期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2019
卷号：15
期号：5
页码：1
DOI：10.1177/1550147719851977
出版社：Hindawi Publishing Corporation
摘要：In order to increase the reliability, accuracy, and efficiency in the eHealth, Internet of Medical Things is playing a vital role. Current development in telemedicine and the Internet of Things have delivered efficient and low-cost medical devices. The Internet of Medical Things architectures being developed do not completely recognize the potential of Internet of Things. The Internet of Medical Things sensor devices have limited computation power; in case if a patient is using implanted medical devices, it is not easy to recharge or replace the devices immediately. Biosensors are small devices with limited energy if these devices do not wisely utilize the energy may drain sharply and devices become inactive. The current medical solutions place the bulk of data on cloud-based systems that ultimately creates a bottleneck. In this article, an energy-efficient fog-to-cloud Internet of Medical Things architecture is proposed to optimize energy consumption. In the proposed architecture, Bluetooth enabled biosensors are used, because Bluetooth technology is an energy efficient and also helps to enable the sleep and awake modes. The proposed fog-to-cloud Internet of Medical Things works in three different modes periodic, sleep–awake, and continue to optimize the energy consumption. The proposed technique enabled the sensing modes that gathers the patients’ data efficiently based on their health conditions. The sensed data are transmitted to the relevant fog and cloud devices for further processing. The performance of fog-to-cloud Internet of Medical Things is evaluated through simulation; the results are compared with the results of existing techniques in terms of an end-to-end delay, throughput, and energy consumption. It is analyzed that the proposed technique reduces the energy consumption between 30% and 40%.
关键词：Bio-gateway; tele-medicine; energy optimization; eHealth
