Remote Health Monitoring Using IoT-Based Smart Wireless Body Area Network

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Abstract
A wireless body area network (WBAN) consists of tiny health-monitoring sensors implanted in or placed on the human body. These sensors are used to collect and communicate human medical and physiological data and represent a subset of the Internet of Things (IoT) systems. WBANs are connected to medical servers that monitor patients’ health. This type of network can protect critical patients’ lives due to the ability to monitor patients’ health continuously and remotely. The inter-WBAN communication provides a dynamic environment for patients allowing them to move freely. However, during patient movement, the WBAN patient nodes may become out of range of a remote base station. Hence, to handle this problem, an efficient method for inter-WBAN communication is needed. In this study, a method using a cluster-based routing technique is proposed. In the proposed method, a cluster head (CH) acts as a gateway between the cluster members and the external network, which helps to reduce the network’s overhead. In clustering, the cluster’s lifetime is a vital parameter for network efficiency. Thus, to optimize the CH’s selection process, three evolutionary algorithms are employed, namely, the ant colony optimization (ACO), multi-objective particle swarm optimization (MOPSO), and the comprehensive learning particle swarm optimization (CLPSO). The performance of the proposed method is verified by extensive experiments by varying values of different parameters, including the transmission range, node number, node mobility, and grid size. A comprehensive comparative analysis of the three algorithms is conducted by extensive experiments. The results show that, compared with the other methods, the proposed ACO-based method can form clusters more efficiently and increase network lifetime, thus achieving remarkable network and energy efficiency. The proposed ACO-based technique can also be used in other types of ad-hoc networks as well.

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