LAUSR

GeoSpatial (GS) data plays an important role for decision-making in different sectors such as engineering, economic, political, environmental, and social aspects. Internet of Spatial Things (IoST) is concerned with revising the Internet of Things (IoT) with the spatial perspective. Cloud environment is used to transmit, process, and analysis a huge amount of GS data. Fog computing is a paradigm, where embedded computers are employed to increase the throughput and reduce latency at the edge of the network. In this paper, an efficient GS Data framework for a Fog environment (GSDFog) is proposed to improve the performance of transmission, process, and analysis of a huge amount of GS data. It has three main contributions, A queuing model is used to balance the load over the available resources, this model considers two types of requests to optimally manage the query and storage operations. A novel GSDFog framework that uses the Pundc cloud instead of the traditional QGIS cloud is used for fog environment to process GS requests. Finally, GSDFog has been implemented and tested on a case study that uses tourism GS data in Port Said city to evaluate the performance of the proposed framework. The proposed GSDFog framework is compared against the traditional QGIS application in different scenarios. The proposed framework is implemented and tested on k6 open-source testing tool. The results show that the GSDFog framework outperforms the QGIS in terms of number of handled requests, average response time, and CPU utilization.