A Centralized Cluster-Based Hierarchical Approach for Green Communication in a Smart Healthcare System

• Main Authors: Guojie Yang, Mian Ahmad Jan, Varun G. Menon, P. G. Shynu, Mian Muhammad Aimal, Mohammad Dahman Alshehri
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Green IoT; patient monitoring system; cluster-based hierarchical routing; cluster head; energy-efficiency
• Online Access: https://ieeexplore.ieee.org/document/9103526/

The emergence of the Internet of Things (IoT) has revolutionized our digital and virtual worlds of connected devices. IoT is a key enabler for a wide range of applications in today's world. For example, in smart healthcare systems, the sensor-embedded devices monitor various vital signs of the patients. These devices operate on small batteries, and their energy need to be utilized efficiently. The need for green IoT to preserve the energy of these devices has never been more critical than today. The existing smart healthcare approaches adopt a heuristic approach for energy conservation by minimizing the duty-cycling of the underlying devices. However, they face numerous challenges in terms of excessive overhead, idle listening, overhearing, and collision. To circumvent these challenges, we have proposed a cluster-based hierarchical approach for monitoring the patients in an energy-efficient manner, i.e., green communication. The proposed approach organizes the monitoring devices into clusters of equal sizes. Within each cluster, a cluster head is designated to gather data from its member devices and broadcast to a centralized base station. Our proposed approach models the energy consumption of each device in various states, i.e., idle, sleep, awake, and active, and also performs the transitions between these states. We adopted an analytical approach for modeling the role of each device and its energy consumption in various states. Extensive simulations were conducted to validate our analytical approach by comparing it against the existing schemes. The experimental results of our approach enhance the network lifetime with a reduced energy consumption during various states. Moreover, it delivers a better quality of data for decision making on the patient's vital signs.