The present research paper proposed SoA-Mist i.e. Results and discussion support the efficacy of proposed architecture for smart geospatial health applications. In the present paper, we proposed a secure mist Computing architecture that is validated on recently released public geospatial health dataset. Mist computing reduces latency and increases throughput by processing data near the edge of the network. The smart health paradigms employ Internet-connected wearables for tele-monitoring, diagnosis providing inexpensive healthcare solutions. Results and discussions showed the efficacy of the proposed system for enhanced analysis of geo-health big data generated from a variety of sensing frameworks. We compared the performance of the proposed framework with the state-of-the-art Cloud-SDI in terms of analysis time. In addition, we discussed energy savings, cost analysis and scalability of the proposed framework with respect to efficient data processing. Also, overlay analysis of geospatial data was implemented. The proposed framework had provision of lossless data compression for reduced data transfer. We conducted a case study on Malaria vector-borne disease positive maps of Maharastra state in India.
We built prototypes using Intel Edison and Raspberry Pi for studying the comparative performance. In this study, we developed and evaluated a Fog-based SDI framework named GeoFog4Health for mining analytics from geo-health big data. Fog computing is a paradigm where embedded computers are employed to increase the throughput and reduce latency at the edge of the network. Integration of SDI with cloud computing led to emergence of Cloud-SDI as a tool for transmission, processing and analysis of geospatial data.
Spatial Data Infrastructure (SDI) is an important framework for sharing geospatial big data using the web.
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