An evolutionary computational approach for optimizing connectivity in disaster scenarios
|Author||Gutiérrez Reina, Daniel
Toral, S. L.
|Department||Universidad de Sevilla. Departamento de Ingeniería Electrónica|
|Abstract||This article presents an evolutionary computation approach for increasing
connectivity in disaster scenarios. Connectivity is considered to be of critical
importance in disaster scenarios due to constrained and mobile ...
This article presents an evolutionary computation approach for increasing connectivity in disaster scenarios. Connectivity is considered to be of critical importance in disaster scenarios due to constrained and mobile conditions. Herein, we propose the deployment of a number of auxiliary static nodes which their purpose is to increase the reachability of broadcast emergency packets among the nodes which are participating in the disaster scenario. These nodes represent people and vehicles acting in rescue operations. The main goal is to find the optimum positions for the auxiliary nodes, reinforcing the communications in points where certain lack of connectivity is found. These points will depend on the movements of the rescue teams which are influenced by tactical reasons. Due to the complexity of the problem and the number of parameters to be considered, a genetic algorithm combined with the network simulator NS-2 is proposed to find the optimum positions of the auxiliary nodes. Specifically, NS- 2 is used to model the communication layers and provide the fitness function guiding the genetic search. The proposed approach has been tested using the disaster mobility model included in the motion generator BonnMotion. The simulation results that have been obtained demonstrate the feasibility of the proposed approach and illustrate its applicability in other scenarios where certain lack of connectivity is evident
|Citation||Gutiérrez Reina, D., Toral, S.L., Bessis, N., Barrero, F. y Asimakopoulou, E. (2013). An evolutionary computation approach for optimizing connectivity in disaster scenarios. Applied Soft Computing, 13 (2)|