Abstract
Flooding is described as an excess of water or sludge over parched soil, and it is caused by water runoff inside water routes from various water sources, such as canals. Urban flooding has been caused by heavy rainfall, deforestation, urbanization, a lack of water and sewerage administration, and a lack of focus on the environment of the hydrological scheme. Furthermore, due to the difficulty in transferring flood data from flood-affected areas to the control center, there is a weakness in flood assessment. To reduce the loss of property due to floods, acquired data from the busy area should be moved immediately to the observation room, without further delay, into a fully fledged technique in the wireless settings of the Internet of Things (IoT). Because wireless nodes are always changing in their environment, this results in unpredictability and uncertainty in information distribution. As a result, there is a necessity for flood-predictable region data between the source and the control room, which may be inflated. There were several ways set up and put into effect in the past with the goal of keeping an eye on the flood regions. However, one of the most difficult issues is sharing data between source and destination nodes without causing delays or data loss. Furthermore, the video quality must be considered at the time of receipt as it is difficult to separate flood events from regular disasters, making scientific complexity greater than the information received in a wireless ad-hoc environment utilizing IoT-based sensors. In light of the foregoing, the proposed study consists of three goals: design of a mobile ad-hoc flooding environment, construction of an urban flood high-definition video surveillance system employing IoT-based sensors, and simulation experiments. The performance analysis of the method is analyzed by various parameters. The path failure of the proposed method (PP1) is 14% higher than that of the secondary path, and packet loss is 7% higher than that of the secondary path. The transmission time is 9% less than that of the secondary path, and the packet loss rate and end-to-end delay are 5% less than that of the secondary path.