Edge Technologies for Disaster Management: A Survey of Social Media and Artificial Intelligence Integration

• Published in: IEEE access Vol. 11; p. 1
• Main Authors: Aboualola, Mohamed, Abualsaud, Khalid, Khattab, Tamer, Zorba, Nizar, Hassanein, Hossam S.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-01-2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial intelligence; Big Data; Crowd management; Deep Learning; Digital media; Disaster management; Disasters; Edge Computing; Electronic devices; Emergency Detection; Emergency management; Emergency response; Emergency services; Event Prediction; Floods; Information management; Internet of Things; Literature reviews; Machine Learning; Management systems; Performance prediction; Real-time systems; Response to Emergency; Social Media; Social networking (online); Social networks; Surveys
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3293035

Within the paradigm of smart cities, smart devices can be considered as a tool to enhance safety. Edge sensing, Internet of Things (IoT), big data, social media analytics, edge computing, and artificial intelligence are key technologies that can be applied through smart devices to create emergency-aware systems. The use of these technologies could make emergency management tasks such as visualizing, analyzing, and predicting disasters easier to perform. The aim of this paper is to conduct a review of recent activities in literature about disaster and emergency management, and showing the role of different edge technologies used in this regard, and through the different stages of dealing with a disaster situation. Special importance is given to two main technologies: Social media analytics and artificial intelligence, due to their exceptional impact on emergency situations. Social media represents a rich source of data while artificial intelligence stands out as the mechanism to deal with the huge amount of data generated by smart devices, and thus needed to tackle all sources of data, in order to predict, detect, manage information, and for authorities to respond to emergency situations. This survey is a comprehensive review for the recent literature on the related topics, providing the reader with a clear overview of the current status and classifying the papers into groups with relations among them. The structuring of the recent literature into four phases makes it easier for the reader to realize the current state of the art. For completeness, this survey ends with a section on open issues and research trends in disaster and emergency management systems.