f Enhanced Wireless Communication Optimization with Neural Networks, Proximal Policy Optimization and Edge Computing for Latency and Energy Efficiency
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Research Article |

Enhanced Wireless Communication Optimization with Neural Networks, Proximal Policy Optimization and Edge Computing for Latency and Energy Efficiency

Author(s): N. Kousika*, J. Babitha Thangamalar, N. Pritha, Beulah Jackson and M. Aiswarya

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 12, Issue 2

Publisher : FOREX Publication

Published : 30 June 2024

e-ISSN : 2347-470X

Page(s) : 721-726

DOI: https://doi.org/10.37391/IJEER.120250

Abstract

This research proposes a novel approach for efficient resource allocation in wireless communication systems. It combines dynamic neural networks, Proximal Policy Optimization (PPO), and Edge Computing Orchestrator (ECO) for latency-aware and energy-efficient resource allocation. The proposed system integrates multiple components, including a dynamic neural network, PPO, ECO, and a Mobile Edge Computing (MEC) server. The experimental methodology involves utilizing the NS-3 simulation platform to assess latency and energy efficiency in resource allocation within a wireless communication network, incorporating an ECO, MEC server, and dynamic task scheduling algorithms. It demonstrates a holistic and adaptable approach to resource allocation in dynamic environments, showcasing a notable reduction in latency for devices and tasks. Latency values range from 5 to 20 milliseconds, with corresponding resource utilization percentages varying between 80% and 95%. Additionally, energy-efficient resource allocation demonstrates a commendable reduction in energy consumption, with measured values ranging from 10 to 30 watts, coupled with efficient resource usage percentages ranging from 70% to 85%. These outcomes validate the efficacy of achieving both latency-aware and energy-efficient resource allocation for enhanced wireless communication systems. The proposed system has broad applications in healthcare, smart cities, IoT, real-time analytics, autonomous vehicles, and augmented reality, offering a valuable solution to optimize energy consumption, reduce latency, and enhance system efficiency in these industries.

Keywords: Proximal Policy Optimization, Edge Computing Orchestrator, Mobile Edge Computing server, Dynamic Neural Networks, Wireless Communication System.




N. Kousika*, Assistant Professor, Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore. Tamil Nadu 641008, India; Email: kousika@skcet.ac.in

J. Babitha Thangamalar, Associate Professor, Department of Biomedical Engineering, P. S. R Engineering College, Sevalpatti, Sivakasi-626140, Tamil Nadu, India; Email: babithathangamalar@psr.edu.in

N. Pritha, Assistant Professor, Department of Electronics and Communication Engineering, Panimalar engineering college, Poonamallee, Chennai, Tamil Nadu 600123, India; Email: prithabe28@gmail.com

Beulah Jackson, Professor, Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai-600 062, Tamil Nadu, India; Email: beulah.jack@gmail.com

M. Aiswarya, Assistant Professor, Karpagam Institute of Technology, Coimbatore-641105 Tamil Nadu, India; Email: aishu100896@gmail.com

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N. Kousika, J. Babitha Thangamalar, N. Pritha, Beulah Jackson, and M. Aiswarya (2024), Enhanced Wireless Communication Optimization with Neural Networks, Proximal Policy Optimization and Edge Computing for Latency and Energy Efficiency. IJEER 12(2), 721-726. DOI: 10.37391/IJEER.120250.