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

Ibrahim Aqeel1, Ibrahim Mohsen Khormi1, Surbhi Bhatia Khan2,3

  • 1College of Computer Science & IT, Jazan University, Jazan 45142, Saudi Arabia.

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Summary

This study introduces an energy-aware artificial intelligence (AI) load balancing model using the Chaotic Horse Ride Optimization Algorithm (CHROA) for cloud-enabled Internet of Things (IoT) environments. The novel CHROA model significantly improves throughput and optimizes energy resources, outperforming existing methods.

Keywords:
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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cloud Computing
  • Internet of Things (IoT)

Background:

  • The rapid growth of Information and Communication Technologies (ICT) has led to the Internet of Things (IoT) and Internet of Everything (IoE).
  • Implementing IoT/IoE faces challenges like limited energy and processing power, especially in data-intensive healthcare applications.
  • There is a critical need for energy-efficient, intelligent load-balancing solutions in cloud-enabled IoT environments.

Purpose of the Study:

  • To propose a novel, energy-aware artificial intelligence (AI)-based load balancing model for cloud-enabled IoT environments.
  • To enhance load balancing and energy resource optimization using the Chaotic Horse Ride Optimization Algorithm (CHROA) and Big Data Analytics (BDA).
  • To evaluate the performance of the proposed CHROA model against existing optimization techniques.

Main Methods:

  • Development of an energy-aware AI load balancing model incorporating the Chaotic Horse Ride Optimization Algorithm (CHROA).
  • Utilization of Big Data Analytics (BDA) for processing and analyzing large volumes of data in IoT environments.
  • Enhancement of the Horse Ride Optimization Algorithm (HROA) using chaotic principles within the CHROA technique for improved optimization.

Main Results:

  • The proposed CHROA model demonstrates superior performance in load balancing and energy optimization compared to existing algorithms.
  • CHROA achieved a significantly higher average throughput (70.122 Kbps) compared to Artificial Bee Colony (ABC), Gravitational Search Algorithm (GSA), and Whale Defense Algorithm with Firefly Algorithm (WD-FA) (58.247–60.819 Kbps).
  • Experimental evaluations confirm the effectiveness of the CHROA model in optimizing energy resources and balancing loads.

Conclusions:

  • The CHROA-based model offers an innovative approach to intelligent load balancing and energy optimization in cloud-enabled IoT systems.
  • The findings highlight the potential of the CHROA model to address key challenges in sustainable IoT/IoE development.
  • The proposed model contributes to the creation of more efficient and energy-conscious cloud-enabled IoT solutions, particularly for healthcare.