26,580 research outputs found
Time-Varying Graphs and Dynamic Networks
The past few years have seen intensive research efforts carried out in some
apparently unrelated areas of dynamic systems -- delay-tolerant networks,
opportunistic-mobility networks, social networks -- obtaining closely related
insights. Indeed, the concepts discovered in these investigations can be viewed
as parts of the same conceptual universe; and the formal models proposed so far
to express some specific concepts are components of a larger formal description
of this universe. The main contribution of this paper is to integrate the vast
collection of concepts, formalisms, and results found in the literature into a
unified framework, which we call TVG (for time-varying graphs). Using this
framework, it is possible to express directly in the same formalism not only
the concepts common to all those different areas, but also those specific to
each. Based on this definitional work, employing both existing results and
original observations, we present a hierarchical classification of TVGs; each
class corresponds to a significant property examined in the distributed
computing literature. We then examine how TVGs can be used to study the
evolution of network properties, and propose different techniques, depending on
whether the indicators for these properties are a-temporal (as in the majority
of existing studies) or temporal. Finally, we briefly discuss the introduction
of randomness in TVGs.Comment: A short version appeared in ADHOC-NOW'11. This version is to be
published in Internation Journal of Parallel, Emergent and Distributed
System
IEEE Access special section editorial: Artificial intelligence enabled networking
With today’s computer networks becoming increasingly dynamic, heterogeneous, and complex, there is great interest in deploying artificial intelligence (AI) based techniques for optimization and management of computer networks. AI techniques—that subsume multidisciplinary techniques from machine learning, optimization theory, game theory, control theory, and meta-heuristics—have long been applied to optimize computer networks in many diverse settings. Such an approach is gaining increased traction with the emergence of novel networking paradigms that promise to simplify network management (e.g., cloud computing, network functions virtualization, and software-defined networking) and provide intelligent services (e.g., future 5G mobile networks). Looking ahead, greater integration of AI into networking architectures can help develop a future vision of cognitive networks that will show network-wide intelligent behavior to solve problems of network heterogeneity, performance, and quality of service (QoS)
Distributed Community Detection in Dynamic Graphs
Inspired by the increasing interest in self-organizing social opportunistic
networks, we investigate the problem of distributed detection of unknown
communities in dynamic random graphs. As a formal framework, we consider the
dynamic version of the well-studied \emph{Planted Bisection Model}
\sdG(n,p,q) where the node set of the network is partitioned into two
unknown communities and, at every time step, each possible edge is
active with probability if both nodes belong to the same community, while
it is active with probability (with ) otherwise. We also consider a
time-Markovian generalization of this model.
We propose a distributed protocol based on the popular \emph{Label
Propagation Algorithm} and prove that, when the ratio is larger than
(for an arbitrarily small constant ), the protocol finds the right
"planted" partition in time even when the snapshots of the dynamic
graph are sparse and disconnected (i.e. in the case ).Comment: Version I
5GNOW: Challenging the LTE Design Paradigms of Orthogonality and Synchronicity
LTE and LTE-Advanced have been optimized to deliver high bandwidth pipes to
wireless users. The transport mechanisms have been tailored to maximize single
cell performance by enforcing strict synchronism and orthogonality within a
single cell and within a single contiguous frequency band. Various emerging
trends reveal major shortcomings of those design criteria: 1) The fraction of
machine-type-communications (MTC) is growing fast. Transmissions of this kind
are suffering from the bulky procedures necessary to ensure strict synchronism.
2) Collaborative schemes have been introduced to boost capacity and coverage
(CoMP), and wireless networks are becoming more and more heterogeneous
following the non-uniform distribution of users. Tremendous efforts must be
spent to collect the gains and to manage such systems under the premise of
strict synchronism and orthogonality. 3) The advent of the Digital Agenda and
the introduction of carrier aggregation are forcing the transmission systems to
deal with fragmented spectrum. 5GNOW is an European research project supported
by the European Commission within FP7 ICT Call 8. It will question the design
targets of LTE and LTE-Advanced having these shortcomings in mind and the
obedience to strict synchronism and orthogonality will be challenged. It will
develop new PHY and MAC layer concepts being better suited to meet the upcoming
needs with respect to service variety and heterogeneous transmission setups.
Wireless transmission networks following the outcomes of 5GNOW will be better
suited to meet the manifoldness of services, device classes and transmission
setups present in envisioned future scenarios like smart cities. The
integration of systems relying heavily on MTC into the communication network
will be eased. The per-user experience will be more uniform and satisfying. To
ensure this 5GNOW will contribute to upcoming 5G standardization.Comment: Submitted to Workshop on Mobile and Wireless Communication Systems
for 2020 and beyond (at IEEE VTC 2013, Spring
A survey of self organisation in future cellular networks
This article surveys the literature over the period of the last decade on the emerging field of self organisation as applied to wireless cellular communication networks. Self organisation has been extensively studied and applied in adhoc networks, wireless sensor networks and autonomic computer networks; however in the context of wireless cellular networks, this is the first attempt to put in perspective the various efforts in form of a tutorial/survey. We provide a comprehensive survey of the existing literature, projects and standards in self organising cellular networks. Additionally, we also aim to present a clear understanding of this active research area, identifying a clear taxonomy and guidelines for design of self organising mechanisms. We compare strength and weakness of existing solutions and highlight the key research areas for further development. This paper serves as a guide and a starting point for anyone willing to delve into research on self organisation in wireless cellular communication networks
Hybrid performance modelling of opportunistic networks
We demonstrate the modelling of opportunistic networks using the process
algebra stochastic HYPE. Network traffic is modelled as continuous flows,
contact between nodes in the network is modelled stochastically, and
instantaneous decisions are modelled as discrete events. Our model describes a
network of stationary video sensors with a mobile ferry which collects data
from the sensors and delivers it to the base station. We consider different
mobility models and different buffer sizes for the ferries. This case study
illustrates the flexibility and expressive power of stochastic HYPE. We also
discuss the software that enables us to describe stochastic HYPE models and
simulate them.Comment: In Proceedings QAPL 2012, arXiv:1207.055
Evolution of Swarm Robotics Systems with Novelty Search
Novelty search is a recent artificial evolution technique that challenges
traditional evolutionary approaches. In novelty search, solutions are rewarded
based on their novelty, rather than their quality with respect to a predefined
objective. The lack of a predefined objective precludes premature convergence
caused by a deceptive fitness function. In this paper, we apply novelty search
combined with NEAT to the evolution of neural controllers for homogeneous
swarms of robots. Our empirical study is conducted in simulation, and we use a
common swarm robotics task - aggregation, and a more challenging task - sharing
of an energy recharging station. Our results show that novelty search is
unaffected by deception, is notably effective in bootstrapping the evolution,
can find solutions with lower complexity than fitness-based evolution, and can
find a broad diversity of solutions for the same task. Even in non-deceptive
setups, novelty search achieves solution qualities similar to those obtained in
traditional fitness-based evolution. Our study also encompasses variants of
novelty search that work in concert with fitness-based evolution to combine the
exploratory character of novelty search with the exploitatory character of
objective-based evolution. We show that these variants can further improve the
performance of novelty search. Overall, our study shows that novelty search is
a promising alternative for the evolution of controllers for robotic swarms.Comment: To appear in Swarm Intelligence (2013), ANTS Special Issue. The final
publication will be available at link.springer.co
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