Distributed and scalable computing framework for improving request processing of wearable IoT assisted medical sensors on pervasive computing system

Abstract

Pervasive computing systems (PCS) are distributed heterogeneous network and communication technology integration for satisfying multi-level user requirements Internet of Things (IoT) assisted systems. The openness in communication, the level of management and heterogeneity support for distributed users is still a challenging demand in PCS. This manuscript introduces a novel distributed and scalable computing framework (DSCF) for improving the communication reliability of end-users on wearable IoT assisted medical sensors (WIoT-MSs). This framework uses recurrent learning for analyzing the resource allocation based on demand and sharing features. With the estimated resource requirements, PCS serve end-users with less time delay and improved communication rates of the WIoT-MSs. This framework is designed for end-user mobility management besides resource allocation and sharing on wearable technology medical sensor data transfer. The performance of the proposed framework is estimated through experimental analysis and the consistency of the framework is proved using metrics. These metrics are response time, request failure, requests handled, request backlogs, bandwidth and storage utilization. The proposed DSCF improves requests handled, bandwidth and storage utilization and minimizes request failure and backlogs with less response time.