194 research outputs found
Networking - A Statistical Physics Perspective
Efficient networking has a substantial economic and societal impact in a
broad range of areas including transportation systems, wired and wireless
communications and a range of Internet applications. As transportation and
communication networks become increasingly more complex, the ever increasing
demand for congestion control, higher traffic capacity, quality of service,
robustness and reduced energy consumption require new tools and methods to meet
these conflicting requirements. The new methodology should serve for gaining
better understanding of the properties of networking systems at the macroscopic
level, as well as for the development of new principled optimization and
management algorithms at the microscopic level. Methods of statistical physics
seem best placed to provide new approaches as they have been developed
specifically to deal with non-linear large scale systems. This paper aims at
presenting an overview of tools and methods that have been developed within the
statistical physics community and that can be readily applied to address the
emerging problems in networking. These include diffusion processes, methods
from disordered systems and polymer physics, probabilistic inference, which
have direct relevance to network routing, file and frequency distribution, the
exploration of network structures and vulnerability, and various other
practical networking applications.Comment: (Review article) 71 pages, 14 figure
Multilayer Networks
In most natural and engineered systems, a set of entities interact with each
other in complicated patterns that can encompass multiple types of
relationships, change in time, and include other types of complications. Such
systems include multiple subsystems and layers of connectivity, and it is
important to take such "multilayer" features into account to try to improve our
understanding of complex systems. Consequently, it is necessary to generalize
"traditional" network theory by developing (and validating) a framework and
associated tools to study multilayer systems in a comprehensive fashion. The
origins of such efforts date back several decades and arose in multiple
disciplines, and now the study of multilayer networks has become one of the
most important directions in network science. In this paper, we discuss the
history of multilayer networks (and related concepts) and review the exploding
body of work on such networks. To unify the disparate terminology in the large
body of recent work, we discuss a general framework for multilayer networks,
construct a dictionary of terminology to relate the numerous existing concepts
to each other, and provide a thorough discussion that compares, contrasts, and
translates between related notions such as multilayer networks, multiplex
networks, interdependent networks, networks of networks, and many others. We
also survey and discuss existing data sets that can be represented as
multilayer networks. We review attempts to generalize single-layer-network
diagnostics to multilayer networks. We also discuss the rapidly expanding
research on multilayer-network models and notions like community structure,
connected components, tensor decompositions, and various types of dynamical
processes on multilayer networks. We conclude with a summary and an outlook.Comment: Working paper; 59 pages, 8 figure
Social relationship based routing for delay tolerant Bluetooth-enabled PSN communications
PhDOpportunistic networking is a concept derived from the mobile ad hoc networking in which devices have no prior knowledge of routes to the intended destinations. Content dissemination in opportunistic networks thus is carried out in a store and forward fashion. Opportunistic routing poses distinct challenges compared to the traditional networks such as Internet and mobile ad hoc networks where nodes have prior knowledge of the routes to the intended destinations. Information dissemination in opportunistic networks requires dealing with intermittent connectivity, variable delays, short connection durations and dynamic topology. Addressing these challenges becomes a significant motivation for developing novel applications and protocols for information dissemination in opportunistic networks.
