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Corona Virus Disease 2019 (COVID-19) is a continuing extensive incident globally affecting several million people's health and sometimes leading to death. The outbreak prediction and making cautious steps is the only way to prevent the spread of COVID-19. This paper presents an Adaptive Neuro-fuzzy Inference System (ANFIS)-based machine learning technique to predict the possible outbreak in India. The proposed ANFIS-based prediction system tracks the growth of epidemic based on the previous data sets fetched from cloud computing. The proposed ANFIS technique predicts the epidemic peak and COVID-19 infected cases through the cloud data sets. The ANFIS is chosen for this study as it has both numerical and linguistic knowledge, and also has ability to classify data and identify patterns. The proposed technique not only predicts the outbreak but also tracks the disease and suggests a measurable policy to manage the COVID-19 epidemic. The obtained prediction shows that the proposed technique very effectively tracks the growth of the COVID-19 epidemic. The result shows the growth of infection rate decreases at end of 2020 and also has delay epidemic peak by 40–60 days. The prediction result using the proposed ANFIS technique shows a low Mean Square Error (MSE) of 1.184 × 10 –3 with an accuracy of 86%. The study provides important information for public health providers and the government to control the COVID-19 epidemic.

The exponential growth of the number of devices that are connected to the internet is increasing. At present, there are more than 13 billion devices are connected and it was predicted that this number reaches up to 25 billion by 2020. In an Internet of Things ecosystem, these devices are embedded to perform specific tasks in which they cannot take decisions. When there is an emergency state in any one of the nodes, the other nodes also produce the traffic simultaneously which makes the border router busy and creates a delay in message propagation and causes loss to the productivity. In the proposed architecture, Software Defined Networking offers a solution to this problem by making wireless sensor network more intelligent and also makes network capable of making self-decision during emergency state. In the proposed architecture an Open vSwitch installed on a Raspberry Pi as a Data Plane and Open Network Operating system, Mininet can be used as a Control Plane. The NodeMCU-ESP8266 wireless devices act as end nodes. A routing path with minimum latency during critical traffic can be implemented using Software Defined Networking.

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