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Home / Equine Research Bank / Sensors (Basel) / Load Balancing Using Artificial Intelligence for Cloud-Enabl...
Sensors (Basel, Switzerland)2023; 23(11); 5349; doi: 10.3390/s23115349

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

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.
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Publication Date: 2023-06-05 PubMed ID: 37300076PubMed Central: PMC10256013DOI: 10.3390/s23115349Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The principal concept of this research paper is a novel, energy-aware artificial intelligence-based load balancing model designed for the rapidly growing cloud-enabled Internet of Things in health care. Through the use of the Chaotic Horse Ride Optimization Algorithm and big data analytics, the model aims to optimize energy resources and effectively manage large volumes of data.

Internet of Things and Need for Load Balancing

  • The Internet of Things (IoT) has seen rapid growth thanks to advancements in information and communication technologies. Its evolution into the Internet of Everything (IoE) allows unprecedented levels of connectivity and data generation.
  • The implementation of this technology, particularly in the health care field where large amounts of data are generated, faces challenges including limited energy resources and processing power.
  • This creates a need for energy-efficient, intelligent load balancing models to handle the data traffic and ensure efficient system operation.

Proposed Solution – Chaotic Horse Ride Optimization Algorithm

  • In this paper, the authors propose a solution using an energy-aware artificial intelligence-based load balancing model. The model employs the Chaotic Horse Ride Optimization Algorithm (CHROA) along with big data analytics.
  • CHROA enhances the optimization capacity of the original Horse Ride Optimization Algorithm (HROA), using the principles of chaos theory to contribute to higher levels of dynamism and randomness.
  • Through the application of artificial intelligence techniques, the proposed model can balance the load and optimize available energy resources in a cloud-enabled IoT environment.

Comparison with Existing Models

  • This model is evaluated using various metrics and compared with existing models including the Artificial Bee Colony (ABC), Gravitational Search Algorithm (GSA), and Whale Defense Algorithm with Firefly Algorithm (WD-FA).
  • Experimental results revealed that the proposed model outperforms the existing models. For example, while the ABC, GSA, and WD-FA techniques had average throughputs of 58.247 Kbps, 59.957 Kbps, and 60.819 Kbps respectively, the CHROA model delivered an average throughput of 70.122 Kbps.

Impact and Future Applications

  • This research indicates the potential of the proposed CHROA-based model as it offers an innovative approach to intelligent load balancing and energy optimization in cloud-enabled IoT environments.
  • Its effectiveness in addressing the critical challenges associated with cloud-enabled IoT in healthcare implies its potential for broader application in IoT/IoE solutions, providing efficiency and sustainability.

Cite This Article

APA
Aqeel I, Khormi IM, Khan SB, Shuaib M, Almusharraf A, Alam S, Alkhaldi NA. (2023). Load Balancing Using Artificial Intelligence for Cloud-Enabled Internet of Everything in Healthcare Domain. Sensors (Basel), 23(11), 5349. https://doi.org/10.3390/s23115349

Publication

Sensors (Basel, Switzerland)
ISSN: 1424-8220
NlmUniqueID: 101204366
Country: Switzerland
Language: English
Volume: 23
Issue: 11
PII: 5349

Researcher Affiliations

Aqeel, Ibrahim
  • College of Computer Science & IT, Jazan University, Jazan 45142, Saudi Arabia.
Khormi, Ibrahim Mohsen
  • College of Computer Science & IT, Jazan University, Jazan 45142, Saudi Arabia.
Khan, Surbhi Bhatia
  • Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon.
  • Department of Data Science, School of Science, Engineering and Environment, University of Salford, Manchester M5 4WT, UK.
Shuaib, Mohammed
  • College of Computer Science & IT, Jazan University, Jazan 45142, Saudi Arabia.
Almusharraf, Ahlam
  • Department of Business Administration, College of Business and Administration, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Alam, Shadab
  • College of Computer Science & IT, Jazan University, Jazan 45142, Saudi Arabia.
Alkhaldi, Nora A
  • Department of Computer Science, College of Computer Science and Information Technology, King Faisal University, Al Hasa 31982, Saudi Arabia.

MeSH Terms

  • Animals
  • Horses
  • Artificial Intelligence
  • Algorithms
  • Intelligence
  • Awareness
  • Internet

Conflict of Interest Statement

The authors declare no conflict of interest.

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