## 3 Research Framework of Internet of Emergency Services
### Computation Paradigms
#### 3.6.1 Cloud Computing
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline
**Reference** & **Contect of Use** & **Description of Use** \\ \hline  & & Fog-Cloud-focused Internet of Things-based cyber physical infrastructure gives rapid medical assistance and evacuation of trapped people a high priority. Physical subsystem gathering information from stranded people and the disaster zone and offers different information services to stakeholders (evacuation personnel and stranded individuals). Based on the medical data collected, a cyber subsystem at the fog layer diagnoses the health status of stranded people in real-time and performs analysis to reduce needles data transmission to the cloud. Using disaster-related health and environmental data at the cloud layer, the cyber subsystem employs Bayesian Belief Network to monitor the public health sensitivity of stranded people upon PHS diagnosis. Using data collected from the cloud layer on environmental disasters, the subsystem constructs an evacuation map. Subsystem develops an evacuation plan based on the evacuation map and measured panic sensitivity of people. \\ \hline  & & Choosing safe pathways for the crowd and preventing stampedes is conducted using a real-time building evacuation model using an enhanced cellular automata (CA) technique that blends cellular automata with the potential energy field (PEF) model from fluid dynamics theory (FDT). In order to sample fire and crowd data, run the intelligent evacuation algorithm, and direct the crowd with the signage system in real-time conditions, edge computing servers, custom-designed wireless sensors, AI enhanced surveillance cameras, intelligent emergency signage systems, and edge computing servers are all used. \\ \hline  & & 5G’s architectural components offer effective remote healthcare services as well as capabilities for emergency health monitoring and response. They proposed a comprehensive plan based on technological enablers such as fog computing and IoT for addressing common problems and existing restrictions that can jeopardize the promised service delivery. \\ \hline  & & Proposed a unified framework that integrates spatial data infrastructure, cloud-of-edge-based hierarchical architecture, and efficient data-driven techniques to manage and analyze health-related data generated by IoT-based devices and smartphones to combat COVID-19. \\ \hline  & & An innovative decentralized early warning system (EWS), Edge Computing-based Sensory Network (BCOSWE), monitors and predicts water levels using a sensor-embedded algorithm that combines the direct step method (DSM) and microgenetic algorithm (MGA) to predict the water surface profile while meeting efficiency requirements to account for sensor computation limitations. \\ \hline  & & Fog computing architecture based on one/M2M that enables nearby Fog nodes to collaborate and communicate with one another to handle operational needs and send high-resolution picture data across Fog nodes. \\ \hline  & & Context-aware fog-based IoT architecture to create a cognitive DMS that can learn from the gathered and synthesized data and act swiftly to lessen the effect of catastrophic occurrences. \\ \hline  & & Proposed a low-cost and delay-aware accident detection and response system, Emergency Response and Disaster Management System (ERDMS), that leverages smartphone sensors and fog computing to detect incidents, locate nearby hospitals using GPS, notify emergency department and family contacts of the victim, and perform required computation on nearby available fog nodes. \\ \hline  & & Proposed an e-health monitoring architecture based on sensors that relies on cloud and fog infrastructures to handle and store patient data and designer and built a prototype to execute performance experiments and identify potential bottlenecks. \\ \hline  & & \\ \end{tabular}
\end{table} 
Table 10: Summary of papers on fog and edge computing for emergency management and response. without having to invest in expensive hardware or software.