Open Access

ARTICLE

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

by Farhan Aadil¹, Bilal Mehmood¹, Najam Ul Hasan², Sangsoon Lim^3,*, Sadia Ejaz¹, Noor Zaman⁴

1 Computer Science Department, COMSATS University Islamabad, Attock Campus, 43600, Pakistan
2 Department of Electrical and Computer Engineering, Dhofar University, Salalah, Oman
3 Department of Computer Engineering, Sungkyul University, Anyang, 430010, Korea
4 School of Computer Science and Engineering (SCE), Taylor’s University, Malaysia

* Corresponding Author: Sangsoon Lim. Email:

(This article belongs to the Special Issue: Emerging Trends in Cyber Security for Communication Networks)

Computers, Materials & Continua 2021, 68(2), 2499-2513. https://doi.org/10.32604/cmc.2021.014647

Received 05 October 2020; Accepted 02 March 2021; Issue published 13 April 2021

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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Citations