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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 et al., 2018; Goto, Sasaki, Hara, & Nishio, 2013; Inaba et al., 2015; Inaba et al., 2016; Matsuo et al., 2018; Sakamoto et al., 2014; Sakamoto et al., 2017; Sakamoto et al., 2018; Sakamoto et al., 2019). The Wireless Mesh Networks (WMNs) are gaining a lot of attention because of their low-cost, which makes them attractive for providing wireless Internet connectivity. The nodes in WMN automatically establish and maintain the mesh connectivity among themselves. This feature has many advantages to WMN such as low up-front cost, easy network maintenance, robustness and reliable service coverage (Akyildiz, Wang, & Wang, 2005). Also, the WMNs can be used to deploy in 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). Node placement problems are known to be computationally hard to solve (Lim, Rodrigues, Wang, & Xu, 2005; Maolin, 2009; Wang, Xie, Cai, & Agrawal, 2007). In some previous works, intelligent algorithms have been recently investigated for node placement problem (Barolli et al., 2018; Girgis, Mahmoud, Abdullatif, & Rabie, 2014; Naka, Genji, Yura, & Fukuyama, 2003; Sakamoto et al., 2013; Sakamoto et al., 2014; Sakamoto et al., 2016; Sakamoto et al., 2017).
In (Sakamoto, 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 (Sakamoto et al., 2014), 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). We considered also the load balancing problem by adding in the fitness function a new parameter called number of covered mesh clients per router (NCMCpR).
In this paper, we consider Exponential, Weibull, and Normal distributions of mesh clients and carry out a comparative study.
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.