Track Chair

• Anna Maria Vegni, University of Roma Tre, Italy
• De-Nian Yang, Institute of Information Science, Academia Sinica, Taiwan

Scope

Natural disasters like flooding and earthquakes, as well as terrorism attacks and industrial disasters, should be dealt with in a fast and effective manner. In such scenarios, first responders, news agencies and the victims are used to exploit social media as a first “communication channel” to disseminate situational information in a reliable way, reaching a huge pool of users. Similarly, crowdsourcing applications engage user communities in emergency response and disaster management for natural hazards. However, some drawbacks may occur in such social tools, thus limiting authorities and disaster management stakeholders to use social media data to take decision. As an instance, social media crowdsourcing data should be georeferenced to improve situational awareness, and the positioning error should be very low. In addition, social media data should be merged with other external data sources and authoritative data to establish geographic relationships between the disaster event and social media messages. Also, the dissemination of a message should occur between trusted and reliable nodes, in order to be sure the disseminated information is secure. As a solution, the main goal is to handle a set of learning materials such as methods, tools and guidelines on the use of social media and crowdsourcing in disasters in an effective manner, especially for what concern security and trustworthiness of data information.

So, this track wants to stimulate the scientific community to propose new technical studies that may address different topics such as:

• Secure and reliable communications in social media and crowdsourcing for disaster & crisis management
• Opportunistic data dissemination in social media and crowdsourcing tools
• Network architectures for social media and crowdsourcing
• Machine learning techniques in social media and crowdsourcing for disaster & crisis management
• Energy harvesting, storage, recycling, and wireless power transfer for social IoTs in disaster and crisis
• Fog/edge computing and social IoT convergent services, systems, infrastructure, and techniques for disaster and crisis management
• Agile, intelligent, and resilient aerial (swarm) social-inspired communications and control in disaster crisis
• Localization and positioning with social IoT in disaster/crisis areas
• Social-aware self-organizing network optimization for efficient crowdsourcing in mobile social networks
• Security, privacy, and trust in social IoT-assisted disaster/crisis management systems
• Disaster/crisis data aggregation, dissemination, collection, and mining via crowdsourcing and social media in multi-hop heterogeneous networks
• Crowdsourcing, gamification, and social media incentivization for natural hazard prevention, mitigation, and management
• Real-time query processing, data fusion, and event summarization for multi-source disaster data and social media
• Big data analysis, AI and machine/deep learning models with Age of Information (AoI) in crowdsourcing and social media analysis
• Innovative crowdsourcing applications and social network services for disaster and crisis management