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

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.