Network topology

About: Network topology is a research topic. Over the lifetime, 52259 publications have been published within this topic receiving 1006627 citations.

Optical network design with mixed line rates and multiple modulation formats

Abstract: We propose a design method for mixed-line-rate (MLR) optical networks with transceivers employing different modulation formats. Our results demonstrate the tradeoff between a transceiver's cost and its optical reach in overall network design.

Sensor Network Optimization Using a Genetic Algorithm

Abstract: In this paper, we propose an efficient method based on genetic algorithms (GAs) to solve a sensor network optimization problem. Long communication distances between sensors and a sink (or destination) in a sensor network can greatly drain the energy of sensors and reduce the lifetime of a network. By clustering a sensor network into a number of independent clusters using a GA, we can greatly minimize the total communication distance, thus prolonging the network lifetime. Simulation results show that our algorithm can quickly find a good solution. This approach is also applicable to multiple network topologies (uniform or non-uniform) or shortest distance optimization problems . Keyword: Genetic algorithm, clustering, network optimization, shortest distance.

Interference-aware topology control for wireless sensor networks

Abstract: Topology control has been well studied in wireless ad hoc networks. However, only a few topology control methods (e.g. (1)) take into account the low interference as a goal of the methods. Some researchers tried to indirectly reduce the interference by reducing the transmission power or by devising low degree topologies, but none of those protocols can guarantee low interference. In this paper we present several algorithms to construct network topologies such that the maximum (or average) link (or nodal) interference of the topology is either minimized or approximately minimized. The algorithms and definitions introduced in this paper are not based on any geometry information about the nodes and they work for any graph models of wireless communication. The theoretical results are corroborated by simulation studies.

Machine learning for network automation: overview, architecture, and applications [Invited Tutorial]

Abstract: Networks are complex interacting systems involving cloud operations, core and metro transport, and mobile connectivity all the way to video streaming and similar user applications.With localized and highly engineered operational tools, it is typical of these networks to take days to weeks for any changes, upgrades, or service deployments to take effect. Machine learning, a sub-domain of artificial intelligence, is highly suitable for complex system representation. In this tutorial paper, we review several machine learning concepts tailored to the optical networking industry and discuss algorithm choices, data and model management strategies, and integration into existing network control and management tools. We then describe four networking case studies in detail, covering predictive maintenance, virtual network topology management, capacity optimization, and optical spectral analysis.