Spatial big data analysis is very important in disaster scenarios to understand distribution patterns of situations, e.g., people's movements, people's requirements, resource shortage situations, and so on. In a general case, spatial big data is generated from distributed sensing devices and analyzed in a centralized way, e.g., a cloud center with high-performance computing resources. However, data transmission from sensing devices to cloud centers always takes a long time, especially in disaster scenarios with an unstable network. Fog computing is a promising technique to solve the above problem by offloading data processing tasks from the cloud to nearby computation devices. But data resolution also decreases after local processing in the fog nodes. It is necessary to investigate the optimal task distribution solutions to efficiently use computation resources in the fog layer. In this paper, we take the above research problem, and study fog-computing supported spatial big data processing. We analyze the process for spatial clustering, which is a typical category for spatial data analysis, and propose an architecture to integrate data processing into fog computing. We formalize a problem to maximize the data-resolution efficiency by considering data resolution and delay. We further propose core algorithms to enable spatial clustering in a fog-computing environment and implement the above algorithms in a real system. We have performed both simulations and experiments on a real Twitter dataset collected when Kumamoto-city suffered an earthquake. Through the simulations and the experiments, we have determined that the proposed solution significantly outperforms the other solutions.

Big data analytics has started to use data collected from the sensors in smartphones. This data may be used by disaster response teams for locating problems. But the regular communication infrastructure can be destroyed after disasters. Movable base stations (MBS), as studied by the company NTT, offer an easily deployable solution to construct an emergency communication network~ (ECN), but are not suitable for transmitting big data from sensing devices to the cloud for data processing in the cloud. To address this issue, MBSs have been equipped with processing capabilities of their own, which creates an MBS-based Fog-computing Network. We proposed a novel algorithm to minimize the overall cost of the system while maintaining 0 data overflow. This will allow the resources to be used at the most efficient level. Our genetic algorithm solution had a reduced system cost over various network sizes when compared to some conventional solutions. During the simulation, it was clear that the best conventional method for preventing data overflow was the fog-based solution, but its cost was quite high. The cloud-based solution had the lowest cost but would lead to a large amount of data overflow, which would need to be cached. The GA-based solution maintained the ideal solution throughout the variation of all bandwidth parameters: the processing rate, the data compression ratio, and the cost coefficient ratio. Because none of the conventional solutions were able to match the capabilities of the GA for the current constraints, we believe that this should be investigated further with a faster algorithm.

Spatial big data analytics has become possible with the data collected from the sensors in smart phones, which can support decision-making in disaster scenarios. However, sometimes the regular communication infrastructure can be destroyed after disasters. Movable base stations (MBS), as studied by the company NTT, offer an easily deployable solution to construct an emergency communication network, but are not suitable for transmitting big data from sensing devices to the cloud for data processing in the cloud. To solve this issue, we studied a novel algorithm to process spatial big data efficiently in a wirelessly networked disaster area that uses multiple MBSs. More specifically, we proposed a novel algorithm to minimize overall delay for spatial data processing in wirelessly-networked disaster areas (SDP-WNDA), to enable quick responses to data analysis. Our proposed model and genetic algorithm solution showed to have a reduced maximum end- to-end (E2E) delay over various network sizes, when compared to some conventional solutions. For the realistic constraints, the cloud solution was the best conventional method, followed by the system which used the fog nodes to process as much data as possible, but the genetic algorithm (GA) had a slight advantage over all other methods. However, as the computation rate, μk, was increased, the maximum processing algorithm got much stronger. Also, as the communication capacity, R, was increased, the cloud computing solution was more successful. The fact that none of the conventional cases matched the capabilities of the GA for increased computation or increased transmission rates suggests the need for this to be investigated even further.