### Literature Review on Available Solutions
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Main Characteristics** & **Limitations** \\ \hline \multirow{4}{*}{[17]} & WSN paradigm for real-time applications in smart cities, & \multirow{4}{*}{Paradigm; Does not consider the overcrowding.} \\  & which aims at balancing performance and energy & \\  & consumption, and uses the Technique for Order of Preference & \\  & by Similarity to Ideal Solution (TOPSIS) optimization & \\  & technique to find the shortest data transmission path. & \\ \hline \multirow{4}{*}{[18]} & WSN based on a method inspired by biological intracellular signaling, which was designed to perform smog pollution & \multirow{4}{*}{Air pollution only; Does not consider the overcrowding.} \\  & sensing, and taking into account the ad hoc demand routing protocol (AODV) and bellman-ford and interzone routing & \\  & protocol (EERP) for data transmission. & \\ \hline \multirow{4}{*}{[19]} & IoT-WSN based on an evidence-based interactive trust management system for disaster management. & \multirow{4}{*}{Medical emergency only; Communications between autonomous and adaptive nodes.} \\ \hline \multirow{4}{*}{[20]} & WSN that aims at detecting, in a disaster scenario, moving people without ‘tracking devices’ (i.e., carrying out the so-called Device free Passive Localization, DfPL). & Does not consider the emergency detection. \\ \hline \multirow{4}{*}{[21]} & IoT-WSN based on smart fire sensors, cameras, and a Convolutional Neural Network (CNN), acting as a surveillance monitoring system for detecting disasters that occur in a remote area (e.g., a forest). & Remote area only; Does not use sensors for structural monitoring. \\ \hline \multirow{4}{*}{[22]} & IoT-WSN based on machine learning algorithms that run in a cloud server and includes a modular redundancy fault tolerant scheme to obtain an accurate prediction from sensor data (gas and force sensors) managed by the ultra-low power MSP30 board and a Raspberry Pi, which was designed for early warning in an industry environment. \\ \hline \multirow{4}{*}{[23]} & IoT-WSN that uses the Advanced Adaptive Wavelet Sampling Algorithm (AAWSA) for prolonging the lifetime and power consumption of sensor nodes that include several sensors (i.e., moisture sensors, pressure sensor, rain gauge, tilt meters, and strain gauge), which was developed for disaster prediction in an urban region. \\ \hline \multirow{4}{*}{[24]} & IoT-based architecture that collects real-time data from the city (from existing sensors at home, parking, vehicular networking, surveillance, weather and water monitoring system, etc.), implemented in the Hadoop ecosystem that allows the processing of Big Data, to obtain a “Smart Digital City”. \\ \cline{1-1}  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont_. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Main Characteristics** & **Limitations** \\ \hline \multirow{4}{*}{[25]} & 2D and 3D WebGIS-based platform that has a scalable network architecture and uses a three-tier software architecture, which was designed for effective landslide multilevel management, and an emergency response. & Landslide emergency only; Does not consider overcrowding. \\ \hline \multirow{4}{*}{[26]} & SENS-ME platform that aims at exploiting the functionalities of Commercial Off-The-Shelf (COTS) smartphones to carry out opportunistic networking, mobile sensing, and distributed information processing. & Does not consider emergency detection. \\ \hline \multirow{4}{*}{[27]} & Flood disaster management system (FDMS) that carries out environmental model selection and disaster-related data binding. & Flood disaster only. \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \end{tabular}
\end{table} 
Table 2: Examples of platforms for emergency and disaster management.