Disaster management alludes to the protection of lives, property during disasters. Disaster plans are usually multi-tiered to address events such as floods, hurricanes, fires, mass utility failures, rapid disease spread and drought. To manage these sudden emergencies, there must be an accumulation of information through sharing of resources and data, making particular decisions, and carrying out widespread actions. The merging of multiple resources and pieces of data is necessary to execute complex tasks such as evacuation from a certain area or operations via actuators.
Unfortunately, the integration of platforms and infrastructures for data collection leads to inadequate management when it comes to the emergency period. This issue can be overcome through Internet of Things (IoT) and Cyber Physical System (CPS), both able to include a great number of heterogeneous end-user systems that grant access to selected data subsets for the development of digital services. With deep learning techniques being used in conjunction, forecasts could be more accurate to prevent loss of lives and damages to moveable property.
This special issue will serve as a source of foundational information on the emerging interdisciplinary domain of deep learning and IoT-CPS, to inspire research that addresses unresolved issues and challenges. Original research and review articles in this area are encouraged in the following topics, but are not limited to:
- IoT-CPS Assisted Disaster Management Framework
- Usage of Deep Learning in Disaster Management System
- Identification and recovery of victim using deep learned smart Disaster Management System
- Enhancement of IoT assisted weather forecasting accuracy
- Exploring innovative technique for damage control during disaster
- Design of smart evacuation system for based on intelligent weather forecasting framework
- Development of intelligent alarming system
- Identification of cause for the disaster
- Deep learning empowered disaster management framework for smooth governance
- Submission deadline: 30 November 2023
- Publication date: 30 March 2024