The main aim of Internet of Things (IoT) is to get every “thing” (sensors, smart cameras, wearable devices, and smart home appliances) to connect to the internet. Henceforth to produce the high volume of data required for data processing between IoT devices, large storage and the huge number of applications to offer cloud computing as a service. The purpose of IoT-based-cloud is to manage the resources, and effective utilization of tasks in cloud. The end user applications are essential to enhance the QoS parameters. As per the QoS parameters, the service provider makes the speed up of tasks. There is a requirement for assigning responsibilities based on priority. The cloud services are increased to the network edge, and the planned model is under the Fog computing paradigm to reduce the makespan of time. The priority based fuzzy scheduling approach is brought by the dynamic feedback-based mechanism. The planned mechanism is verified with the diverse prevailing algorithms and evidenced that planned methodology is supported by effective results.

The healthcare technologies in COVID‐19 pandemic had grown immensely in various domains. Blockchain technology is one such turnkey technology, which is transforming the data securely; to store electronic health records (EHRs), develop deep learning algorithms, access the data, process the data between physicians and patients to access the EHRs in the form of distributed ledgers. Blockchain technology is also made to supply the data in the cloud and contact the huge amount of healthcare data, which is difficult and complex to process. As the complexity in the analysis of data is increasing day by day, it has become essential to minimize the risk of data complexity. This paper supports deep neural network (DNN) analysis in healthcare and COVID‐19 pandemic and gives the smart contract procedure, to identify the feature extracted data (FED) from the existing data. At the same time, the innovation will be useful to analyse future diseases. The proposed method also analyze the existing diseases which had been reported and it is extremely useful to guide physicians in providing appropriate treatment and save lives. To achieve this, the massive data is integrated using Python scripting language under various libraries to perform a wide range of medical and healthcare functions to infer knowledge that assists in the diagnosis of major diseases such as heart disease, blood cancer, gastric and COVID‐19.

In this article, the proposed feedback-based resource management approach provides data processing, huge computation, large storage, and networking services between Internet of Things (IoT)-based Cloud data centers and the end-users. The real-time applications of IoT, such as smart city, smart home, health care management systems, traffic management systems, and transportation management systems, require less response time and latency to process the huge amount of data. The proposed feedback-based resource management plan provides a novel resource management technique, consisting of an integrated architecture and maintains the service-level agreement (SLA). It can optimize energy consumption, response time, network bandwidth, security, and reduce latency. The experimental results are tested with the IFogSim tool kit and have proved that the proposed approach is effective and suitable for smart communication in IoT-based cloud.

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