scales in video traffic characterization for queueing behavior

To guarantee quality of service (QoS) in future integrated service networks, traffic sources must be characterized to capture the traffic characteristics relevant to network performance. Recent studies reveal that multimedia traffic shows burstiness over multiple time scales and long range dependence (LRD). While researchers agree on the importance of traffic correlation there is no agreement on how much correlation should be incorporated into a traffic model for performance estimation and dimensioning of networks. In this article, we present an approach for defining a relevant time scale for the characterization of VER video traffic in the sense of queueing delay. We first consider the Reich formula and characterize traffic by the Piecewise Linear Arrival Envelope Function (PLAEF). We then define the cutoff interval above which the correlation does not affect the queue buildup. The cutoff interval is the upper bound of the time scale which is required for the estimation of queue size and thus the characterization of VER video traffic. We also give a procedure to approximate the empirical PLAEF with a concave function; this significantly simplifies the calculation in the estimation of the cutoff interval and delay bound with little estimation loss. We quantify the relationship between the time scale in the correlation of video traffic and the queue buildup using a set of experiments with traces of MPEG/JPEG-compressed video. We show that the critical interval i.e. the range for the correlation relevant to the queueing delay, depends on the traffic load: as the traffic load increases, the range of the time scale required for estimation for queueing delay also increases. These results offer further insights into the implication of LRD in VER video traffic. (C) 1999 Elsevier Science B.V. Ail rights reserved

Graph-Based Multi-Label Classification for WiFi Network Traffic Analysis

Network traffic analysis, and specifically anomaly and attack detection, call for sophisticated tools relying on a large number of features. Mathematical modeling is extremely difficult, given the ample variety of traffic patterns and the subtle and varied ways that malicious activity can be carried out in a network. We address this problem by exploiting data-driven modeling and computational intelligence techniques. Sequences of packets captured on the communication medium are considered, along with multi-label metadata. Graph-based modeling of the data are introduced, thus resorting to the powerful GRALG approach based on feature information granulation, identification of a representative alphabet, embedding and genetic optimization. The obtained classifier is evaluated both under accuracy and complexity for two different supervised problems and compared with state-of-the-art algorithms. We show that the proposed preprocessing strategy is able to describe higher level relations between data instances in the input domain, thus allowing the algorithms to suitably reconstruct the structure of the input domain itself. Furthermore, the considered Granular Computing approach is able to extract knowledge on multiple semantic levels, thus effectively describing anomalies as subgraphs-based symbols of the whole network graph, in a specific time interval. Interesting performances can thus be achieved in identifying network traffic patterns, in spite of the complexity of the considered traffic classes

Age of Information of One-Hop Broadcast Communications in a CSMA Network

We consider a network of agents that send periodic updates to their neighbors. A trade-off between load on the shared communication channel and data timeliness is obtained by looking at the Age of Information (AoI) metric. We develop a model of a Carrier-Sense Multiple Access (CSMA) network with partial sensing, to calculate the AoI of one-hop broadcast messages exchanged among the agents. The model is applied to beacon messages in a vehicular network to gain insight into the impact of system parameters

A Multi-Hop Broadcast Wave Approach for Floating Car Data Collection in Vehicular Networks

Inter-Vehicle Communication (IVC) is bringing connected and cooperative mobility closer to reality. Vehicles today are able to produce huge amounts of information, known in the literature as Floating Car Data (FCD), containing status information gathered from sensing the internal condition of the vehicle and the external environment. Adding networking capabilities to vehicles allows them to share this information among themselves and with the infrastructure. Collecting real-time FCD information from vehicles opens up the possibility of having access to an enormous amount of useful information that can boost the development of innovative services and applications in the domain of Intelligent Transportation System (ITS). In this paper we propose several solutions to efficiently collect real-time FCD information in Dedicated Short-Range Communication (DSRC)-enabled Vehicular Ad Hoc Networks (VANETs). The goal is to improve the efficiency of the FCD collection operation while keeping the impact on the DSRC communication channel as low as possible. We do this by exploiting a slightly modified version of a standardized data dissemination protocol to create a backbone of relaying vehicles that, by following local rules, generate a multi-hop broadcast wave of collected FCD messages. The proposed protocols are evaluated via realistic simulations under different vehicular densities and urban scenarios

