Energy-efficient fuzzy data offloading for IoMT

Abstract

The advancement of Internet of Things based technologies in healthcare monitoring systems has evolved a new terminology, named Internet of Medical Things for medical services and devices. It is integrated with cloud-fog computing environment to facilitate load balancing solutions and enhance the quality of service using message exchange protocols. However, challenges for the quality parameters such as energy consumption, latency, resource utilization, scalability and packet loss associated with the existing architectural models are still of a great concern for researchers. This paper presents an energy efficient fuzzy data offloading scheme with a four tier cloud-fog architectural design to improve the quality parameters. The proposed Message Queuing Telemetry Transfer protocol based message exchange mechanism encapsulates the payload area of messages with client-ID and timestamp to provide authentication and ordering in packet transmission. Fuzzy logic based categorization of medical data has been used to classify it as emergency data, significant data and general data. Clustering of fog nodes has been done based on CPU speed and remaining energy. An adaptive scheduling technique has been proposed which considers the memory value for assigning the weight to different classified queues. The proposed scheme is evaluated and validated using iFogSim toolkit. The performance analysis shows maximum of 77% reduction in energy consumption, 48%, 60% and 44% reduction in end to end delay for different Quality of Services QoS 0, QoS 1, and QoS 2, respectively compared to other existing schemes.