| 研究実績の概要 |
In this year, we have taken the following two research problems in this project. (1) How to optimal communications network in a disaster situation; (2) How to understand situations through big data analytics.
More specifically,
(1) In recent studies, Deployable Base Stations (DBSs) have demonstrated their ability to reconstruct an ECN. However, considering limited resources, it is impossible to deploy DBSs in the whole disaster area. The above shortage can be covered by deploying small-cell networks (i.e., low-power transmission base stations ) in areas with high communication demand, e.g., in refuges. Considering the above 2-tier ECN, in this work, we study its performance and optimization issue with the objective of minimizing number/density of DBSs while guaranteeing the quality-of-experience (QoE), i.e., coverage probability to users. We investigate optimization method to minimize the number/density of DBSs. We used Monte Carlo methods with various parameter choices to evaluate the results and to determine the accuracy of our evaluation.
(2) Cooperating with other fundings, we have performed detailed survey on big data analytics to understand situations happened in disaster scenarios. We have surveyed big data analytics from both the content and the spatial points of view. From the content point of view, we survey existing data mining and analysis techniques, and further study the possibilities to understand situations in disaster situation. From the spatial point of view, we discuss the most popular methods and further discuss the possibility to understand situations.
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