Organización conceptual del entorno natural wichí: resultados preliminares

La diversidad del entorno natural es conceptualizada por todas las poblaciones del mundo mediante categorías lexicalizadas organizadas en taxonomías jerárquicas, las que se hallan estructuradas bajo fuertes principios universales pero también moldeadas por la experiencia individual de cada grupo humano (Berlin et al., 1973; Brown, 1986). Desde un enfoque cognitivo y transcultural el presente trabajo, el que es parte de un proyecto más amplio sobre cómo personas de orígenes lingüísticos, culturales y de experiencia con el entorno diferentes conceptualizan la naturaleza (Taverna et al., 2012; 2014), se centra en cómo los wichí conceptualizan el entorno natural que habitan. Estudios propios previos han sugerido que esta población ordenaría su entorno viviente mediante una taxonomía de los habitantes de la tierra (hunhat-lheley) y que sus agrupamientos estarían organizados en base a propiedades ecológicas (ej. hábitat) más que taxonómicas (Taverna et al., 2012). Aquí se explora directa y empíricamente las categorías lexicalizadas encontradas en el referido estudio, más específicamente se pretende establecer: la organización conceptual global de las categorías de los habitantes de la tierra que corresponden al mundo animal y vegetal (animales de monte, de agua, de aire, árboles y arbustos, plantas cultivables, hierbas, enredaderas y cactus); las conexiones entre los ejemplares de cada categoría; y sus miembros más familiares o representativos. Para explorar lo antedicho, se utiliza una tarea de producción de nombres (Deese, 1965; Winkler-Rhoades et al., 2010). Esta prueba se apoya en el supuesto de que cuando una palabra o concepto son activados, activa otros que están semántica o asociativamente relacionados. El orden en que los nombres son producidos es tomado como un índice de la proximidad psicológica de los conceptos subyacentes, las listas que las personas generan en la prueba es la antesala de la organización conceptual subyacente en determinado dominio (Kail & Nippold, 1984; Medin et al., 1997; Neely, 1991).The diversity of the natural environment is conceptualized by all people through lexicalized categories organized in hierarchical taxonomies (Berlin et al., 1973). From a cognitive and cross-cultural perspective, here we explore the global conceptual organization of the Wichi folkbiological categories which organize the animal and plant domains, particularly we focus on the connections between the items for each category; and the most familiar or representative exemplars. Fourteen Wichi adults (Age M=29.14) from the Wichi-Lawet community, Laguna Yema, Formosa, were asked to take part in a free-listing task.Trabajos libres: Estudios interdisciplinarios y nuevos desarrollosFacultad de Psicologí

Age of Information in CSMA-Based Networks With Bursty Update Traffic

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Real Time Identification of SSH Encrypted Application Flows by Using Cluster Analysis Techniques

Abstract. The identification of application flows is a critical task in order to manage bandwidth requirements of different kind of services (i.e. VOIP, Video, ERP). As network security functions spread, an increasing amount of traffic is natively encrypted due to privacy issues (e.g. VPN). This makes ineffective current traffic classification systems based on ports and payload inspection, e.g. even powerful Deep Packet Inspection is useless to classify application flow carried inside SSH sessions. We have developed a real time traffic classification method based on cluster analysis to identify SSH flows from statistical behavior of IP traffic parameters, such as length, arrival times and direction of packets. In this paper we describe our approach and relevant obtained results. We achieve detection rate up to 99.5 % in classifying SSH flows and accuracy up to 99.88 % for application flows carried within those flows, such as SCP, SFTP and HTTP over SSH