Sleep apnea syndrome(SAS)is a breathing disorder while a person is asleep.The traditional method for examining SAS is Polysomnography(PSG).The standard procedure of PSG requires complete overnight observation in a lab...Sleep apnea syndrome(SAS)is a breathing disorder while a person is asleep.The traditional method for examining SAS is Polysomnography(PSG).The standard procedure of PSG requires complete overnight observation in a laboratory.PSG typically provides accurate results,but it is expensive and time consuming.However,for people with Sleep apnea(SA),available beds and laboratories are limited.Resultantly,it may produce inaccurate diagnosis.Thus,this paper proposes the Internet of Medical Things(IoMT)framework with a machine learning concept of fully connected neural network(FCNN)with k-near-est neighbor(k-NN)classifier.This paper describes smart monitoring of a patient’s sleeping habit and diagnosis of SA using FCNN-KNN+average square error(ASE).For diagnosing SA,the Oxygen saturation(SpO2)sensor device is popularly used for monitoring the heart rate and blood oxygen level.This diagnosis information is securely stored in the IoMT fog computing network.Doctors can care-fully monitor the SA patient remotely on the basis of sensor values,which are efficiently stored in the fog computing network.The proposed technique takes less than 0.2 s with an accuracy of 95%,which is higher than existing models.展开更多
目前研究人员针对雾计算环境中的任务调度问题已经展开了研究并取得了一定成果,但是仍然存在不足。当前研究在任务调度中着重考虑了用户对低时延的需求,而未能针对用户的多维服务质量(Quality of Service,简称QoS)需求进行优化。事实上...目前研究人员针对雾计算环境中的任务调度问题已经展开了研究并取得了一定成果,但是仍然存在不足。当前研究在任务调度中着重考虑了用户对低时延的需求,而未能针对用户的多维服务质量(Quality of Service,简称QoS)需求进行优化。事实上用户在使用雾端资源节点处理任务请求时对时延、安全性、可靠性等方面均有需求。因此,文章将对目前研究中存在的不足展开讨论,提出基于多维QoS约束的任务调度算法,该算法考虑了用户的多维QoS需求。展开更多
基金Taif University Researchers Supporting Project Number(TURSP-2020/98),Taif University,Taif,Saudi Arabia.
文摘Sleep apnea syndrome(SAS)is a breathing disorder while a person is asleep.The traditional method for examining SAS is Polysomnography(PSG).The standard procedure of PSG requires complete overnight observation in a laboratory.PSG typically provides accurate results,but it is expensive and time consuming.However,for people with Sleep apnea(SA),available beds and laboratories are limited.Resultantly,it may produce inaccurate diagnosis.Thus,this paper proposes the Internet of Medical Things(IoMT)framework with a machine learning concept of fully connected neural network(FCNN)with k-near-est neighbor(k-NN)classifier.This paper describes smart monitoring of a patient’s sleeping habit and diagnosis of SA using FCNN-KNN+average square error(ASE).For diagnosing SA,the Oxygen saturation(SpO2)sensor device is popularly used for monitoring the heart rate and blood oxygen level.This diagnosis information is securely stored in the IoMT fog computing network.Doctors can care-fully monitor the SA patient remotely on the basis of sensor values,which are efficiently stored in the fog computing network.The proposed technique takes less than 0.2 s with an accuracy of 95%,which is higher than existing models.
文摘目前研究人员针对雾计算环境中的任务调度问题已经展开了研究并取得了一定成果,但是仍然存在不足。当前研究在任务调度中着重考虑了用户对低时延的需求,而未能针对用户的多维服务质量(Quality of Service,简称QoS)需求进行优化。事实上用户在使用雾端资源节点处理任务请求时对时延、安全性、可靠性等方面均有需求。因此,文章将对目前研究中存在的不足展开讨论,提出基于多维QoS约束的任务调度算法,该算法考虑了用户的多维QoS需求。
