The Internet of things (IoT) is a network of technologies that support a wide variety of healthcare workflow applications to facilitate users' obtaining real-time healthcare services. Many patients and doctors' hospitals use different healthcare services to monitor their healthcare and save their records on the servers. Healthcare sensors are widely linked to the outside world for different disease classifications and questions. These applications are extraordinarily dynamic and use mobile devices to roam several locales. However, healthcare apps confront two significant challenges: data privacy and the cost of application execution services. This work presents the mobility-aware security dynamic service composition (MSDSC) algorithmic framework for workflow healthcare based on serverless, serverless, and restricted Boltzmann machine mechanisms. The study suggests the stochastic deep neural network trains probabilistic models at each phase of the process, including service composition, task sequencing, security, and scheduling. The experimental setup and findings revealed that the developed system-based methods outperform traditional methods by 25% in terms of safety and 35% in application cost.
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