IoT cloud-based services in network management solutions

Jukić, Oliver; Heđi, I.; Cirikovic, E.

doi:10.23919/mipro48935.2020.9245117

Cited by 4 publications

(4 citation statements)

References 17 publications

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“…Many medical service offerings can be improved via integration of cloud of IoT.For example series of affected person data through sensor moving sensed data to cloud for processing and storing. By analyzing the stored data at cloud and we can take appropriate action [2]. available in many homes, the data produced by these devices is huge in volume and random in nature, necessitating analysis with a big data analytics system in order to comprehend user behavioral patterns or extract the necessary information.…”

Section: Healthcare Monitoring Devicesmentioning

confidence: 99%

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Application of IoT Framework for Prediction of Heart Disease using Machine Learning

Swain,

Behera,

Swain

et al. 2023

IJRITCC

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Prognosis of illnesses is a difficult problem these days throughout the globe. Elder people of twenty years and over are taken into consideration to be laid low with this sickness now a days. For example, human beings having HbA1c level more than 6.5% are diagnosed as infected with diabetic diseases. This paper uses IoT to evaluate threat factors which have been similar to heart diseases which are not treated properly. Diagnosis, prevention of heart disease may be done by use of machine learning (ML). There has been an extensive disconnect among Machine Learning architects, health care researchers, patients and physicians in their technology. This paper intends to perform an in-intensity evaluation on Machine Learning to make us of new advance technologies. Latest advances within the development of IoT implanted devices and other medicine delivery gadgets, disease diagnostic methods and other medical research have considerably helped human beings diagnosed heart diseases. New soft computing models can be helpful for remedy of various heart diseases. The Food and Drug Administration (FDA) employs several particularly creative thoughts to get their capsules to the client. Artificial Neural Community offers a first-rate chance to deal with heart diseases with advance IoT and cloud applications.

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Section: Healthcare Monitoring Devicesmentioning

confidence: 99%

“…IoT collects data using gateway or other edge devices. IoT connected devices communicate via network on cloud-based platforms connected to the Internet of Things [2]. In a forecast nearly 10 billion IoT devices are used till 2020 that increases up to 30 billion by 2030.…”

Section: Introductionmentioning

confidence: 99%

Application of IoT Framework for Prediction of Heart Disease using Machine Learning

Swain,

Behera,

Swain

et al. 2023

IJRITCC

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“…Amazon Web Services (AWS) IoT [ 37 , 38 ] are a set of cloud services available through AWS that connect IoT devices to other devices and/or AWS cloud services. AWS IoT provides device software that can help customers integrate IoT devices into AWS IoT-based solutions.…”

Section: State Of the Artmentioning

confidence: 99%

Building an IoT Platform Based on Service Containerisation

Antunes

Santiago

Manso

et al. 2021

Sensors

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IoT platforms have become quite complex from a technical viewpoint, becoming the cornerstone for information sharing, storing, and indexing given the unprecedented scale of smart services being available by massive deployments of a large set of data-enabled devices. These platforms rely on structured formats that exploit standard technologies to deal with the gathered data, thus creating the need for carefully designed customised systems that can handle thousands of heterogeneous data sensors/actuators, multiple processing frameworks, and storage solutions. We present the SCoT2.0 platform, a generic-purpose IoT Platform that can acquire, process, and visualise data using methods adequate for both real-time processing and long-term Machine Learning (ML)-based analysis. Our goal is to develop a large-scale system that can be applied to multiple real-world scenarios and is potentially deployable on private clouds for multiple verticals. Our approach relies on extensive service containerisation, and we present the different design choices, technical challenges, and solutions found while building our own IoT platform. We validate this platform supporting two very distinct IoT projects (750 physical devices), and we analyse scaling issues within the platform components.

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“…There are several robust commercial platforms, such as ThingSpeak [18], Amazon Web Services (AWS) IoT Core [19], Azure IoT Hub [20], Arduino Cloud [21] each other, that significantly reduce the implementation and management costs of IoT systems. These platforms offer a range of services including communication services, data storage from sensors, and data analysis capabilities.…”

Section: Introductionmentioning

confidence: 99%

Approach to Simplify the Development of IoT Systems that Interconnect Embedded Devices Using a Single Program

Blanco,

Espada,

Crespo

2023

CMC

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Many Internet of Things (IoT) systems are based on the intercommunication among different devices and centralized systems. Nowadays, there are several commercial and research platforms available to simplify the creation of such IoT systems. However, developing these systems can often be a tedious task. To address this challenge, a proposed solution involves the implementation of a unified program or script that encompasses the entire system, including IoT devices functionality. This approach is based on an abstraction, integrating the control of the devices in a single program through a programmable object. Subsequently, the proposal processes the unified script to generate the centralized system code and a controller for each device. By adopting this approach, developers will be able to create IoT systems with significantly reduced implementation costs, surpassing current platforms by more than 10%. The results demonstrate that the single program approach can significantly accelerate the development of IoT systems relying on device communication.

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IoT cloud-based services in network management solutions

Cited by 4 publications

References 17 publications

Application of IoT Framework for Prediction of Heart Disease using Machine Learning

Application of IoT Framework for Prediction of Heart Disease using Machine Learning

Building an IoT Platform Based on Service Containerisation

Approach to Simplify the Development of IoT Systems that Interconnect Embedded Devices Using a Single Program

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