Wireless Body Sensor Network (BSNs) are devices that can be worn by human beings. They have sensors with transmission, computation, storage and varying sensing qualities. When there are multiple devices to obtain data from, it is necessary to merge these data to avoid errors from being transmitted, resulting in a high quality fused data. In this proposed work, we have designed a data fusion approach with the help of data obtained from the BSNs, using Fog computing. Everyday activities are gathered in the form of data using an array of sensors which are then merged together to form high quality data. The data so obtained is then given as the input to ensemble classifier to predict heart-related diseases at an early stage. Using a fog computing environment, the data collector is established and the computation process is done with a decentralised system. A final output is produced on combining the result of the nodes using the fog computing database. A novel kernel random data collector is used for classification purpose to result in an improved quality. Experimental analysis indicates an accuracy of 96% where the depth is about 10 with an estimator count of 45 along with 7 features parameters considered.

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At present, the traditional healthcare system is completely replaced by the revolutionary technique, the Internet of Medical Things (IoMT). Internet of Medical Things is the IoT hub that comprises of medical devices and applications which are interconnected through online computer networks. The basic principle of IoMT is machine-to-machine communication that takes place online. The major goal of IoMT is to reduce frequent or unwanted visits to the hospitals which makes it comfortable and is also highly preferred by the older people. Another advantage of this methodology is that the interpreted or collected data is stored in cloud modules unlike amazon and Mhealth, making it accessible remotely. Although there are countless advantages in IoMT, the critical factor lies in data security or encryption. A surplus number of threat related to devices, connectivity, and cloud might occur under unforeseen or threatening circumstances which makes the person in the situation helpless. Yet, with the help of data security techniques designed especially for Internet of Medical Things, it is possible to address these challenges. In this paper, a review on data securing techniques for the internet of medical things is made along with a discussion on related concepts.

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