A Unified Computational Model for Assessing Security Risks in Internet of Transportation Things-Based Healthcare Applications

The rapid growth of web-based applications has attracted increasing attention from cybercriminals, particularly within the expanding field of the internet of transportation things, which has diverse applications across industries such as healthcare. As internet of transportation things technologies are adopted more widely, significant challenges emerge, particularly regarding data and service security. Hackers are specifically targeting sensitive medical data during the transportation of health emergency services, with internet of transportation things devices utilized for remote patient monitoring, medical equipment tracking, and logistics optimization. This research aims to tackle these security concerns by evaluating the risks associated with maintaining data integrity in healthcare emergency services. The research also utilizes a symmetrical fuzzy decision-making methodology, Fuzzy ANP-TOPSIS, to evaluate diverse security concerns associated with the internet of transportation things, with an emphasis on healthcare applications. The case study of seven alternatives reveals that mediXcel electronic medical records are the most viable solution, whilst the Caresoft system for hospital information is considered the least effective. The findings provide critical insights for improving the security of internet of transportation things applications and assuring their seamless integration into healthcare, especially in emergency services, hence protecting patient data and fostering user confidence.