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Performance Evaluation of IoST–Mist–Fog–Cloud Framework for Geospatial Crime Data Visualization: A State Dependent Queueing Approach

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Abstract

Modern Information and Communication Technology, Internet of Spatial Things(IoT), cloud, fog, and mist computing enable an expansion of real-time geospatial applications in crime analysis. Due to their sensitivity to latency and QoS, these applications must process at the network’s edge, not on the central cloud servers. Mist nodes have the ability to cache low-volume geographical data that is regularly requested and then process that data using lightweight applications. Display the results of the geospatial data processing on the client’s devices or systems in accordance with their requirements.Computing in the mist and fog have been the focus of a significant amount of study recently, particularly in geospatial application contexts such as crime analysis and visualization. Real-time geospatial crime data visualization can be more efficient and productive through the mist computing framework. By keeping this in mind, the present research paper proposes the IoST-Mist–Fog–Cloud framework for the visualization of crime data. With the help of this proposed framework, it visualizes the geospatial crime data through the thin client and mobile client environment. In addition to this, it provides a one-of-a-kind analytical model that investigates a state-dependent service queuing strategy using the IoST–Mist–Fog–Cloud framework and the influence of state-dependent service time on the system’s overall performance. It explains some of the system’s characteristics, and numerical evaluations and simulations validate the system’s functionality. According to the evaluation’s findings, it can attain an adequate degree of precision and successfully offload tasks when it uses the framework presented.

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Authors and Affiliations

  1. School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, 751024, Odisha, India

    Sunil K. Panigrahi & G. B. Mund

  2. School of Computer Applications, Kalinga Institute of Industrial Technology, Bhubaneswar, 751024, Odisha, India

    Veena Goswami & Rabindra K. Barik

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  1. Sunil K. Panigrahi
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  2. Veena Goswami
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  3. G. B. Mund
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Correspondence to Rabindra K. Barik.

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Panigrahi, S.K., Goswami, V., Mund, G.B. et al. Performance Evaluation of IoST–Mist–Fog–Cloud Framework for Geospatial Crime Data Visualization: A State Dependent Queueing Approach. SN COMPUT. SCI. 5, 85 (2024). https://doi.org/10.1007/s42979-023-02400-0

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