4.8 Article

Cyber-Physical-Social Model of Community Resilience by Considering Critical Infrastructure Interdependencies

Journal

IEEE INTERNET OF THINGS JOURNAL
Volume 10, Issue 19, Pages 17530-17543

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2023.3277450

Keywords

Community resilience; critical infrastructures; cyber-physical-social system; fake news; natural language processing; power systems; social media; urban computing

Ask authors/readers for more resources

Translation: Study on modeling community resilience and analyzing the interrelationships among social, physical, and cyber aspects. This article presents a multiagent cyber-physical-social model to assess community resilience, considering the interconnection of power systems, emergency services, social communities, and cyberspace. Through quantifying social metrics and conducting three different case studies, the study findings suggest that cooperation can positively influence individual behavior, and smaller populations with greater empathy may be more resilient.
Each year, several disasters occur, resulting in enormous human, infrastructural, and economic losses. To minimize losses and ensure an adequate emergency response, it is vital to prepare the community for greater shock absorption and recovery after an occurrence. This raises the concept of community resilience and also demands appropriate metrics and prediction models for improved preparedness and adaptability. While a community is impacted in three main ways during a disaster, namely social, physical, and cyber there are currently no tools to model their interrelationship. Thus, this article presents a multiagent cyber-physical-social model of community resilience, taking into account the interconnection of power systems, emergency services, social communities, and cyberspace. To validate the model, we used data on two hurricanes (Irma and Harvey) collected from Twitter, GoogleTrends, FEMA, power utilities, CNN, and Snopes (a fact-checking organization). We also describe methods for quantifying social metrics, such as the level of anxiety, risk perception, and cooperation using social sensing, natural language processing, and text mining tools. We examine the suggested paradigm through three different case studies: 1) hurricanes Irma and Harvey; 2) a group of nine agents; and 3) a society comprised of six distinct communities. According to the results, cooperation can positively change individual behavior. Relationships within a community are so crucial that a smaller population with greater empathy may be more resilient. Similar dynamic changes in social characteristics occur when two empathetic communities share resources after a disaster.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.8
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available