Enhancing the Performance of Healthcare Service in IoT and Cloud Using Optimized Techniques

Abstract

Big data research for health service applications, from the approval of the cloud computing and the Internet of Things (IoT) model of healthcare brought drastic changes in the medical field and improved healthcare services. But the required source to bring the data in a cloud-IoT environment poses a big challenge. In this research work, optimization techniques of virtual machines (VMs) in cloud environment are introduced. The performance of healthcare methods by decreasing the stakeholder’s requirements, execution time, CPU utilization, and storing the patient’s digitals is considered in this research work. The proposed structure defines different steps such as customer devices, customer request (tasks), cloud broker, and network administrator. Three optimization methods such as the Cuckoo Search Algorithm, Particle Swarm Optimization, and Artificial Bee Colony Optimization (ABCO) are employed in the research to optimize the execution time of the stakeholder’s request. The fitness function consists of three fields such as CPU utilization, turn-around time, and waiting time. The experimental result shows the details about three optimization techniques in order to enhance execution time, data processing time, and system efficiency. The simulation result shows that the proposed method decreases the performance rate of total execution time and the system efficiency regarding the real-time improvement of the system. After comparing the proposed optimization methods, ABCO achieves a better efficiency rate of 92.5%, for use in industries.

KEYWORDS:

• Artificial bee colony
• Big data
• Cloud computing
• IoT
• Particle swarm optimization
• Stakeholders’ devices
• Virtual machines

ACKNOWLEDGMENT

The authors are thankful to our colleagues Raghavendran Srinivasan [Research Scholar] who provided expertise that greatly assisted the research, although they may not agree with all of the interpretations provided in this paper. The authors are also grateful to friends for assistance with [Optimization techniques] and who moderated this paper and in that line improved the manuscript significantly.

Additional information

Funding

This research is fully self-funded by authors, There is no external funding agency/organization.

Notes on contributors

Parasuraman Kumar

Parasuraman Kumar obtained his MTech, PhD, degrees in information technology – computer science and engineering from Manonmaniam Sundaranar University, India and MBA in systems from Alagappa University, India. He is currently working as an assistant professor with the Department of Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India. He has published around 70 research papers in international/national journals/proceedings/books. His current research interests include signal and image processing, visual perception, cyber security, pattern recognition and big data analytics. Corresponding author. Email: [email protected]

Karunagaran Silambarasan

Karunagaran Silambarasan has received his Master of Technology in computer science and engineering from SASTRA University, Tanjore, Tamilnadu, India in 2010 and Bachelor of Engineering degree in electronics and communication engineering from Anna University, Tamilnadu, India in 2006. Currently, he is pursuing research work leading to PhD degree in Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli-627012, Tamilnadu, India. His area of research includes cloud computing, virtual machine optimization, network security, Internet of things. He has published over 6 papers in peer-reviewed international journals and 2 IEEE conferences. His current research work includes healthcare services using IoT and cloud environment. Email: [email protected]