Abstract
Elderly population in the Asian countries is increasing at a very fast rate. Lack of healthcare resources and infrastructure in many countries makes the task of provding proper healthcare difficult. Internet of things (IoT) in healthcare can address the problem effectively. Patient care is possible at home using IoT devices. IoT devices are used to collect different types of data. Various algorithms may be used to analyse data. IoT devices are connected to the internet and all the data of the patients with various health reports are available online and hence security issues arise. IoT sensors, IoT communication technologies, IoT gadgets, components of IoT, IoT layers, cloud and fog computing, benefits of IoT, IoT-based algorithms, IoT security issues, and IoT challenges are discussed in the chapter. Nowadays global epidemic COVID19 has demolished the economy and health services of all the countries worldwide. Usefulness of IoT in COVID19-related issues is explained here.
TopIot Technologies
In this section the different technologies used in IoT are discussed. IoT needs different sensors, connectivity and components. So all these technologies are discussed one by one.
Key Terms in this Chapter
Decision Tree: A decision tree is a tree like model of decisions. In decision tree each branch represents the outcome of the test.
K-Nearest Neighbourhood: K-nearest neighbourhood is a algorithum which stores all available cases and classifies new cases based on a similarity measure. It is used in statistical estimation and pattern recognition.
Neural Network: It is a network/circuit of artificial neurons or nodes.
K-Means Algorithm: It is an iterative algorithm that partition the hole data set into K non overlaping subsets (Clusters). Each data point belongs to only one subset.
RFID: It is called radio-frequency identification. It uses radio frequency electromagnetic waves to identify and track attached to an object automatically.
Support Vector Machines (SVM): Support vector machines are supervised learning models with related learning algorithms for analyising data to be used for classification and regression analysis.
Genetic Algorithm: Genatic algorithm is a metaheuristic which is inspired by the process of natural selection corresponding to the larger class of evolutionary algorithms.
Gaussian Mixture Model (GMM): It is a probabilistic model in which, it is assumed that all the data points are generated from a miture of Gaussian distributions with unknown parameter.