Effects of population size for location-aware node placement in WMNs: evaluation by a genetic algorithm--based approach

  • Authors:

  • Tetsuya Oda;Admir Barolli;Evjola Spaho;Leonard Barolli;Fatos Xhafa;Muhammad Younas

  • Affiliations:

  • Graduate School of Engineering, Fukuoka Institute of Technology, Higashi-ku, Fukuoka, Japan 811-0295;Department of Computers and Information Science, Seikei University, Musashino-Shi, Japan 180-8633;Graduate School of Engineering, Fukuoka Institute of Technology, Higashi-ku, Fukuoka, Japan 811-0295;Department of Information and Communication Engineering, Fukuoka Institute of Technology (FIT), Japan, Higashi-ku, Fukuoka, Japan 811-0295;Department of Languages and Informatics Systems Technical, University of Catalonia, Barcelona, Spain 08034;Department of Computing and Communication Technologies, Oxford Brookes University, Oxford, UK OX33 1HX

  • Venue:
  • Personal and Ubiquitous Computing
  • Year:
  • 2014

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Abstract

Wireless mesh networks (WMNs) are cost-efficient networks that have the potential to serve as an infrastructure for advanced location-based services. Location service is a desired feature for WMNs to support location-oriented applications. WMNs are also interesting infrastructures for supporting ubiquitous multimedia Internet access for mobile or fixed mesh clients. In order to efficiently support such services and offering QoS, the optimized placement of mesh router nodes is very important. Indeed, such optimized mesh placement can support location service managed in the mesh and keep the rate of location updates low. This node location-based problem has been shown to be NP-hard and thus is unlikely to be solvable in reasonable amount of time. Therefore, heuristic methods, such as genetic algorithms (GAs), are used as resolution methods. In this paper, we deal with the effect of population size for location-aware node placement in WMNs. Our WMN-GA system uses GA to determine the positions of the mesh routers and mesh clients in the grid area. We used a location-aware node placement of mesh router in cells of considered grid area to maximize network connectivity and user coverage. We evaluate the performance of the proposed and implemented WMN-GA system for low and high density of clients considering different distributions and considering giant component and number of covered users parameters. The simulation results show that for low-density networks, with the increasing of population size, GA obtains better result. However, with the increase in the population size, the GA needs more computational time. The proposed system has better performance in dense networks like hot spots for Weibull distribution when the population size is big.