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Energy efficient IoT-Fog based architectural paradigm for prevention of Dengue fever infection

机译:基于节能的IOT-FOG基于IOT-FOG预防登革热感染的架构范式

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Dengue is one of the most common and widespread infectious illnesses in humans transmitted by female Aedes albopictis. The prevalence of Dengue cases has increased substantially leading to human morbidity. Inadequate availability of healthcare professionals and inaccessibility to healthcare institutions have aggravated the problem. The traditional medical technologies are too antiquated to serve the purpose. The innovative latest technologies like Internet of Things (IoT), Cloud Computing, Fog Computing have made real-time and remote healthcare possible with huge success. In this paper, an IoT based Fog-Cloud enabled system for monitoring, assessment and control of Dengue Fever has been proposed. IoT sensors acquire data about a large spectrum of health as well as environmental factors that contribute to infection. The battery constrained sensors set their sampling rate according to the degree of cruciality that saves power to make battery long lasting. The Fog layer employs Support Vector Machine (SVM) for Dengue infection evaluation with least latency and sends alerts including precautionary measures to the users, hospital officials and government agencies. Moreover, the proposed system utilizes Temporal Network Analysis (TNA) and Google map service to categorize areas as infected, uninfected or risk prone. The experimental results are evaluated by a number of analytical parameters to investigate the effect of proposed system. SVM performs the best in terms of accuracy, recall, specificity, precision and f-measure with values 93%, 95%, 89%, 94% and 95% respectively. Furthermore, TNA based outbreak assessment gives valuable inputs for the government institutions to control the outbreak.
机译:登革热是女性AEDES Albopictis传播的人类中最常见和普遍的传染病之一。登革热病例的患病率大幅增加了人类发病率。医疗保健专业人员的可用性不足以及医疗保健机构无法访问的问题加剧了问题。传统的医疗技术过于沉重,以满足目的。创新的最新技术,如东西互联网(物联网),云计算,雾计算使得具有巨大成功的实时和远程医疗保健。在本文中,提出了一种基于IOT的雾云,用于监测,评估和控制登革热的控制。物联网传感器获取有关大量健康以及有助于感染的环境因素的数据。电池受限的传感器根据需要的关键程度设置它们的采样率,以节省电力以使电池长持久。雾层采用支持向量机(SVM),用于登革热感染评估,最小延迟,并发送警报,包括对用户,医院官员和政府机构的预防措施。此外,所提出的系统利用时间网络分析(TNA)和谷歌地图服务,将区域分类为受感染,未感染或风险的俯卧。通过许多分析参数评估实验结果,以研究所提出的系统的效果。 SVM在准确性,召回,特异性,精度和F测量方面表现了最佳,分别具有93%,95%,89%,94%和95%的值。此外,TNA基于TNA的爆发评估为政府机构提供了有价值的投入,以控制爆发。

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