1,720,961 research outputs found
On Cost-Effective, Reliable Coverage for LoS Communications in Urban Areas
Full text linkThe use of ultra high frequencies in 5G and future networks to improve transmission speeds and capacity requires that users' equipment remain in Line of Sight with the access antennas most of the service time. This requirement implies a change in perspective to plan the coverage: Antennas cannot be placed on roofs or remote antenna sites, and a robust coverage is based on multi-antenna visibility from any point. This paper tackles the problem of public street coverage in urban areas with a data-driven methodology. Starting from 3D digital maps, we formalize the problem of antenna placement as a set coverage problem and leverage powerful heuristics to implement a general algorithm that allows the exploration of different policies, returning the detailed coverage, the antenna placement, and the cost of the coverage. Results on 15 areas in 3 Italian cities show the properties of different policies and confirm for the first time on large scale real data the feasibility of Line of Sight communications with a sustainable number of antennas per km2
Poster: TrueNets, a Topology Generator for Realistic Network Analysis
Full text linkThe availability of realistic topology generators is a key component in the study of network performance. This work describes a new approach for realistic generation of topologies, named TrueNets, that uses open data provided by public administrations and crowd-sensing efforts for populated areas, maps, altitude of land and buildings; TrueNets estimates link performance with classical propagation models and produces annotated topologies of networks that can actually exist in the selected areas, thus providing not only an abstract tool for performance evaluation, but also a design tool for planning. We use TrueNets to model distributed mesh networks and we show that the generated topologies differ substantially from state-of-the-art synthetic generators
On the Properties of Next Generation Wireless Backhaul
Full text linkWith the advent of 5G, cellular networks require a high number of base stations, possibly interconnected with wireless links, an evolution introduced in the last revision of 5G as the Integrated Access and Backhaul (IAB). Researchers are now working to optimize the complex topologies of the backhaul network, using synthetic models for the underlying visibility graph, i.e., the graph of possible connections between the base stations. The goal of this paper is to provide a novel methodology to generate visibility graphs starting from real data (and the data sets themselves together with the source code for their manipulation), in order to base the IAB design and optimization on assumptions that are as close as possible to reality. We introduce a GPU-based method to create visibility graphs from open data, we analyze the properties of the realistic visibility graphs, and we show that different geographic areas produce very different graphs. We run state-of-the-art algorithms to create wireless backhaul networks on top of visibility graphs, and we show that the results that exploit synthetic models are far from those that use our realistic graphs. Our conclusion is that the data-based approach we propose is essential to design mobile networks that work in a variety of real-world situations
Anomaly detection for fault detection in wireless community networks using machine learning
No full textMachine learning has received increasing attention in computer science in recent years and many types of methods have been proposed. In computer networks, little attention has been paid to the use of ML for fault detection, the main reason being the lack of datasets. This is motivated by the reluctance of network operators to share data about their infrastructure and network failures. In this paper, we attempt to fill this gap using anomaly detection techniques to discern hardware failure events in wireless community networks. For this purpose we use 4 unsupervised machine learning, ML, approaches based on different principles. We have built a dataset from a production wireless community network, gathering traffic and non-traffic features, e.g. CPU and memory. For the numerical analysis we investigated the ability of the different ML approaches to detect an unprovoked gateway failure that occurred during data collection. Our numerical results show that all the tested approaches improve to detect the gateway failure when non-traffic features are also considered. We see that, when properly tuned, all ML methods are effective to detect the failure. Nonetheless, using decision boundaries and other analysis techniques we observe significant different behavior among the ML methods
NPART+: Improving Wireless Network Topology Generators with Data from the Real World
No full textTopology generators are a key asset for researchers in computer science and telecommunications that often need to test network protocols or distributed systems in simulated environments that resemble real scenarios. Despite that, in the research area of distributed wireless networks still many works use very simplistic models that do not have the characteristics of the currently existing large-scale wireless mesh networks. The only topology generator that tries to produce synthetic graphs that look like real networks is NPART [1].In this work we test the characteristics of NPART against another, completely different approach: TrueNets [2]. TrueNets uses accurate data representing land surface of a real world location to create topologies of networks that could actually exist. The downside of TrueNets is twofold: it can be used only when data-sets are available and generating topologies is computationally intensive. We show that using aggregate data from TrueNets we are able to improve NPART. We call the new generator NPART+ and we show that compared to topologies generated with TrueNets, NPART+ (or its variants) improves NPART in several metrics, but still it can not match the accuracy of TrueNets
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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