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Fog-inspired framework for emergency rescue operations in post-disaster scenario

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Abstract

Time-critical applications, such as emergency systems, healthcare, or smart traffic monitoring, demand latency-aware and real-time decision-making. Excessive delays in rescue actions result in a significant death toll each year around the world. In such crucial situations, technologies like the Internet of Things (IoT) coupled with cloud and fog infrastructure might be extremely effective in post-disaster scenarios. This study investigates the opportunities and prospects, and of combining cloud computing services with fog computing to deliver emergency rescue assistance to victims. A hierarchical framework has been developed in this work that gathers and aggregates data from various IoT devices, undertakes preliminary data processing in fog nodes, and finds optimal paths when certain locations remain inaccessible due to disaster damage. When a victim’s health status is abnormal, an alert is issued to assist the victim, and the cloud server predicts the ambulance’s path so that it arrives with the minimal possibility of delay, preventing any deadly conditions. Also, evaluation of the model has demonstrated that by incorporating fog, significant average delay reduction can be achieved while ensuring better precision, recall value, and response time than the cloud-based systems.

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Data Availability Statement

Data sharing is not applicable as authors of the study do not give written consent for their data to be shared.

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Authors and Affiliations

  1. Department of Engineering & Technology, Guru Nanak Dev University, Regional Campus, Jalandhar, Punjab, India

    Kanika Saini & Sheetal Kalra

  2. Department of Computer Applications, National Institute of Technology, Kurukshetra, Haryana, India

    Sandeep K. Sood

Authors
  1. Kanika Saini
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  2. Sheetal Kalra
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  3. Sandeep K. Sood
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Correspondence to Kanika Saini.

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Saini, K., Kalra, S. & Sood, S.K. Fog-inspired framework for emergency rescue operations in post-disaster scenario. J Supercomput 79, 21057–21088 (2023). https://doi.org/10.1007/s11227-023-05475-x

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