382 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
Spreading processes in Multilayer Networks
Several systems can be modeled as sets of interconnected networks or networks
with multiple types of connections, here generally called multilayer networks.
Spreading processes such as information propagation among users of an online
social networks, or the diffusion of pathogens among individuals through their
contact network, are fundamental phenomena occurring in these networks.
However, while information diffusion in single networks has received
considerable attention from various disciplines for over a decade, spreading
processes in multilayer networks is still a young research area presenting many
challenging research issues. In this paper we review the main models, results
and applications of multilayer spreading processes and discuss some promising
research directions.Comment: 21 pages, 3 figures, 4 table
Synchronization in complex networks
Synchronization processes in populations of locally interacting elements are
in the focus of intense research in physical, biological, chemical,
technological and social systems. The many efforts devoted to understand
synchronization phenomena in natural systems take now advantage of the recent
theory of complex networks. In this review, we report the advances in the
comprehension of synchronization phenomena when oscillating elements are
constrained to interact in a complex network topology. We also overview the new
emergent features coming out from the interplay between the structure and the
function of the underlying pattern of connections. Extensive numerical work as
well as analytical approaches to the problem are presented. Finally, we review
several applications of synchronization in complex networks to different
disciplines: biological systems and neuroscience, engineering and computer
science, and economy and social sciences.Comment: Final version published in Physics Reports. More information
available at http://synchronets.googlepages.com
A survey on cost-effective context-aware distribution of social data streams over energy-efficient data centres
Social media have emerged in the last decade as a viable and ubiquitous means of communication. The ease of user content generation within these platforms, e.g. check-in information, multimedia data, etc., along with the proliferation of Global Positioning System (GPS)-enabled, always-connected capture devices lead to data streams of unprecedented amount and a radical change in information sharing. Social data streams raise a variety of practical challenges, including derivation of real-time meaningful insights from effectively gathered social information, as well as a paradigm shift for content distribution with the leverage of contextual data associated with user preferences, geographical characteristics and devices in general. In this article we present a comprehensive survey that outlines the state-of-the-art situation and organizes challenges concerning social media streams and the infrastructure of the data centres supporting the efficient access to data streams in terms of content distribution, data diffusion, data replication, energy efficiency and network infrastructure. We systematize the existing literature and proceed to identify and analyse the main research points and industrial efforts in the area as far as modelling, simulation and performance evaluation are concerned
Parallel -Clique Community Detection on Large-Scale Networks
The analysis of real-world complex networks has been the focus of recent research. Detecting communities helps in uncovering their structural and functional organization. Valuable insight can be obtained by analyzing the dense, overlapping, and highly interwoven k-clique communities. However, their detection is challenging due to extensive memory requirements and execution time. In this paper, we present a novel, parallel k-clique community detection method, based on an innovative technique which enables connected components of a network to be obtained from those of its subnetworks. The novel method has an unbounded, user-configurable, and input-independent maximum degree of parallelism, and hence is able to make full use of computational resources. Theoretical tight upper bounds on its worst case time and space complexities are given as well. Experiments on real-world networks such as the Internet and the World Wide Web confirmed the almost optimal use of parallelism (i.e., a linear speedup). Comparisons with other state-of-the-art k-clique community detection methods show dramatic reductions in execution time and memory footprint. An open-source implementation of the method is also made publicly available
Modeling and Control Techniques in Smart Systems
Energy and food crisis are two major problems that our human society has to face in the 21st
century. With the world’s population reaching 7.62 billion as of May 2018, both electric power
and agricultural industries turn to technological innovations for solutions to keep up the increasing
demand. In the past and currently, utility companies rely on rule of thumb to estimate power
consumption. However, inaccurate predictions often result in over production, and much energy is
wasted. On the other hand, traditional periodic and threshold based irrigation practices have also
been proven outdated. This problem is further compounded by recent years’ frequent droughts
across the globe. New technologies are needed to manage irrigations more efficiently.
Fortunately, with the unprecedented development of Artificial Intelligence (AI), wireless communication,
and ubiquitous computing technologies, high degree of information integration and
automation are steadily becoming reality. More smart metering devices are installed today than
ever before, enabling fast and massive data collection. Patterns and trends can be more accurately
predicted using machine learning techniques. Based on the results, utility companies can schedule
production more efficiently, not only enhancing their profitabilities, but also making our world’s
energy supply more sustainable. In addition, predictions can serve as references to detect anomalous
activities like power theft and cyber attacks.
On the other hand, with wireless communication, real-time soil moisture sensor readings and
weather forecasts can be collected for precision irrigation. Smaller but more powerful controllers
provide perfect platforms for complicated control algorithms. We designed and built a fully automated
irrigation system at Bushland, Texas. It is designed to operate without any human intervention.
Workers can program, move, and monitor multiple irrigation systems remotely. The
algorithm that runs on the controls deserves more attention. AI and other state of art controlling
techniques are implemented, making it much more powerful than any existing systems. By integrating
professional crop yield simulation models like DSSAT, computers can run tens of thousand
simulations on all kinds of weather and soil conditions, and more importantly, learn from the experience. In reality, such process would take thousands of years to obtain. Yet, the computers can
find an optimum solution in minutes. The experience is then summarized as a policy and stored
inside the controller as a lookup table. Furthermore, after each crop season, users can calibrate and
update current policy with real harvest data.
Crop yield models like DSSAT and AquaCrop play very important roles in agricultural research.
They represent our best knowledge in plant biology and can be very accurate when well
calibrated. However, the calibration process itself is often time consuming, thus limiting the scale
and speed of using these models. We made efforts to combine different models to produce a single
accurate prediction using machine learning techniques. The process does not require manual calibration,
but only soil, historical weather, and harvest data. 20 models were built, and their results
were evaluated and compared. With high accuracy, machine learning techniques have shown a
promising direction to best utilize professional models, and demonstrated great potential for use in
future agricultural research
Scale invariance in natural and artificial collective systems : a review
Self-organized collective coordinated behaviour is an impressive phenomenon, observed in a variety of natural and artificial systems, in which coherent global structures or dynamics emerge from local interactions between individual parts. If the degree of collective integration of a system does not depend on size, its level of robustness and adaptivity is typically increased and we refer to it as scale-invariant. In this review, we first identify three main types of self-organized scale-invariant systems: scale-invariant spatial structures, scale-invariant topologies and scale-invariant dynamics. We then provide examples of scale invariance from different domains in science, describe their origins and main features and discuss potential challenges and approaches for designing and engineering artificial systems with scale-invariant properties
Advances in Computer Science and Engineering
The book Advances in Computer Science and Engineering constitutes the revised selection of 23 chapters written by scientists and researchers from all over the world. The chapters cover topics in the scientific fields of Applied Computing Techniques, Innovations in Mechanical Engineering, Electrical Engineering and Applications and Advances in Applied Modeling
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