Relative traffic management scheme for robot-assisted healthcare applications

Autonomous and interactive healthcare applications exist for providing patients with customized services through user equipment (UE). For user interaction, these applications take the form of chatbots or physical robots. A user's request/query is fed as input for fetching and delivering user services. Due to the wide range of connectivity and application services, heavy data traffic can deteriorate the quality of application service. This paper proposes a relative traffic management scheme (RTMS) to address traffic issues in applications involving delicate healthcare data. The proposed scheme uses healthcare data flow and query processing to improve the responsiveness of autonomous robot-based services. Dilated traffic at any interconnecting edge is identified based on flow concentration; for this purpose, ant colony optimization (ACO) is used. The premature convergence issue in ACO is addressed by using a linear sigmoid function, which identifies the converging points based on the query processing factor to reinitiate the application service discovery and responsive deliveries. This helps to reduce both outages in healthcare service provisioning and processing time. It additionally improves response with controlled processing complexity.

Automated summary

This paper proposes a relative traffic management scheme (RTMS) to address traffic issues in healthcare applications, improving responsiveness of autonomous robot-based services using healthcare data flow and query processing. The scheme utilizes ant colony optimization and a linear sigmoid function to address premature convergence issues, reducing outages in healthcare service provisioning and processing time while enhancing response with controlled processing complexity.