A Framework for Dynamic Network Architecture and Topology Optimization

A new paradigm in wireless network access is presented and analyzed. In this concept, certain classes of wireless terminals can be turned temporarily into an access point (AP) anytime while connected to the Internet. This creates a dynamic network architecture (DNA) since the number and location of...

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Vydáno v:IEEE/ACM transactions on networking Ročník 24; číslo 2; s. 717 - 730
Hlavní autoři: Shams Shafigh, Alireza, Lorenzo, Beatriz, Glisic, Savo, Perez-Romero, Jordi, DaSilva, Luiz A., MacKenzie, Allen B., Juha Roning
Médium: Journal Article Publikace
Jazyk:angličtina
Vydáno: New York IEEE 01.04.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Access point selection
Architecture (computers)
Clusters
Computer networks
Deoxyribonucleic acid
DNA
Dynamics
Economic model
Economic models
Economics
Enginyeria de la telecomunicació
Genetic algorithms
Network optimization
Network topology
Networks
Optimization
Ordinadors, Xarxes d
Quality of service
quality of service (QoS)
Security
Security investment
Telemàtica i xarxes d'ordinadors
Topology
Topology reconfiguration
Traffic dynamics
Wireless networks
Àrees temàtiques de la UPC
ISSN:1063-6692, 1558-2566
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Shrnutí:A new paradigm in wireless network access is presented and analyzed. In this concept, certain classes of wireless terminals can be turned temporarily into an access point (AP) anytime while connected to the Internet. This creates a dynamic network architecture (DNA) since the number and location of these APs vary in time. In this paper, we present a framework to optimize different aspects of this architecture. First, the dynamic AP association problem is addressed with the aim to optimize the network by choosing the most convenient APs to provide the quality-of-service (QoS) levels demanded by the users with the minimum cost. Then, an economic model is developed to compensate the users for serving as APs and, thus, augmenting the network resources. The users' security investment is also taken into account in the AP selection. A preclustering process of the DNA is proposed to keep the optimization process feasible in a high dense network. To dynamically reconfigure the optimum topology and adjust it to the traffic variations, a new specific encoding of genetic algorithm (GA) is presented. Numerical results show that GA can provide the optimum topology up to two orders of magnitude faster than exhaustive search for network clusters, and the improvement significantly increases with the cluster size.
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ISSN:1063-6692
1558-2566
DOI:10.1109/TNET.2014.2383437