Artificial Intelligence in Next-Generation Networking: Energy Efficiency Optimization in IoT Networks Using Hybrid LEACH Protocol-Reference-Cited by-同舟云学术

Artificial Intelligence in Next-Generation Networking: Energy Efficiency Optimization in IoT Networks Using Hybrid LEACH Protocol

Published:2024-05-15 Issue:5 Volume:5 Page:
ISSN:2661-8907
Container-title:SN Computer Science
language:en
Short-container-title:SN COMPUT. SCI.

Author:

Khan Surbhi Bhatia,Kumar Ankit,Mashat Arwa,Pruthviraja Dayananda,Imam Rahmani Mohammad Khalid,Mathew Jimson

Abstract

AbstractThe convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) is significantly transforming the landscape of future networking. The Internet of Things (IoT) is a technological paradigm that encompasses embedded systems, wireless sensors, and automation, facilitating the integration of various applications ranging from smart homes to wearable devices. In addition, the advent of artificial intelligence (AI) amplifies this influence by providing data-driven analytics, optimising processes, and presenting novel opportunities for growth. Nevertheless, the widespread adoption of devices within Internet of Things (IoT) networks gives rise to apprehensions regarding increased energy consumption. In order to ensure the longevity of network operations, it is imperative to employ energy-efficient protocols for sensor nodes that possess limited power resources. One example of a protocol that demonstrates this concept is the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. This protocol effectively divides networks into clusters and dynamically adjusts the cluster heads to optimise the transmission of data to the base stations. Our study enhances the LEACH protocol by incorporating digital twin simulation, thereby enhancing the efficiency of IoT systems. Virtual network models and AI analytics are employed to assess energy consumption and performance. Cache nodes play a crucial role within this framework as they collect data from cluster heads in order to transmit it to the base station. By leveraging artificial intelligence (AI) and simulation techniques, we are able to improve the energy efficiency and reliability of the Internet of Things (IoT) systems. The findings indicate a significant reduction of 83% in non-functioning nodes and a notable increase of 1.66 times in energy levels of nodes compared to conventional approaches. This study highlights a potential direction for energy-efficient, AI-enhanced Internet of Things (IoT) networking through the utilisation of the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol.

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s42979-024-02778-5.pdf

Reference68 articles.

1. O. Vermesan SINTEF, Norway, Dr. Peter Friess EU, Belgium, “Internet of Things: Converging Technologies for Smart Environments and Integrated Ecosystems”, river publishers’ series in communications, vol. 9, issue 4, pp. 25–30, 2013.

2. O. Vermesan SINTEF, Norway, Dr. Peter Friess EU, Belgium, “Internet of Things–From Research and Innovation to Market Deployment”, river publishers’ series in communications, vol. 15, issue 8, pp. 125–130, 2014.

3. O. Vermesan, P. Friess, P. Guillemin, S. Gusmeroli, “Internet of Things Strategic Research Agenda”, Chapter 2 in Internet of Things -Global Technological and Societal Trends, River Publishers, vol. 4, issue 1, pp. 54–66, 2011.

4. M. Serrano, Insight Centre for Data Analytics, Ireland, Omar Elloumi, Alcatel Lucent, France, Paul Murdock, Landis+Gyr, Switzerland, “Alliance for Internet of Things Innovation, Semantic Interoperability”, Release 2.0, AIOTI WG03 – IoT Standardisation, vol. 5, issue 5, pp. 15–30, 2015.

5. Serrano M, Barnaghi P, Cousin FCP. OvidiuVermesan, Peter Friess, “Internet of Things Semantic Interoperability: Research Challenges, Best Practices, Recommendations and Next Steps.” European research cluster on the internet of things, IERC. 2015;9(2):18–35.