The smart city is an ecosystem that interconnects various devices like sensors, actuators, mobiles, and vehicles. The intelligent and connected transportation system (ICTS) is an essential part of this ecosystem that provides new real-time applications. The emerging applications are based on Internet-of-Things (IoT) technologies, which bring out new challenges, such as heterogeneity and scalability, and they require innovative communication solutions. The existing routing protocols cannot achieve these requirements due to the surrounding knowledge supported by individual nodes and their neighbors, displaying partial visibility of the network. However, the issue grew ever more arduous to conceive routing protocols to satisfy the ever-changing network requirements due to its dynamic topology and its heterogeneity. Software-Defined Networking (SDN) offers the latest view of the entire network and the control of the network based on the application’s specifications. Nonetheless, one of the main problems that arise when using SDN is minimizing the transmission delay between ubiquitous nodes. In order to meet this constraint, a well-attended and realistic alternative is to adopt the Machine Learning (ML) algorithms as prediction solutions. In this paper, we propose a new routing protocol based on SDN and Naive Bayes solution to improve the delay. Simulation results show that our routing scheme outperforms the comparative ones in terms of end-to-end delay and packet delivery ratio.
In this article we propose a mixed 0-1 linear programming model for the topological network design problem with modular switches such as the ones that will be used in asynchronous transfer mode (ATM) frame relay and other broadband networks. The model includes the location of switches, their configuration with respect to ports and multiplexers, the design of an access network with a star topology, and a backbone network with a fixed topology (ring or tree). To obtain a solution, we propose a greedy heuristic that provides a good starting solution, and a tabu search heuristic to improve the solution. Finally, we present an example of the application of the heuristics and results for a set of randomly generated problems with up to 500 users and 30 potential switch sites. For the hundreds of problems generated, the tabu algorithm produced solutions that were, on average, within 1.5% of the optimal solution, and in the worst case within 4.95% of the optimal solution.
In this paper, we tackle the problem of how to expand Internet protocol (IP) networks with performance guarantees in a cost-effective way. We firsi propose an optimization model that deals wilh selecting the location of the routers, selecting their types, expanding (he access and the backbone networks and selecting the transmission link types. The model also considers the routing of the IP traffic using a standardized routing protocol. The pcrtbrmance guarantees we refer to are bandwidth guarantees between the clients' routers. The performance guarantees stand not only for the nonnal state of the network, but also for all failure scenarios of interest to the network planner. A tabu-based metaheuristic algorithm is proposed to find solutions of the model. Numerical results show that "good" solutions can be found using the proposed algorithm.Keywords: Internet protocol (IP): IP network planning; access and backbone networks; metaheuristic; tabu search RESUME Dans cet article, nous traitons du probleme de I'expansion des r^seaux IP (Internet Protocol) avec garantic de pedbrmance el ce en minimisant le cout du icseau. Dans un premier temps, nous proposons un modele comprenant la localisation et la configuration dcs routeurs, I'expansion des reseaux d'acces et etendu et le choix des liens dc transmission. De plus, la garanlie de performance est desiree non seutement pour I'ctat normal du reseau mais aussi pour les scenarios de pannes d'interets pour ie planiHcateur du teseau. Ensuite, nous presentons une mctaheuristique basee sur la recherche avec tabous pour trouver des solutions. Lcs lesultats numeriques montrent que de bonnes solutions peuvent etre obtenues avec l'approche proposee.Mots-cles : Internet protocol (IP); planification de rcseaux; reseaux d'acces et etendu; metaheuristique; recherche avec tabous
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