On Efficient Traffic Distribution for Disaster Area Communication Using Wireless Mesh Networks
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In recent time, a great deal of research effort has been directed toward promptly facilitating post-disaster communication by using wireless mesh networks (WMNs). WMN technology has been considered to be effectively exploited for this purpose as it provides multi-hop communication through an access network comprising wireless mesh routers, which are connected to the Internet through gateways (GWs). One of the critical challenges in using WMNs for establishing disaster-recovery networks is the issue of distributing traffic among the users in a balanced manner in order to avoid congestion at the GWs. To overcome this issue, we envision a disaster zone WMN comprising a network management center. First, we thoroughly investigate the problem of traffic load balancing amongst the GWs in our considered disaster zone WMN. Then, we develop traffic load distribution techniques from two perspectives. Our proposal from the first perspective hinges upon a balanced distribution of the bandwidth to be allocated per user. On the other hand, our second perspective considers the dynamic (i.e., varying) bandwidth demands from the disaster zone users that requires a more practical and refined distribution of the available bandwidth by following an intelligent forecasting method. The effectiveness of our proposals is evaluated through computer-based simulations.
KeywordsDisaster area network Traffic distribution Wireless mesh networks (WMNs)
Part of this work was conducted under the national project, “Research and development of technologies for realizing disaster-resilient networks”, supported by the Ministry of Internal Affairs and Communications (MIC), Japan. A preliminary version of this work was presented at the 1st IEEE International Conference on Communications in China (ICCC), August 2012.
- 1.Asplund, M., & Nadjm-Tehrani, S. (2009). A partition-tolerant manycast algorithm for disaster area networks. In IEEE international symposium on reliable distributed systems (SRDS).Google Scholar
- 2.Shibata, Y., Sato, Y., Ogasawara, N., & Chiba, G. (2009). A disaster information system by ballooned wireless adhoc network. In International conference on complex, intelligent and software intensive systems (CISIS).Google Scholar
- 3.Ishizu, K., Murakami, H., & Harada, H. (2011). Cognitive wireless network infrastructure and restoration activities for the earthquake disaster. In International symposium on wireless personal multimedia communications (WPMC).Google Scholar
- 4.Fouda, M. M., Nishiyama, H., & Kato, N. (2012). A novel heuristic-based traffic distribution method for disaster zone wireless mesh networks. In IEEE international conference on communications in China (ICCC).Google Scholar
- 5.Liu, W., Nishiyama, H., Kato, N., Shimizu, Y., & Kumagai, T. (2012). A novel gateway selection method to maximize the system throughput of wireless mesh network deployed in disaster areas. In IEEE international symposium on personal, indoor and mobile radio communication (PIMRC).Google Scholar
- 6.Ngo, T., Nishiyama, H., Kato, N., Shimizu, Y., Mizuno, K., & Kumagai, T. (2013). On the throughput evaluation of wireless mesh network deployed in disaster areas. In International conference on computing, networking and communications (ICNC).Google Scholar
- 7.Wishart, R., Portmann, M., & Indulska, J. (2008). Evaluation of wireless mesh network handoff approaches for public safety and disaster recovery networks. In IEEE Australasian telecommunication networks and applications conference (ATNAC).Google Scholar
- 9.Lien, Y.-N., Jang, H.-C., & Tsai, T.-C. (2009). A MANET based emergency communication and information system for catastrophic natural disasters. In IEEE international conference on distributed computing systems workshops (ICDCSW).Google Scholar
- 10.Sato, G., Asahizawa, D., Takahata, K., & Shibata, Y. (2009). A combination of different wireless LANs to realize disaster communication network. In IEEE international conference on distributed computing systems workshops (ICDCSW).Google Scholar
- 11.Sakano, T., Fadlullah, Z. M., Kumagai, T., Takahara, A., Ngo, T., Nishiyama, H., et al. (2013). Disaster resilient networking—A new vision based on movable and deployable resource units (MDRUs). IEEE Network Magazine.Google Scholar
- 12.Bedi, P. K., Gupta, P., & Gupta, T. K. (2011). A congestion-aware and load-balanced geographic multipath routing protocol for WMN. In International conference on sustainable energy and intelligent systems (SEISCON).Google Scholar
- 13.Chen, J., Jia, J., Wen, Y., Zhao, D., & Liu, J. (2009). Optimization of resource allocation in multi-radio multi-channel wireless mesh networks. In International conference on hybrid intelligent systems (HIS).Google Scholar
- 14.Babu, Y. K., Babu, T. N., & Ramesh, B. (2011). Minimizing interference through channel assignment in multiradio wireless mesh networks. In International conference on advances in social networks analysis and mining (ASONAM).Google Scholar
- 15.Matlab, http://www.mathworks.com. Accessed 13 May 2013.
- 16.Bahr, M. (2007). Update on the hybrid wireless mesh protocol of IEEE 802.11s. In IEEE international workshop on enabling technologies and standards for wireless mesh networking (MeshTech).Google Scholar
- 17.Jain, R. K., Chiu, D.-M. W., & Hawe, W. R. (1984). A quantitative measure of fairness and discrimination for resource allocation in shared computer system, Research Report. Digital Equipment Corporation. http://www.cse.wustl.edu/~jain/papers/ftp/fairness. Accessed 13 May 2013.