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Recent decades have seen a significant increase in the frequency, intensity, and impact of natural disasters and other emergencies, forcing the governments around the world to make emergency response and disaster management national priorities. The growth of extremely large and complex datasets — commonly referred to as big data — and various advances in information and communications technology and computing now support more effective approaches to humanitarian relief, logistical coordination, overall disaster management, and long-term recovery in connection with natural disasters and emergency events. Leveraging big data and technological advances for emergency management has attracted considerable attention in the research community. However, the desired merging of big data and emergency management (BDEM) requires coordinated efforts to align and define interdisciplinary terminologies and methodologies. To date, the key concepts and technologies in this emerging research area have not been coherently discussed in a sufficiently broad and multidisciplinary manner. In this article, an international team presents an overview of the BDEM domain, highlighting a general framework and discussing key challenges from several perspectives. We introduce and summarize typical technologies and applications, organized into the six broad categories. Finally, we outline several directions of future research. 

Network disaster recovery is one of the greatest concerns for Mobile Network Operators (MNOs) and first responders during large-scale natural disasters such as earth- quakes. In many recent studies, wireless multi-hop networking has been demonstrated as an effective technique to quickly and efficiently extend the network coverage during disasters. In this paper, we specifically address the network deployment problem by proposing the Population-Aware Relay Placement (PARP) solution, which seeks the efficient deployment of a limited number of relays such that population coverage is maximized in the scenario of network disaster recovery. We provide a graph-based modeling and prove its NP-hardness accordingly. In order to efficiently solve this problem, we propose a heuristic solution, which is constructed in two steps. We first design a simple algorithm based on a disk graph to determine the Steiner locations, which is the biggest challenge in this problem. Then, we formulate the problem as an integer programming problem, which is inspired by the formulation of Prize-Collecting Steiner Tree (PCST). Thus, the integer problem is solved by exploring the similarity of the existing algorithm for PCST. To evaluate the proposed solution extensively, we present numerical results on both real-world and random scenarios, which validate the effectiveness of the proposed solution and show substantial improvement by comparing to the previous one. 

Natural disasters can result in severe damage to communication infrastructure, which leads to further chaos to the damaged area. After the disaster strikes, most of the victims would gather at the evacuation sites for food supplies and other necessities. Having a good communication network is very important to help the victims. In this paper, we aim at recovering the network from the still-alive mobile base stations to the out-of-service evacuation sites by using multi-hop relaying technique. We propose to reconstruct the post-disaster network in a capacity-aware way based on prize collecting Steiner tree. The purpose of the proposed scheme is to achieve high capacity connectivity ratio in a cost efficient way. To provide more accurate evaluation results, we evaluate the proposed scheme by using the real evacuation site and base station data in Tokyo area, and utilizing the big data analysis based post-disaster service availability model.