# A Cloud Based Disaster Management System
## 3 Proposed System Overview  
Our research aimed to design and implement a complete system to help rescue teams and first responders to visualize, monitor, and plan interventions in a 3D environment. The proposed system combines various technologies, including WSN, routing protocols, cloud computing, 3D rendering approaches, and multi-objective optimization (Figure 1). The proposed disaster management system work as follows. First, a set of wireless sensors are deployed in the region under control. Upon detecting an incident such as a fire disaster or a crowded zone, a protocol for gathering data, such as location and severity, is set. The data are forwarded to the central station thanks to a routing protocol. Once data are received on the cloud side, a near real-time rendering approach starts in the cloud to construct a 3D environment closely reflecting reality and is then transferred to a user's machine (firefighter, manager, etc.). After that, a rescue plan is created using the AHP-MTSP solution to determine the optimized plan, including robots, firefighters, police, etc., for the different objectives. A 3D representation of the current situation is constructive for police, military, and medical personnel, allowing them to react with suitable management effort when they reach the incident site. To construct an efficient and accurate plan, it is essential to have a real-time realistic 3D virtual environment that precisely reflects the physical environment. Any disaster events must be communicated and displayed after rendering using the pre-built geometry approach (GRB) [42]. In GRB, the scene contains many geometrically-defined surfaces with different properties, such as color, transparency, and lighting conditions. In contrast, image-based rendering (IRB) [43; 44] depends on a scene description for the plenoptic function [45]. Scene photographs are needed from different viewpoints to create the 3D environment. We favor GRB in our solution because IRB requires a high-quality photo from the source, which may be unclear/blurry due to the presence of smoke, water leakage, etc., in the original environment. Additionally, the use of the plenoptic function requires too much data to handle. Generally, the rescue implementation depends on several parameters such as the environment condition, the equipment capacity, and the victim's situation, etc. In this work, we use the facial behavior of avatars to imitate the real situation described by the sensor data during the runtime. Figure 2 describes the proposed system architecture. Three entities are identified: (1) real environment; (2) the corresponding 3D virtual environment-user side; and (3) the cloud computing. To guarantee quick access and modeling, we proposed to preload the initial 3D representation of the VE into the cloud. Moreover, a modular game engine is implemented in the cloud to manage the VE modifications and extensions, including the avat  
Figure 2: The overall system architecture. The implementation of the main game engine components in the cloud offers the user the freedom to use different kinds of devices (desktop, mobile device, etc.) when running the system. Additionally, users are allowed to interact with applications without installing or configuring a 3D development environment, regardless of the amount of storage and memory required. The detailed cloud architecture used for our system is shown in Figure 3.  
When sensors identify a fire within the actual environment, the system collects numerous events from the real surroundings and sends them back to a central database. RPL [46; 47] is the typical de-facto standard protocol currently used for routing data from sensors to an Internet gateway. Security protocols, such as [48; 49], are applied at the sensor layer to protect integrity and authentication of messages. A rendering protocol within the cloud infrastructure is utilized to image a comprehensive live depiction of the accident site in a 3D fashion.