This research looks at opportunistic networking, specifically at networks composed of mobile devices or, pocket switched networks. Mobile devices are now accepted as an integral part of society and are often equipped with Bluetooth capabilities that allow for opportunistic information sharing between devices. The ad hoc nature of opportunistic networks means nodes have no advance routing knowledge and this is key challenge. Human social relationships are based on certain patterns that can be exploited to make opportunistic routing decisions. Targeting nodes that evidence high popularity or high influence can enable more efficient content dissemination. Based on this observation, a novel impact based neighbourhood algorithm called Lobby Influence is presented. The algorithm is tested against two previously proposed algorithms and proves better in terms of message delivery and delay. Moreover, unlike other social based algorithms, which have a tendency to concentrate traffic through their identified routing nodes, the new algorithm provides a fairer load distribution, thus alleviating the tendency to saturate individual nodes
A Survey of Social Network Analysis Techniques and their Applications to Socially Aware Networking
Socially aware networking is an emerging research field that aims to improve the current networking technologies and realize novel network services by applying social network analysis (SNA) techniques. Conducting socially aware networking studies requires knowledge of both SNA and communication networking, but it is not easy for communication networking researchers who are unfamiliar with SNA to obtain comprehensive knowledge of SNA due to its interdisciplinary nature. This paper therefore aims to fill the knowledge gap for networking researchers who are interested in socially aware networking but are not familiar with SNA. This paper surveys three types of important SNA techniques for socially aware networking: identification of influential nodes, link prediction, and community detection. Then, this paper introduces how SNA techniques are used in socially aware networking and discusses research trends in socially aware networking
Analyzing and Modeling Real-World Phenomena with Complex Networks: A Survey of Applications
The success of new scientific areas can be assessed by their potential for
contributing to new theoretical approaches and in applications to real-world
problems. Complex networks have fared extremely well in both of these aspects,
with their sound theoretical basis developed over the years and with a variety
of applications. In this survey, we analyze the applications of complex
networks to real-world problems and data, with emphasis in representation,
analysis and modeling, after an introduction to the main concepts and models. A
diversity of phenomena are surveyed, which may be classified into no less than
22 areas, providing a clear indication of the impact of the field of complex
networks.Comment: 103 pages, 3 figures and 7 tables. A working manuscript, suggestions
are welcome
Complex Systems: Nonlinearity and Structural Complexity in spatially extended and discrete systems
Resumen Esta Tesis doctoral aborda el estudio de sistemas de muchos elementos (sistemas discretos) interactuantes. La fenomenologĂa presente en estos sistemas esta dada por la presencia de dos ingredientes fundamentales: (i) Complejidad dinĂĄmica: Las ecuaciones del movimiento que rigen la evoluciĂłn de los constituyentes son no lineales de manera que raramente podremos encontrar soluciones analĂticas. En el espacio de fases de estos sistemas pueden coexistir diferentes tipos de trayectorias dinĂĄmicas (multiestabilidad) y su topologĂa puede variar enormemente dependiendo de dos parĂĄmetros usados en las ecuaciones. La conjunciĂłn de dinĂĄmica no lineal y sistemas de muchos grados de libertad (como los que aquĂ se estudian) da lugar a propiedades emergentes como la existencia de soluciones localizadas en el espacio, sincronizaciĂłn, caos espacio-temporal, formaciĂłn de patrones, etc... (ii) Complejidad estructural: Se refiere a la existencia de un alto grado de aleatoriedad en el patrĂłn de las interacciones entre los componentes. En la mayorĂa de los sistemas estudiados esta aleatoriedad se presenta de forma que la descripciĂłn de la influencia del entorno sobre un Ășnico elemento del sistema no puede describirse mediante una aproximaciĂłn de campo medio. El estudio de estos dos ingredientes en sistemas extendidos se realizarĂĄ de forma separada (Partes I y II de esta Tesis) y conjunta (Parte III). Si bien en los dos primeros casos la fenomenologĂa introducida por cada fuente de complejidad viene siendo objeto de amplios estudios independientes a lo largo de los Ășltimos años, la conjunciĂłn de ambas da lugar a un campo abierto y enormemente prometedor, donde la interdisciplinariedad concerniente a los campos de aplicaciĂłn implica un amplio esfuerzo de diversas comunidades cientĂficas. En particular, este es el caso del estudio de la dinĂĄmica en sistemas biolĂłgicos cuyo anĂĄlisis es difĂcil de abordar con tĂ©cnicas exclusivas de la BioquĂmica, la FĂsica EstadĂstica o la FĂsica MatemĂĄtica. En definitiva, el objetivo marcado en esta Tesis es estudiar por separado dos fuentes de complejidad inherentes a muchos sistemas de interĂ©s para, finalmente, estar en disposiciĂłn de atacar con nuevas perspectivas problemas relevantes para la FĂsica de procesos celulares, la Neurociencia, DinĂĄmica Evolutiva, etc..
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