Green IOT and Sustainable Wireless Sensor Networks: A Deep

Reinforcement Learning Approach for Energy Optimization and

Qos Enhancement

Surapaneni Phani Praveen1* Massila Kamalrudin2 Sai Srinivas Vellela3

Deshinta Arrova Dewi4 Dedeepya Pulletikurthy5 Klodian Dhoska6

1 Associate Professor, Department of Computer Science and Engineering, Prasad V. Potluri Siddhartha Institute of Technology,

Kanuru, Vijayawada – 520007, Andhra Pradesh, India

2 Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM), Melaka, Malaysia.

3 Associate Professor, Department of CSE – Data Science, Chalapathi Institute of Technology, Guntur – 522016, Andhra Pradesh,

India

4 Professor, Faculty of Data Science and Information Technology (FDSIT), INTI International University, Malaysia

5 Department of Computer Science & Engineering, SRM university AP, Amaravati, Andhra Pradesh, India

6 Polytechnic University of Tirana, Tirana, Albania

Received: January 18, 2026 Revised: February 12, 2026 Accepted: March 22, 2026 ⋆ Corresponding author

ABSTRACT

Due to the increasing adoption of IoT applications, there is a growing necessity for energy-efficient and sustainable

WSN. Yet, traditional routing protocols tend to face problems like energy wastage, congestion, unreliable communication,

and shorter network life spans under dynamic network conditions. This study presents the development

of a DRL-powered Green IoT framework to enhance efficient communication through WSN while optimizing

QoS performance. Specifically, the proposed framework employs the Deep Q-Network, Double Deep Q-Learning,

adaptive clustering, and multi-objective optimization in order to enhance both routing and QoS performance.The

model makes use of residual energy, congestion levels, throughput, delivery rate, and communication delays during

its decision-making processes. Experimentation with the model was performed by making use of Python and NS-3.

The simulation results showed that the presented model performed better than traditional routing methods like

LEACH, PEGASIS, and HEED when evaluated on factors like energy preservation, enhanced throughput, minimized

congestion, reduced delays, and increased network life spans. It can be concluded that DRL-powered communication

optimization is a viable solution for the future development of Green IoT communication systems.

Keywords: Green IoT Wireless Sensor Networks Deep Reinforcement Learning Energy Efficiency QoS Enhancement

Energy Sustainable Communication Adaptive Routing Network Lifetime

1. INTRODUCTION

The advent of Internet of Things (IoT) has revolutionized

modern communication networks in that it enables automatic

information sharing among different smart objects connected

in the network. With more deployments, the IoT has produced

a lot of communication information that necessitates efficient

management solutions for the network (Chen et al., 2014).