Natural disasters greatly threaten our lives in addition to adversely affecting all activities. Unfortunately, most solutions currently used in flood management are suffering from many drawbacks related to latency and accuracy. Moreover, the previous solutions consider that the whole city has the same level of vulnerability to damage, while each area in the city may have different topologies and conditions. This study presents a new framework that collects data in real-time about bad weather, which may cause floods, where the framework has a proposed classification algorithm to process sensed data to determine the level of danger in each area of the city. In case of a threat, the framework will send early alerts to users and rescue teams. The framework depends on the Internet of Things (IoT) and fog computing coupled with multiple models of machine learning (Rain Forest, Decision Tree, K-Nearest Neighbor, Support Vector Machine, Logistic Regression, and Deep Learning) to enhance performance and reliability. In addition, the research suggests some assistant services. To prove the efficiency of the framework, we applied the proposed algorithm to real data for the city of Jeddah, Saudi Arabia, for the years 2009 to 2013 and for the years 2018 to 2022. Then, we depended on standard metrics (accuracy, precision, recall, F1-score, and ROC curve). The Rain Forest and Decision Tree achieved the highest accuracy, exceeding 99 percent, followed by the K-Nearest Neighbor. The framework will provide flood detection systems that can predict floods early, send a multi-level warning, and reduce financial, human, and infrastructural damage.

Privacy and security are unavoidable challenges in the future of smart health services and systems. Several approaches for preserving privacy have been provided in the Internet of Health Things (IoHT) applications. However, with the emergence of COVID-19, the healthcare centers needed to track, collect, and share more critical data such as the location of those infected and monitor social distancing. Unfortunately, the traditional privacy-preserving approaches failed to deal effectively with emergency circumstances. In the proposed research, we introduce a Tokens Shuffling Approach (TSA) to preserve collected data’s privacy, security, and reliability during the pandemic without the need to trust a third party or service providers. TSA depends on a smartphone application and the proposed protocol to collect and share data reliably and safely. TSA depends on a proposed algorithm for swapping the identities temporarily between cooperated users and then hiding the identities by employing fog nodes. The fog node manages the cooperation process between users in a specific area to improve the system’s performance. Finally, TSA uses blockchain to save data reliability, ensure data integrity, and facilitate access. The results prove that TSA performed better than traditional approaches regarding data privacy and the performance level. Further, we noticed that it adapted better during emergency circumstances. Moreover, TSA did not affect the accuracy of the collected data or its related statistics. On the contrary, TSA will not affect the quality of primary healthcare services.

Smart cities require effective disaster management (like flooding, solar storms, sandstorms, or hurricanes), as it directly impacts people’s lives. The key challenges of disaster management are timely detection and effective notification during the crisis. This research presents a smart multi-layer framework for notification classification and management before and during flooding disasters. The framework includes an early detection module as the main phase in the alerting process. This step depends on an Ensemble Learning (EL) model based on a triad of the three best selected models (Deep Learning (DL), Random Forest (RF), and K-nearest Neighbor (KNN)) to analyze data collected continuously from the Internet of Things (IoT) layer. In the boosting phase, the framework utilizes Large Language Models (LLMs) with DL to analyze social textual crowdsourcing data. The results will enable the framework to identify the most affected areas during a flood. The framework adds a fog computing layer alongside a cloud layer to enable instantaneous processing of user responses and generate specialized alerts based on contextual factors such as location, time, risk level, alert type, and user characteristics. Through testing and implementation, the proposed algorithms demonstrated an accuracy rate of over 98% in detecting threats using a dataset of real, collected weather and flooding data. Additionally, the framework proposes a centralized control panel and a design of a smartphone application that offers essential services and facilitates communication among managed civil defense teams, citizens, and volunteers.