## 3 Research Framework of Internet of Emergency Services
### Computation Paradigms  
As the volume and complexity of data generated by the Internet of Emergency Services (IoES) continue to grow, there is a need for advanced computation paradigms that can handle the data in real-time and ensure a timely and efficient emergency response. Two such technologies are edge computing and fog computing, which enable data processing and analysis at the edge of the network, closer to the devices and sensors that generate the data. In this way, they can reduce latency, improve response times, and reduce the amount of data that needs to be transmitted to the cloud or data center. Edge computing is a distributed computing paradigm that brings computation and data storage closer to the sources of data, such as IoT devices and sensors [123,124]. This allows data processing and analysis to be conducted in real-time, without the need for transmitting the data to a remote cloud or data center. This approach can help reduce network latency, improve response times, and reduce bandwidth requirements. In the context of emergency response, edge computing can be used to process and analyze data from sensors and other devices in real-time, enabling faster decision making and a more efficient emergency response (see Table 10). For example, during a natural disaster, edge computing can be used to process and analyze data from sensors deployed in the affected area to provide real-time updates on the situation. Fog computing is a variation of edge computing that is designed to support the processing and analysis of data from multiple edge devices [125,126]. Fog computing involves deploying a layer of computing resources, such as servers and storage devices, closer to the edge devices, forming a distributed computing network. This enables data processing and analysis to be conducted at multiple levels, from the edge devices to the fog layer and then to the cloud or data center. In the context of emergency response, fog computing can be used to process and analyze data from multiple sensors and devices in real-time, enabling a more comprehensive and accurate understanding of the situation. One example of the use of edge computing in an emergency response is the Smart Emergency Response System (SERS) developed by IBM. SERS is designed to provide real-time situational awareness and response capabilities during emergencies such as natural disasters or terrorist attacks. The system integrates multiple data sources, including social media feeds, news reports, weather data, and data from IoT sensors and devices, to provide a comprehensive view of the situation. Edge computing is used to process and analyze data from IoT sensors and devices in real-time, enabling faster decision making and response. Another example of the use of fog computing in an emergency response is the Smart Ambulance project developed by Cisco. The project involves equipping ambulances with IoT sensors and devices that can transmit patient data in real-time to a fog layer deployed in the ambulance. The fog layer can process and analyze the data, providing real-time feedback to medical staff and enabling faster and more effective treatment of patients. The fog layer can also transmit data to a cloud or data center for further analysis and storage. While edge computing and fog computing have many potential benefits for emergency response, there are also several challenges and limitations to consider. One challenge is the need for robust and reliable network connectivity, particularly in remote or disaster-affected areas. Another challenge is the need for effective data management and security, particularly when dealing with sensitive or confidential data. Finally, there is a need for effective coordination and collaboration between different stakeholders, including