Spelling suggestions: "subject:"link formation"" "subject:"sink formation""
1 |
An Efficient On-Demand Point-To-Point Piconet Formation Scheme for Bluetooth Personal Area NetworkLee, Song-Ying 03 September 2004 (has links)
In the short-range wireless communication and networking, Bluetooth is a promising technology, mainly used as a replacement for connected cables. Since the Bluetooth specification only defines how to build a Piconet, several solutions have been proposed to construct a Scatternet from the Piconets in the literatures. The process of constructing a Scatternet is called the Scatternet formation. The traditional scatternet formation has three defects: First, more power and time need to be consumed in order to construct the scatternet. Second, after the scatternet is formed, more power and bandwidth are required to maintain the connection of scatternet. Third, due to the restriction of topology, the communication between two nodes must be relayed through the bridge or master, even when they are in the communication range.
In this thesis, we propose a novel method in the transmission ranges of all the other nodes to form temporary point-to-point piconet only when two nodes want to communicate with each other. When the communication is finished, the temporary point-to-point piconet is destroyed immediately. Two nodes in the communication range can communicate with each other directly without the relay node. Our On-Demand Point-To-Point Piconet Formation (ODP2P) scheme resolves the defects of traditional scatternet formation in communication range. In order to reduce the communication delay, every node owns its list to record the information of all nodes within the communication range. An on-event method maintains the list. Network performance analysis and simulations show that our method can reduce the routing path significantly, provide better utilization for Bluetooth personal area network (PAN), and maintain the range list efficiently.
|
2 |
Generative Models of Link Formation and Community Detection in Continuous-Time Dynamic NetworksArastuie, Makan January 2020 (has links)
No description available.
|
3 |
Network, clusters and innovations : 3 essays / Réseaux, clusters et innovations : 3 essaisBehfar, Stefan kambiz 03 April 2017 (has links)
[...] Mes travaux portent sur les clusters structurant le réseau et l'innovation car 1) le cluster impacte collectivement plutôt qu’individuellement la sortie du réseau, 2) les couplages intra et inter-cluster représentent la structure même des clusters mais ils influencent différemment l'innovation ou la croissance du cluster, 3) un certain compromis reste à définir entre la structure dense et éparse des différents réseaux. Un cluster est de façon générale défini comme un groupe de choses similaires ou de personnes qui travaillent sur des sujets analogues. Selon le domaine auquel il s’applique, même si l’idée reste la même, la définition s’affine. En sciences des organisations, un cluster représente un regroupement d’entreprises et d’institutions qui interagissent entre-elles par le biais de contrats, d’opérations formelles ou informelles et de réunions occasionnelles afin de contribuer collectivement à un résultat innovant. [...] La thèse est structurée comme suit. Dans l'introduction générale, nous passons en revue la littérature des connaissances existantes qui sert de base pour le cadre conceptuel des documents. Nous définissons ensuite certains concepts utilisés dans les trois articles présentés tels que la structure de réseau complexe (utilisée dans le premier article), l'innovation et les liens de réseau (utilisés principalement dans le deuxième article), et la gestion des connaissances utilisées (dans le troisième article). Dans le premier article, nous discutons les différents mécanismes de formation de liens dictés par les réseaux dirigés permettant de distinguer la distribution des degrés. Dans le deuxième article, nous abordons l'impact de la dynamique de groupe sur l'innovation du groupe de projet OSS. Dans le troisième article, nous nous attachons à l'impact du transfert des connaissances à l'intérieur des groupes sur le transfert des connaissances entre les groupes. L'annexe A permettra de discuter la modélisation analytique de la croissance des réseaux sociaux en utilisant la projection de réseaux multicouches ; l'annexe B sera l’occasion de présenter statistiquement le lien entre les relations intragroupe et les relations intergroupe. / [...] However, there is a gap in the literature with regard to the analysis of cluster or group structure as an input and cluster or group innovation as an output, e.g. “impact of network cluster structure on cluster innovation and growth”, i.e. how intra- and inter-cluster coupling, structural holes and tie strength impact cluster innovation and growth; and how intra-cluster density affects inter-cluster coupling; that I address in my thesis.Therefore, I focus on the cluster (or group of individuals) rather than the individual to analyze both network structure and innovation, because 1) clusters represent collective impact on network output rather than individuals’ impact, 2) intra and inter cluster couplings both represent cluster structure but have different impacts on cluster innovation and growth, 3) trade-offs among dense and sparse network cluster structures are different from those associated with networks of individuals. [...] The thesis is structured as follows. In the general introduction, I review the literature of existing knowledge in the field, which serves as a basis for the conceptual framework for the papers. I then define certain concepts used in the papers, such as complex network structure used in the first paper, innovation and network ties mainly used in the second paper, and knowledge management used in the third paper. In the first paper I discuss directed networks’ different link formation mechanisms causing degree distribution distinction. In the second paper, I discuss the impact of group dynamics on OSS project group innovation. In the third paper, I discuss impact of knowledge transfer inside groups onto knowledge transfer between groups. In appendix A, I discuss analytical modeling of social network growth using multilayer network projection; and in appendix B, I discuss statistically how intragroup ties and intergroup ties are related.
|
Page generated in 0.0846 seconds