## 3 Research Framework of Internet of Emergency Services
### Real-Time Data Collection and Analysis
#### 3.3.3 Data Visualization in Internet of Emergency Services
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Context of Use** & **Description of Use** \\ \hline [PERSON] et al. (2022)[101] & Automated accident reporting & Android smartphone-based IoT system for accident detection and classification using a multi-sensor fusion framework and machine learning classifiers \\ \hline [PERSON] and [PERSON] (2022)[102] & Mobile GIS application development & Development of mobile GIS based on free and open-source software (FOSS) framework with machine learning approaches integrated for data collection and filtering \\ \hline [PERSON] et al. (2021)[98] & Accident detection and classification & IoT system for accident detection and classification based on severity level and reporting essential information to emergency services providers using different machine learning classifiers \\ \hline [PERSON] and [PERSON] (2006) & Evacuation in indoor disaster environments & Machine-learning-based indoor augmented reality (AR) navigation and emergency evacuation system that can guide an optimal escape path for individual users. IoT-enabled ad hoc network for sensing data delivery, and hybrid reinforcement-learning-based routing algorithm for safe and reliable delivery to severe. Disaster area prediction method using elementary component gradient boosting machine models. User location estimation by deep neural network using signal strength from beacon nodes. Model-based Q-learning method for optimum evacuation path derivation, considering building structural model and disaster context information. Experimental evaluation for various disaster scenarios. \\ \hline [PERSON] et al. (2010) & Disaster emergency management under IoT environment & Framework for managing catastrophe emergencies in an IoT ecosystem. Bottom-up integration of machine learning, hybrid indexing, and queries, as well as raw data storage (RD-Sores) technologies for emergency situations. IoT hybrid index and query technique for instantaneous data retrieval from vast sensor sampling. \\ \hline [PERSON] et al. (2022)[104] & Disaster damage assessment & Integration of DL techniques into the IoTSE system for disaster damage assessment. Two scenarios (Single and Complex Event Settings) and four CNN models. Experimental validation with three possible network services. All four CNN models can learn each label during Single Event Setting, whereas with Complex Event Settings, the CNN models have learning difficulty due to closely related labels. \\ \hline [PERSON] et al. (2021)[105] & Individual monitoring in indoor disaster environments & Individual monitoring system based on edge intelligence for victim rescue and detection in indoor disaster environments. User state detection mechanism for monitoring coexisting states with a user and a smart mobile device. Fine-grained localization scheme for precise user location perception. Hierarchical edge computing resources for sensing data collection and management with encryption. Ensemble paradigm of three machine learning technologies for user behavior analysis. \\ \hline [PERSON] et al. (2016) & Disaster management & Development of a disaster preparedness and forecasting system with early warning through IoT architecture and ML algorithms. \\ \hline [PERSON] et al. (2017) & Factory safety & Development of a test-bed with sensors for proactive monitoring of human activity in a smart factory, using ML algorithms including CNN. \\ \hline [PERSON] et al. (2018) & Disaster response & APRON, an edge solution for task planning management in a network of IoT devices, using deep-learning based audio-recognition for UAV-based rescue operations. \\ \hline [PERSON] et al. (2019) & Disaster management & Post-disaster framework using IoT communication technologies and crowd sensing clustering algorithm for real-time data collection, aggregation, and monitoring dissemination. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The use of Artificial Intelligence methods for emergency response.