317,323 research outputs found
Avatar: A Time- and Space-Efficient Self-Stabilizing Overlay Network
Overlay networks present an interesting challenge for fault-tolerant
computing. Many overlay networks operate in dynamic environments (e.g. the
Internet), where faults are frequent and widespread, and the number of
processes in a system may be quite large. Recently, self-stabilizing overlay
networks have been presented as a method for managing this complexity.
\emph{Self-stabilizing overlay networks} promise that, starting from any
weakly-connected configuration, a correct overlay network will eventually be
built. To date, this guarantee has come at a cost: nodes may either have high
degree during the algorithm's execution, or the algorithm may take a long time
to reach a legal configuration. In this paper, we present the first
self-stabilizing overlay network algorithm that does not incur this penalty.
Specifically, we (i) present a new locally-checkable overlay network based upon
a binary search tree, and (ii) provide a randomized algorithm for
self-stabilization that terminates in an expected polylogarithmic number of
rounds \emph{and} increases a node's degree by only a polylogarithmic factor in
expectation
Distributed and Load-Adaptive Self Configuration in Sensor Networks
Proactive self-configuration is crucial for MANETs such as sensor networks, as these are often deployed in hostile environments and are ad hoc in nature. The dynamic architecture of the network is monitored by exchanging so-called Network State Beacons (NSBs) between key network nodes. The Beacon Exchange rate and the network state define both the time and nature of a proactive action to combat network performance degradation at a time of crisis. It is thus essential to optimize these parameters for the dynamic load profile of the network. This paper presents a novel distributed adaptive optimization Beacon Exchange selection model which considers distributed network load for energy efficient monitoring and proactive reconfiguration of the network. The results show an improvement of 70% in throughput, while maintaining a guaranteed quality-of- service for a small control-traffic overhead
Limit theorems for assortativity and clustering in null models for scale-free networks
An important problem in modeling networks is how to generate a randomly
sampled graph with given degrees. A popular model is the configuration model, a
network with assigned degrees and random connections. The erased configuration
model is obtained when self-loops and multiple edges in the configuration model
are removed. We prove an upper bound for the number of such erased edges for
regularly-varying degree distributions with infinite variance, and use this
result to prove central limit theorems for Pearson's correlation coefficient
and the clustering coefficient in the erased configuration model. Our results
explain the structural correlations in the erased configuration model and show
that removing edges leads to different scaling of the clustering coefficient.
We then prove that for the rank-1 inhomogeneous random graph, another null
model that creates scale-free simple networks, the results for Pearson's
correlation coefficient as well as for the clustering coefficient are similar
to the results for the erased configuration model
Analytical controllability of deterministic scale-free networks and Cayley trees
According to the exact controllability theory, the controllability is
investigated analytically for two typical types of self-similar bipartite
networks, i.e., the classic deterministic scale-free networks and Cayley trees.
Due to their self-similarity, the analytical results of the exact
controllability are obtained, and the minimum sets of driver nodes (drivers)
are also identified by elementary transformations on adjacency matrices. For
these two types of undirected networks, no matter their links are unweighted or
(nonzero) weighted, the controllability of networks and the configuration of
drivers remain the same, showing a robustness to the link weights. These
results have implications for the control of real networked systems with
self-similarity.Comment: 7 pages, 4 figures, 1 table; revised manuscript; added discussion
about the general case of DSFN; added 3 reference
Generation of uncorrelated random scale-free networks
Uncorrelated random scale-free networks are useful null models to check the
accuracy an the analytical solutions of dynamical processes defined on complex
networks. We propose and analyze a model capable to generate random
uncorrelated scale-free networks with no multiple and self-connections. The
model is based on the classical configuration model, with an additional
restriction on the maximum possible degree of the vertices. We check
numerically that the proposed model indeed generates scale-free networks with
no two and three vertex correlations, as measured by the average degree of the
nearest neighbors and the clustering coefficient of the vertices of degree ,
respectively
Simplicial Homology for Future Cellular Networks
Simplicial homology is a tool that provides a mathematical way to compute the
connectivity and the coverage of a cellular network without any node location
information. In this article, we use simplicial homology in order to not only
compute the topology of a cellular network, but also to discover the clusters
of nodes still with no location information. We propose three algorithms for
the management of future cellular networks. The first one is a frequency
auto-planning algorithm for the self-configuration of future cellular networks.
It aims at minimizing the number of planned frequencies while maximizing the
usage of each one. Then, our energy conservation algorithm falls into the
self-optimization feature of future cellular networks. It optimizes the energy
consumption of the cellular network during off-peak hours while taking into
account both coverage and user traffic. Finally, we present and discuss the
performance of a disaster recovery algorithm using determinantal point
processes to patch coverage holes
Self-Configuration and Self-Optimization Process in Heterogeneous Wireless Networks
Self-organization in Wireless Mesh Networks (WMN) is an emergent research area, which is becoming important due to the increasing number of nodes in a network. Consequently, the manual configuration of nodes is either impossible or highly costly. So it is desirable for the nodes to be able to configure themselves. In this paper, we propose an alternative architecture for self-organization of WMN based on Optimized Link State Routing Protocol (OLSR) and the ad hoc on demand distance vector (AODV) routing protocols as well as using the technology of software agents. We argue that the proposed self-optimization and self-configuration modules increase the throughput of network, reduces delay transmission and network load, decreases the traffic of HELLO messages according to networkâs scalability. By simulation analysis, we conclude that the self-optimization and self-configuration mechanisms can significantly improve the performance of OLSR and AODV protocols in comparison to the baseline protocols analyzed
Autonomous Interconnection of Heterogeneous Networks
In today\u27s and future networks heterogeneity tends to grow which complicates configuration of devices and communication between them. This dissertation introduces a generic solution for auto-detecting and interconnecting heterogeneous networks in a self-organizing and scalable manner. As a result, this approach relieves users and administrators from complicated configuration and enables the deployment of P2P applications in heterogeneous networks
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