Energy Conservation Clustering through Agent Nodes and Clusters (EECANC) for Wearable Health Monitoring and Smart Building Automation in Smart Hospitals using Wireless Sensor Networks

Sulalah Qais Mirkar; Shilpa Shinde

doi:10.35882/jeeemi.v7i4.1082

Sulalah Qais Mirkar Mukesh Patel School of Technology Management & Engineering, SVKM's NMIMS, Maharashtra, Mumbai, India; Department of Computer Engineering, Ramrao Adik Institute of Technology, D.Y. Patil Deemed to be University Navi Mumbai, Maharashtra, India https://orcid.org/0000-0001-7660-524X
Shilpa Shinde Department of Computer Engineering, Ramrao Adik Institute of Technology, D.Y. Patil Deemed to be University Navi Mumbai, Maharashtra, India https://orcid.org/0000-0003-2619-3464

DOI: https://doi.org/10.35882/jeeemi.v7i4.1082

Keywords: Wireless Sensor Network, Smart Hospital , Energy Efficiency, Clustering , Wearable patient monitoring sensors, Smart Building Management System.

Abstract

Wireless Sensor Networks (WSNs) play a vital role in enabling real-time patient monitoring, medical device tracking, and automated management of building operations in smart hospitals. Wearable health sensors and hospital automation systems produce a constant flow of data, resulting in elevated energy usage and network congestion. This study introduces an advanced framework named Energy Conservation via Clustering by Agent Nodes and Clusters (EECANC), designed to improve energy efficiency, extend the network's longevity, and facilitate smart building automation in hospitals. The EECANC protocol amalgamates wearable medical monitoring (oxygen saturation, body temperature, heart rate, and motion tracking) with intelligent hospital building automation (HVAC regulation, lighting management, and security surveillance) through a hierarchical Wireless Sensor Network-based clustering system. By reducing routing and data redundancy, cluster heads (CHs) and agent nodes (ANs) reduce redundant transmissions and extend the life of sensor batteries. EECANC limits direct interaction with the hospital's Smart Building Management System, thereby reducing emergency response times and improving energy efficiency throughout the hospital. The efficiency of EECANC was proven by comparing its performance with other existing clustering protocols, including EECAS, ECRRS, EA-DB-CRP, and IEE-LEACH. The protocol achieved a successful packet delivery rate of 83.33% to the base station, exceeding the performance of EECAS (83.33%), ECRRS (48.45%), EA-DB-CRP (54.37%), and IEE-LEACH (59.13%). The system demonstrated better energy utilization, resulting in a longer network longevity and lower transmission costs especially during high-traffic medical events. It is clear from the first and last node death rates that EECANC is the most energy-efficient protocol, significantly better than the other methods available. The EECANC model supports hospital automation, enhances patient safety, and promotes sustainability, providing a cost-effective and energy-efficient solution for future smart healthcare facilities

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Author Biography

Sulalah Qais Mirkar, Mukesh Patel School of Technology Management & Engineering, SVKM's NMIMS, Maharashtra, Mumbai, India; Department of Computer Engineering, Ramrao Adik Institute of Technology, D.Y. Patil Deemed to be University Navi Mumbai, Maharashtra, India

Sulalah Qais Mirkar completed her B.Tech degree in Information Technology from SNDT College
of Engineering, Mumbai, in 2006. She earned her M.Tech degree in Information Technology from
MPSTME, NMIMS University, in 2013. She is currently an Assistant Professor in the Department of
Information Technology at MPSTME, NMIMS University, Mumbai.
She has industry experience, having worked at the Bombay Stock Exchange as a Software Engineer
and in the derivatives market for four years, specializing in Sybase with C in the Unix domain.
She is currently pursuing her Ph.D. in Computer Engineering from RAIT, DY Patil University, Navi
Mumbai.
Her primary research interests include the Internet of Things (IoT), Wireless Sensor Networks
(WSN), Artificial Intelligence, Data Mining, and Visual Analytics. She can be contacted at sulalah.
rizwani@gmail.com

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