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The wireless networks and devices are becoming popular, and they provide users access to information and communication anytime and anywhere (Barolli, Sakamoto, Barolli, & Takizawa, 2018; Goto, Sasaki, Hara, & Nishio, 2013; Matsuo, et al., 2018; Sakamoto, et al., 2019). Wireless Mesh Networks (WMNs) are gaining a lot of attention because of their low-cost nature that makes them attractive for providing wireless Internet connectivity. A WMN is dynamically self-organized and self-configured, with the nodes in the network automatically establishing and maintaining mesh connectivity among themselves (creating, in effect, an ad hoc network). This feature brings many advantages to WMN, such as low up-front cost, easy network maintenance, robustness, and reliable service coverage (Akyildiz, Wang, & Wang, 2005). Moreover, such infrastructure can be used to deploy community networks, metropolitan area networks, municipal and corporative networks, and to support applications for urban areas, medical, transport and surveillance systems.
Mesh node placement in WMNs can be seen as a family of problems, which are computationally hard to solve for most of the formulations (Franklin & Murthy, 2007; Muthaiah & Rosenberg, 2008; Vanhatupa, Hannikainen, & Hamalainen, 2007; Lim, Rodrigues, Wang, & Xu, 2005; Maolin, 2009; Wang, Xie, Cai, & Agrawal, 2007). In previous works, intelligent algorithms have been recently investigated for node placement problem (Sakamoto, Ozera, Ikeda, & Barolli, 2018; Girgis, Mahmoud, Abdullatif, & Rabie, 2014; Naka, Genji, Yura, & Fukuyama, 2003).
In (Sakamoto S., Oda, Ikeda, Barolli, & Xhafa, 2016), we implemented a Particle Swarm Optimization (PSO) based simulation system, called WMN-PSO. Also, we implemented another simulation system based on Genetic Algorithm (GA), called WMN-GA (Barolli, et al., 2018), for solving node placement problem in WMNs. Then, we designed and implemented a hybrid simulation system based on PSO and Distributed GA (DGA). We call this system WMN-PSODGA. The network connectivity is measured by Size of Giant Component (SGC), while the user coverage is the number of mesh client nodes that fall within the radio coverage of at least one mesh router node and is measured by Number of Covered Mesh Clients (NCMC). For load balancing, we added in the fitness function a new parameter called NCMCpR (Number of Covered Mesh Clients per Router).
By using the WMN-PSODGA, we can explore different router replacement methods and various distributions of mesh clients. This is important because each router replacement method does not achieve the same performance on every distribution of mesh clients. Moreover, every distribution of mesh clients yields different results for different WMN applications since the topology of a WMN can vary widely. In this paper, we consider Chi-square and Normal distributions of mesh clients and carry out a comparative study for different router replacement methods.
The rest of the paper is organized as follows. In the next section, we present our designed and implemented hybrid simulation system. Then, we give the simulation results. Finally, we present conclusions and future work.