Healthcare Data Analysis and Secure Storage in Edge Cloud Module with Blockchain Federated Sparse Convolutional Network++

In current scenario, most difficult requirements are managing massive amount of multimedia data generated by Internet of Things (IoT) devices, which can only be managed with the cloud. The intelligent Edge Cloud computing technology operates in a distributed environment and emerges as a solution. This research aims to use Edge Cloud computing to reduce latency in e-healthcare. This study proposes a novel method for using machine learning to analyze healthcare data and storing it in an edge cloud module. The input is monitored healthcare data that is collected, processed, and analyzed with the help of a blockchain-federated sparse convolutional network++, which also improves network security. The malicious attacks are then identified and malicious data is stored using a centralized edge cloud computing module and authentication process. The experimental analysis is conducted on a variety of healthcare datasets and is based on a security analysis of the data in terms of data transmission rate, computation cost, communication overhead, random accuracy, mean average precision (map), and specificity. The proposed method achieved a data transmission rate of 65%, a computation cost of 51%, a communication overhead of 75%, random accuracy of 85%, mean average precision (map) of 63%, and specificity of 79%.