90 research outputs found
Achieving Small World Properties using Bio-Inspired Techniques in Wireless Networks
It is highly desirable and challenging for a wireless ad hoc network to have
self-organization properties in order to achieve network wide characteristics.
Studies have shown that Small World properties, primarily low average path
length and high clustering coefficient, are desired properties for networks in
general. However, due to the spatial nature of the wireless networks, achieving
small world properties remains highly challenging. Studies also show that,
wireless ad hoc networks with small world properties show a degree distribution
that lies between geometric and power law. In this paper, we show that in a
wireless ad hoc network with non-uniform node density with only local
information, we can significantly reduce the average path length and retain the
clustering coefficient. To achieve our goal, our algorithm first identifies
logical regions using Lateral Inhibition technique, then identifies the nodes
that beamform and finally the beam properties using Flocking. We use Lateral
Inhibition and Flocking because they enable us to use local state information
as opposed to other techniques. We support our work with simulation results and
analysis, which show that a reduction of up to 40% can be achieved for a
high-density network. We also show the effect of hopcount used to create
regions on average path length, clustering coefficient and connectivity.Comment: Accepted for publication: Special Issue on Security and Performance
of Networks and Clouds (The Computer Journal
Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks
Soaring capacity and coverage demands dictate that future cellular networks
need to soon migrate towards ultra-dense networks. However, network
densification comes with a host of challenges that include compromised energy
efficiency, complex interference management, cumbersome mobility management,
burdensome signaling overheads and higher backhaul costs. Interestingly, most
of the problems, that beleaguer network densification, stem from legacy
networks' one common feature i.e., tight coupling between the control and data
planes regardless of their degree of heterogeneity and cell density.
Consequently, in wake of 5G, control and data planes separation architecture
(SARC) has recently been conceived as a promising paradigm that has potential
to address most of aforementioned challenges. In this article, we review
various proposals that have been presented in literature so far to enable SARC.
More specifically, we analyze how and to what degree various SARC proposals
address the four main challenges in network densification namely: energy
efficiency, system level capacity maximization, interference management and
mobility management. We then focus on two salient features of future cellular
networks that have not yet been adapted in legacy networks at wide scale and
thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP), and
device-to-device (D2D) communications. After providing necessary background on
CoMP and D2D, we analyze how SARC can particularly act as a major enabler for
CoMP and D2D in context of 5G. This article thus serves as both a tutorial as
well as an up to date survey on SARC, CoMP and D2D. Most importantly, the
article provides an extensive outlook of challenges and opportunities that lie
at the crossroads of these three mutually entangled emerging technologies.Comment: 28 pages, 11 figures, IEEE Communications Surveys & Tutorials 201
On node ranking in graphs
The ranking of nodes in a network according to their ``importance'' is a
classic problem that has attracted the interest of different scientific
communities in the last decades. The current COVID-19 pandemic has recently
rejuvenated the interest in this problem, as it is related to the selection of
which individuals should be tested in a population of asymptomatic individuals,
or which individuals should be vaccinated first. Motivated by the COVID-19
spreading dynamics, in this paper we review the most popular methods for node
ranking in undirected unweighted graphs, and compare their performance in a
benchmark realistic network, that takes into account the community-based
structure of society. Also, we generalize a classic benchmark network
originally proposed by Newman for ranking nodes in unweighted graphs, to show
how ranks change in the weighted case
Scale-dependent measure of network centrality from diffusion dynamics
Classic measures of graph centrality capture distinct aspects of node importance, from the local (e.g., degree) to the global (e.g., closeness). Here we exploit the connection between diffusion and geometry to introduce a multiscale centrality measure. A node is defined to be central if it breaks the metricity of the diffusion as a consequence of the effective boundaries and inhomogeneities in the graph. Our measure is naturally multiscale, as it is computed relative to graph neighbourhoods within the varying time horizon of the diffusion. We find that the centrality of nodes can differ widely at different scales. In particular, our measure correlates with degree (i.e., hubs) at small scales and with closeness (i.e., bridges) at large scales, and also reveals the existence of multi-centric structures in complex networks. By examining centrality across scales, our measure thus provides an evaluation of node importance relative to local and global processes on the network
Automatic text summarization using pathfinder network scaling
Contém uma errataTese de Mestrado. Inteligência Artificial e Sistemas Inteligentes. Faculdade de Engenharia. Universidade do Porto, Faculdade de Economia. Universidade do Porto. 200
Road network selection for small-scale maps using an improved centrality-based algorithm
The road network is one of the key feature classes in topographic maps and databases. In the task of deriving road networks for products at smaller scales, road network selection forms a prerequisite for all other generalization operators, and is thus a fundamental operation in the overall process of topographic map and database production. The objective of this work was to develop an algorithm for automated road network selection from a large-scale (1:10,000) to a small-scale database (1:200,000). The project was pursued in collaboration with swisstopo, the national mapping agency of Switzerland, with generic mapping requirements in mind. Preliminary experiments suggested that a selection algorithm based on betweenness centrality performed best for this purpose, yet also exposed problems. The main contribution of this paper thus consists of four extensions that address deficiencies of the basic centrality-based algorithm and lead to a significant improvement of the results. The first two extensions improve the formation of strokes concatenating the road segments, which is crucial since strokes provide the foundation upon which the network centrality measure is computed. Thus, the first extension ensures that roundabouts are detected and collapsed, thus avoiding interruptions of strokes by roundabouts, while the second introduces additional semantics in the process of stroke formation, allowing longer and more plausible strokes to built. The third extension detects areas of high road density (i.e., urban areas) using density-based clustering and then locally increases the threshold of the centrality measure used to select road segments, such that more thinning takes place in those areas. Finally, since the basic algorithm tends to create dead-ends—which however are not tolerated in small-scale maps—the fourth extension reconnects these dead-ends to the main network, searching for the best path in the main heading of the dead-end
The Global R&D Network. A network analysis of international R&D centres
A firm's decision to establish an R&D centre in a specific location creates externalities affecting other firms and, thus, a random distribution of location choices is unlikely. Expecting that the global distribution of R&D centres fulfils the criteria of a complex network, we apply social network analysis to study the locations of international R&D centres and the relationships between the countries owning and hosting them. We analyse the characteristics of the global R&D network and identify its core members. Further, we include network indices in an empirical analysis of the R&D internationalisation determinants. We find that a country's position in the network, which does not necessarily coincide with its geographical or cultural proximity to other countries, has a significant impact on the formation and intensity of R&D linkages between countries. We provide policy implications addressing the challenges emerging from the increasing internationalisation and network of R&D.JRC.J.3-Information Societ
The Factors of Interest Group Networks and Success: Organization, Issues and Resources
While interest groups use a variety of techniques to exert influence, coalition strategies are the dominant lobbying technique. However, many questions remain about such coalitions. This paper is the second in a series of social network analyses of purposive and coordinated interest group relationships. We utilize a network measure based on cosigner status to United States Supreme Court amicus curiae, or friend of the court briefs. The illuminated structures lend insight into the central players and overall formation of the network over the first several years of the 21st century. The factions are tied together by various central players, who act as hubs, leaving a disparate collection of organizations that work alone. Using an exponential-family random graph model (ERGM), we find that graph-theorectic and organizational characteristics, such as size and budget, as well as policy interests explain interest group network formation
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