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Showing papers on "Routing protocol published in 1970"


DOI
01 Jan 1970
TL;DR: In this article, the authors present fundamental models and methods for the geometric description of real networks with a focus on applications of real network maps, including decentralized routing protocols, geometric community detection, and the self-similar multiscale unfolding of networks by geometric renormalization.
Abstract: Real networks comprise from hundreds to millions of interacting elements and permeate all contexts, from technology to biology to society. All of them display non-trivial connectivity patterns, including the small-world phenomenon, making nodes to be separated by a small number of intermediate links. As a consequence, networks present an apparent lack of metric structure and are difficult to map. Yet, many networks have a hidden geometry that enables meaningful maps in the two-dimensional hyperbolic plane. The discovery of such hidden geometry and the understanding of its role have become fundamental questions in network science giving rise to the field of network geometry. This Element reviews fundamental models and methods for the geometric description of real networks with a focus on applications of real network maps, including decentralized routing protocols, geometric community detection, and the self-similar multiscale unfolding of networks by geometric renormalization.

6 citations


Journal ArticleDOI
01 Jan 1970
TL;DR: The Neuro-Fuzzy model outperformed by saving more than 32% of energy than the random model with 50 and 100-sensor node deployment and it was confirmed that by increasing the number of sensor nodes, it was possible to increase the energy utilization but not the energy saved from the network.
Abstract: Wireless sensor network (WSN) is one of the recent technologies in communication and engineering world to assist various civilian and military applications. It is deployed remotely in severe environment that doesn’t have an infrastructure. Energy is a limited resource that needs efficient management to work without any failure. Energy efficient clustering of WSN is the ultimate mechanism to conserve energy for long time. The major objective of this research was to efficiently consume energy based on the Neuro-Fuzzy approach particularly adaptive Neuro fuzzy inference system (ANFIS). The significance of this study was to examine the challenges of energy efficient algorithms and the network lifetime on WSN so that it could assist several applications. Clustering is one of the hierarchical based routing protocols, which manage the communication between sensor nodes and sink via Cluster Head (CH); CH is responsible for sending and receiving information from multiple sensor nodes and multiple sink nodes. There are various algorithms that can efficiently select appropriate CH and localize the membership of cluster with fuzzy logic classification parameters to minimize periodic clustering which consumes more energy and we have applied neural network learning algorithm to learn various patterns based on the fuzzy rules and measured how much energy was saved from random clustering. Finally, we compared it to our Neuro-Fuzzy logic and consequently demonstrated that our Neuro-Fuzzy model outperformed by saving more than 32% of energy than the random model with 50 and 100-sensor node deployment. We confirmed that by increasing the number of sensor nodes, it was possible to increase the energy utilization but not the energy saved from the network.

5 citations


Journal ArticleDOI
TL;DR: A parallel implementation of genetic algorithms is applied to routing protocols with low bandwidth consumption, including the (LSP) link state packet protocols and a strategy is proposed that allows the algorithm to replace segments of the entire path.
Abstract: A parallel implementation of genetic algorithms is applied to routing protocols with low bandwidth consumption. In particular, the paper discusses the (LSP) link state packet protocols. The first part of the paper deals with network topology and transmission parameters, together with the structure for storing the network. The second part discusses the Genetic Algorithm implementation. To this end, it considers the generation of the initial population that is a subset of all the possible paths connecting couples of nodes. As far as the mating and mutation policy is concerned, a strategy is proposed that allows the algorithm to replace segments of the entire path The implementation is carried out in parallel, thus letting different populations to evolve separately. Subsets of different populations migrate periodically to avoid the populations to persist in some local minima. These are the equilibrium states, where no better path, with a lower cost, can be found for a given period. As for conclusions, comparisons between the results of the sequential and distributed implementations of Genetic Algorithms are reported.

2 citations


Proceedings ArticleDOI
01 Dec 1970
TL;DR: Simulation experiments indicate that adaptive routing techniques can be effective in large communications networks.
Abstract: This paper is concerned with the behavior of an adaptive routing system in a large communications network. The adaptive routing algorithm described uses stochastic switching matrices to automatically find and complete the traffic paths through a system. A realistic network and traffic which were derived from military field exercises are used to illustrate the real time behavior of the algorithm. Simulation experiments indicate that adaptive routing techniques can be effective in large communications networks.

1 citations