Fog based Healthcare Monitoring System in SDN-IoT Networks

Kumari, Neeraj; Jain, Vivek Kumar

doi:10.1109/icatiece56365.2022.10047334

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…Nesses casos, é importante avaliar o tradeoff entre os recursos computacionais necessários para analisar os dados da aplicação e os seus requisitos de segurança [Misra et al 2020]. Neste sentido, algumas propostas na literatura sugerem o gerenciamento de QoS de dados de saúde por meio do uso de SDN e técnicas de aprendizado de máquina [Kumari andJain 2022, Misra et al 2020]. Em tais propostas, as camadas de névoa e nuvens são adotadas para apoiar o armazenamento e processamento dos dados de saúde coletados por sensores na borda da rede.…”

Section: Redes Definidas Por Softwareunclassified

Um Panorama dos Serviços de Saúde Avançados: Conectividade e Segurança em Sistemas de Vida Assistida

Ribeiro¹,

Nakayama²,

Nogueira³

et al. 2023

Minicursos Do XLI Simpósio Brasileiro De Redes De Computadores E Sistemas Distribuídos

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An ambient assisted living is an advanced health service that includes smart space applications and location-independent individual monitoring. The development of assisted living applications relies on several technologies, such as the Internet of Things, short and long-range communication protocols, middlewares, cloud computing, and artificial intelligence. The heterogeneity of the architectural components and communication protocols arises challenges in providing network and security requirements. This chapter overviewes this service and identifies the main communication and security requirements and the challenges to address them. ResumoO ambiente de vida assistida é um serviço avançado de saúde que inclui aplicações em espaços inteligentes e o monitoramento da saúde de indivíduos, independente de sua localização. O desenvolvimento dessas aplicações segue diferentes tecnologias, como a Internet das Coisas, protocolos de comunicação de curto e longo alcance, middlewares, computação em nuvem e inteligência artificial. A heterogeneidade dos componentes que fazem parte dessas arquiteturas e dos próprios protocolos de comunicação utilizados geram desafios para alcançar os requisitos de rede e segurança das aplicações. Este capítulo apresenta uma visão geral desse serviço, identifica os principais requisitos de comunicação e segurança e os principais desafios para endereçá-los. IntroduçãoEm 2017, o Ministério da Saúde do Brasil informou que as hospitalizações de pessoas idosas no Sistema Único de Saúde (SUS) custam aproximadamente 30% a mais

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Section: Redes Definidas Por Softwareunclassified

Um Panorama dos Serviços de Saúde Avançados: Conectividade e Segurança em Sistemas de Vida Assistida

Ribeiro¹,

Nakayama²,

Nogueira³

et al. 2023

Minicursos Do XLI Simpósio Brasileiro De Redes De Computadores E Sistemas Distribuídos

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“…It aims to promote as well as automate network infrastructure as well as setup. It is true that creating infrastructures that enable a more complicated method by placing reliability as well as security at forefront of process is a relatively new platform for preventing, tracking, monitoring, segregating, reducing, and probably reducing the negative consequences of the majority of network intrusions [4]. The fundamental element of the SDN architecture that has improved technical innovation is centralization of control plane, which is now scattered among routers as well as switches inside conventional networks with forwarding device capabilities.…”

Section: Introductionmentioning

confidence: 99%

Revolutionizing Neurostimulator Care: Enhancing Remote Health Monitoring through SDN-Cloud Networks

Antony,

Elangovan

2024

Preprint

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The Internet of Things (IoT) and artificial intelligence (AI) are the fastest-growing technological approaches globally. With the rising urban population, the concept of a smart city isn't new. To effectively integrate IoT and AI into remote healthcare monitoring (RHM) systems within smart cities, we must have a comprehensive grasp of smart city frameworks. Our proposed model focuses on gathering data from an auricular therapy device, a neurostimulator that provides non-invasive stimulation to the outer ear. This device communicates via Bluetooth, allowing data exchange between the patient's and doctor's phones. After collecting the brain signal data, it's processed to eliminate noise and is normalized. This data is then classified using the adaptive fuzzy based Bayesian metasalp neural network (AFBBMNN) combined with levy flight secure offloading analysis in Software Defined Networking (SDN). The results prominently emphasize the need for enhanced healthcare provision. This information is then relayed to doctors via a cloud-SDN module that comprises a communication phase, cloud server, and cloud database where the signals are stored. The proposed method offers promising outcomes, emphasizing its viability as an efficient tool for early neurological disease detection and treatment within a smart city healthcare framework.

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