Network topology

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

Orbis: rescaling degree correlations to generate annotated internet topologies

Abstract: Researchers involved in designing network services and protocols rely on results from simulation and emulation environments to understand their application performance and scalability. To better understand the behavior of these applications and predict their performance when deployed on the actual Internet, the generated topologies must closely match real network characteristics, not just in terms of graph structure (node interconnectivity) but also with respect to various node and link annotations. Relevant annotations include link latencies, AS membership and whether a router is a peering or internal router. Finally, it should be possible to rescale a given topology to a variety of sizes while still maintaining its essential characteristics.In this paper, we propose techniques to generate annotated, Internet router graphs of different sizes based on existing observations of Internet characteristics. We find that our generated graphs match a variety of graph properties of observed topologies for a range of target graph sizes. While the best available data of Internet topology currently remains imperfect, the quality of our generated topologies will improve with the fidelity of available measurement techniques or next generation architectures that make Internet structure more transparent.

Efficient in-network moving object tracking in wireless sensor networks

Abstract: The rapid progress of wireless communication and embedded microsensing MEMS technologies has made wireless sensor networks possible In light of storage in sensors, a sensor network can be considered as a distributed database, in which one can conduct in-network data processing An important issue of wireless sensor networks is object tracking, which typically involves two basic operations: update and query This issue has been intensively studied in other areas, such as cellular networks However, the in-network processing characteristic of sensor networks has posed new challenges to this issue In this paper, we develop several tree structures for in-network object tracking which take the physical topology of the sensor network into consideration The optimization process has two stages The first stage tries to reduce the location update cost based on a deviation-avoidance principle and a highest-weight-first principle The second stage further adjusts the tree obtained in the first stage to reduce the query cost The way we model this problem allows us to analytically formulate the cost of object tracking given the update and query rates of objects Extensive simulations are conducted, which show a significant improvement over existing solutions

On singular topologies in optimum structural design

Abstract: A major obstacle to topological optimization is that the optimal topology might correspond to a singular point in the design space. Despite its crucial importance, the phenomenon of singular optimal topologies is not well understood.

Distributed Adaptive Tracking Control for Synchronization of Unknown Networked Lagrangian Systems

Abstract: This paper investigates the cooperative tracking control problem for a group of Lagrangian vehicle systems with directed communication graph topology. All the vehicles can have different dynamics. A design method for a distributed adaptive protocol is given which guarantees that all the networked systems synchronize to the motion of a target system. The dynamics of the networked systems, as well as the target system, are all assumed unknown. A neural network (NN) is used at each node to approximate the distributed dynamics. The resulting protocol consists of a simple decentralized proportional-plus-derivative term and a nonlinear term with distributed adaptive tuning laws at each node. The case with nonconstant NN approximation error is considered. There, a robust term is added to suppress the external disturbances and the approximation errors of the NNs. Simulation examples are included to demonstrate the effectiveness of the proposed algorithms.