Bridging the Gap with Sensor Networks and Cloud Computing in the Geospatial Web for Optimized Data Services

Implementing cloud computing and data networks for local internet services is a priority. This research addresses the issue by introducing a novel approach to enhance the quality of regional internet data services. The proposed technique, Geographical Data Fusion with Dynamic Cloud Resource Allocation (GDF-DCRA), enhances map analytics by combining geographical data fusion with dynamic cloud resource allocation. GDF-DCRA enhances the speed of geospatial analytics by reducing sensor data, efficiently allocating cloud resources, and integrating spatial and temporal data. It consistently outperforms six industry benchmarks in terms of speed, latency, scalability, data accuracy, bandwidth efficiency, fault tolerance, and resource utilisation. Despite the increasing difficulty of working with real-time location data, the proposed approach has the potential to address these challenges.