A Probabilistic Approach to Deploying Disaster Response Network

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Disasters, in most cases, cause the breakdown of important structures, such as communication and Internet connection. This is concerning because victims need a way to communicate, either to contact family and loved ones for emotional support or those responsible for disaster relief in order to coordinate rescue and safety measures. Because these scenarios are characterized by a lack of infrastructure, one attractive solution is to bring in mobile communication units to provide connection as part of disaster relief. This is the basis behind moveable and deployable resource unit (MDRU) based wireless mesh networks (WMN), where the MDRU is a communication equipment, usually mounted on a vehicle, that is deployed into a disaster area to establish a WMN (i.e., a mostly static, highly interconnected network) with an Internet connection. Here, the MDRU is the one responsible for connecting victims to exterior networks. However, MDRU based WMNs are composed of different hierarchical levels, such as routers and gateways, as well as the MDRU and users themselves. Despite this, existing literature model it as a flat network. In this paper, we propose a hierarchical model of the MDRU based WMN, which is more realistic than conventional research. Additionally, our model exposes an interlevel tradeoff relationship between the number of routers and performance in this network, which proves that too little or too many routers both lead to low quality of experience. Our research, thus, analyzes this tradeoff and provides the optimal configuration that maximizes throughput.

Original languageEnglish
Article number8477999
Pages (from-to)12086-12094
Number of pages9
JournalIEEE Transactions on Vehicular Technology
Volume67
Issue number12
DOIs
Publication statusPublished - 2018 Dec

Keywords

  • Wireless mesh networks, disaster response networks, moveable and deployable resource unit, probabilistic modeling

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