Network topology

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

Virtual backbone generation and maintenance in ad hoc network mobility management

Abstract: In this paper, we present the implementation issues of a virtual backbone that supports the operations of the uniform quorum system (UQS) and the randomized database group (RDG) mobility management schemes in an ad hoc network. The virtual backbone comprises nodes that are dynamically selected to contain databases that store the location information of the network nodes. Together with the UQS and RDG schemes, the virtual backbone allows both dynamic database residence and dynamic database access, which provide high degree of location data availability and reliability. We introduce a distributed database coverage heuristic (DDCH), which is equivalent to the centralized greedy algorithm for virtual backbone generation, but only requires local information exchange and local computation. We show how DDCH can be employed to dynamically maintain the structure of the virtual backbone, along with database merging, as the network topology changes. We also provide a means to maintain connectivity among the virtual backbone nodes. We discuss optimization issues of DDCH through simulations. Simulation results suggest that the cost of ad hoc mobility management with a virtual backbone can be far below that of the conventional link-state routing.

MobiCent: a Credit-Based Incentive System for Disruption Tolerant Network

Abstract: When Disruption Tolerant Network (DTN) is used in commercial environments, incentive mechanism should be employed to encourage cooperation among selfish mobile users. Key challenges in the design of an incentive scheme for DTN are that disconnections among nodes are the norm rather than exception and network topology is time varying. Thus, it is difficult to detect selfish actions that can be launched by mobile users or to pre-determine the routing path to be used. In this paper, we propose MobiCent, a credit-based incentive system for DTN. While MobiCent allows the underlying routing protocol to discover the most efficient paths, it is also incentive compatible. Therefore, using MobiCent, rational nodes will not purposely waste transfer opportunity or cheat by creating non-existing contacts to increase their rewards. MobiCent also provides different payment mechanisms to cater to client that wants to minimize either payment or data delivery delay.

A Time Division Beacon Scheduling Mechanism for IEEE 802.15.4/Zigbee Cluster-Tree Wireless Sensor Networks

Abstract: While the IEEE 802.15.4/Zigbee protocol stack is being considered as a promising technology for low-cost low-power wireless sensor networks (WSNs), several issues in their specifications are still open. One of those ambiguous issues is how to build a synchronized cluster-tree network, which is quite suitable for ensuring QoS support in WSNs. In fact, the current IEEE 802.15.4/Zigbee specifications restrict the synchronization in the beacon-enabled mode (by the generation of periodic beacon frames) to star-based networks, while they support multi-hop networking using the peer-to-peer mesh topology, but with no synchronization. Even though both specifications mention the possible use of cluster-tree topologies, which combine multi-hop and synchronization features, the description on how to effectively construct such a network topology is missing. This paper tackles this problem, unveiling the ambiguities regarding the use of the cluster-tree topology and proposing a synchronization mechanism based on Time Division Beacon Scheduling to construct cluster-tree WSNs. We also propose a methodology for an efficient duty-cycle management in each router (cluster-head) of a cluster-tree WSN that ensures the fairest use of bandwidth resources. The feasibility of the proposal is clearly demonstrated through an experimental test bed based on our own implementation of the IEEE 802.15.4/Zigbee protocols.

Analysing information flows and key mediators through temporal centrality metrics

Abstract: The study of influential members of human networks is an important research question in social network analysis. However, the current state-of-the-art is based on static or aggregated representation of the network topology. We argue that dynamically evolving network topologies are inherent in many systems, including real online social and technological networks: fortunately the nature of these systems is such that they allow the gathering of large quantities of finegrained temporal data on interactions amongst the network members.In this paper we propose novel temporal centrality metrics which take into account such dynamic interactions over time. Using a real corporate email dataset we evaluate the important individuals selected by means of static and temporal analysis taking two perspectives: firstly, from a semantic level, we investigate their corporate role in the organisation; and secondly, from a dynamic process point of view, we measure information dissemination and the role of information mediators. We find that temporal analysis provides a better understanding of dynamic processes and a more accurate identification of important people compared to traditional static methods.

Optimal network alignment with graphlet degree vectors.

Abstract: Important biological information is encoded in the topology of biological networks. Comparative analyses of biological networks are proving to be valuable, as they can lead to transfer of knowledge between species and give deeper insights into biological function, disease, and evolution. We introduce a new method that uses the Hungarian algorithm to produce optimal global alignment between two networks using any cost function. We design a cost function based solely on network topology and use it in our network alignment. Our method can be applied to any two networks, not just biological ones, since it is based only on network topology. We use our new method to align protein-protein interaction networks of two eukaryotic species and demonstrate that our alignment exposes large and topologically complex regions of network similarity. At the same time, our alignment is biologically valid, since many of the aligned protein pairs perform the same biological function. From the alignment, we predict function of yet unannotated proteins, many of which we validate in the literature. Also, we apply our method to find topological similarities between metabolic networks of different species and build phylogenetic trees based on our network alignment score. The phylogenetic trees obtained in this way bear a striking resemblance to the ones obtained by sequence alignments. Our method detects topologically similar regions in large networks that are statistically significant. It does this independent of protein sequence or any other information external to network topology.