Load Balancing Using Artificial Intelligence for Cloud-Enabled Internet of Everything in Healthcare Domain

Ibrahim Aqeel; Ibrahim Mohsen Khormi; Surbhi Bhatia Khan; Mohammed Shuaib; Ahlam Almusharraf; Shadab Alam; Nora A Alkhaldi

doi:10.3390/s23115349

Load Balancing Using Artificial Intelligence for Cloud-Enabled Internet of Everything in Healthcare Domain

Sensors (Basel). 2023 Jun 5;23(11):5349. doi: 10.3390/s23115349.

Authors

Ibrahim Aqeel¹, Ibrahim Mohsen Khormi¹, Surbhi Bhatia Khan^{2

3}, Mohammed Shuaib¹, Ahlam Almusharraf⁴, Shadab Alam¹, Nora A Alkhaldi⁵

Affiliations

¹ College of Computer Science & IT, Jazan University, Jazan 45142, Saudi Arabia.
² Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon.
³ Department of Data Science, School of Science, Engineering and Environment, University of Salford, Manchester M5 4WT, UK.
⁴ Department of Business Administration, College of Business and Administration, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
⁵ Department of Computer Science, College of Computer Science and Information Technology, King Faisal University, Al Hasa 31982, Saudi Arabia.

Abstract

The emergence of the Internet of Things (IoT) and its subsequent evolution into the Internet of Everything (IoE) is a result of the rapid growth of information and communication technologies (ICT). However, implementing these technologies comes with certain obstacles, such as the limited availability of energy resources and processing power. Consequently, there is a need for energy-efficient and intelligent load-balancing models, particularly in healthcare, where real-time applications generate large volumes of data. This paper proposes a novel, energy-aware artificial intelligence (AI)-based load balancing model that employs the Chaotic Horse Ride Optimization Algorithm (CHROA) and big data analytics (BDA) for cloud-enabled IoT environments. The CHROA technique enhances the optimization capacity of the Horse Ride Optimization Algorithm (HROA) using chaotic principles. The proposed CHROA model balances the load, optimizes available energy resources using AI techniques, and is evaluated using various metrics. Experimental results show that the CHROA model outperforms existing models. For instance, while the Artificial Bee Colony (ABC), Gravitational Search Algorithm (GSA), and Whale Defense Algorithm with Firefly Algorithm (WD-FA) techniques attain average throughputs of 58.247 Kbps, 59.957 Kbps, and 60.819 Kbps, respectively, the CHROA model achieves an average throughput of 70.122 Kbps. The proposed CHROA-based model presents an innovative approach to intelligent load balancing and energy optimization in cloud-enabled IoT environments. The results highlight its potential to address critical challenges and contribute to developing efficient and sustainable IoT/IoE solutions.

Keywords: Internet of Everything; Internet of Things; big data analytics; cloud computing; clustering; healthcare; load balancing.

MeSH terms

Algorithms*
Animals
Artificial Intelligence*
Awareness
Horses
Intelligence
Internet

Grants and funding

This research is supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R432), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.