Computational Enhancements of Wearable Healthcare Devices on Pervasive Computing System

The diverse user demands in the system supported with the internet of things are often managed efficiently, using the computing system that is pervasive. Pervasive computing system in an integration of heterogeneous distributed network and communication technologies and other referred as the ubiquitous computing. All though it handles the user requirement properly. The ingenuousness in the conveyance of the information, in the standard of handling and extending the heterogeneity assistance for the dispersed clients are still under construction in the as it is very challenging in the pervasive computing system. In order to provide proper and a steadfast communication for the users using an IOT based wearable health care device the paper introduces the new dispersed and elastic computing model (DECM). The developed system utilizes the recurrent-learning for the examining the allocation of resources according to the requirements as well as the allotment aspects. Based on the determined requirements of the resources, the pervasive computing system provide services to the user in the end with minimized delay and enhanced rate of communication for the health care wearable devices. The developed system emphasis also on managing the mobility, apart from allocation of resources and distribution for proper data conveyance over the wearable health care device. The working of the laid out system is determined by the experimental analysis. The constancy of the model developed is demonstrated utilizing the metrics such as the failure of request, time of response, managed and backlogged requests, bandwidth as well as storage used. The developed model heightens the number of request managed properly (handled) along with the bandwidth and storage and minimizes the failure in requests, backlogs and the time taken for response.