ItemConference Paper

Multi-objective Deep Reinforcement Learning Approach for Multiple -Input/Multiple-output Routing in WSN

Title
Multi-objective Deep Reinforcement Learning Approach for Multiple -Input/Multiple-output Routing in WSN
Creator
Reddy, R. Archana; Adnan, Myasar Mundher; Paramesh, S.P.; Petli, Vishwanath; Manohar, M.
Description
The Wireless Sensor Network (WSN) is a network of numerous devices that are interconnected via the internet and significantly impact the network. However, despite their significant applications WSNs face challenges related to network security energy levels and information transmission delays. To address these challenges, a method utilizing Multi-Objective Deep Reinforcement Learning (DRL) has been proposed. The proposed method aims to maximize energy utilization in the network by efficiently managing covered and uncovered cluster network routing. The performance of energy transmission is enhanced through the use of the Markov Decision Process model based on multi-objective DRL combined with training the network using Deep Q Network (DQN) to reduce network energy consumption. Training the network with multiple objectives may pose challenges requiring more samples and leading to higher sample complexity, which can be a limiting factor in real-world applications. Despite this, the proposed multi-objective DRL method demonstrates high performance compared to existing methods such as Particle Swarm Optimization (PSO) and Convolutional Neural Network (CNN). Specifically, multi-Objective DRL method yields superior results, achieving an energy consumption of 42J, Packet Delivery Ratio (PDR) of 90%, and an End-To-End Delay (ETED) of 45 S. These outcomes outperform existing methods in the context of WSNs. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Source
Lecture Notes in Electrical Engineering;Volume;1270;pp.127-137
Date
01-01-2025
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
Artificial neural network; Deep Q network; Multi-objective deep reinforcement learning; Particle swarm optimization and wireless sensor network
Coverage
Reddy R.A., School of Computer Science and Artificial Intelligence, SR University, Telangana, Warangal, India; Adnan M.M., The Islamic University, Najaf, Iraq; Paramesh S.P., Department of Computer Science and Engineering, School of Engineering, Central University of Karnataka, Karnataka, Kalaburagi, India; Petli V., Electronics and Communication Engineering, H.K.E. Societys Sir M. Visvesvaraya College of Engineering, Raichur, India; Manohar M., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Bangalore, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 18761100; ISBN: 978-981977875-1;
Format
online
Language
English
Type
Conference paper

Citation

Reddy, R. Archana; Adnan, Myasar Mundher; Paramesh, S.P.; Petli, Vishwanath; Manohar, M., “Multi-objective Deep Reinforcement Learning Approach for Multiple -Input/Multiple-output Routing in WSN,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/25659.

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