Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 7, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

451

Advanced generative adversarial network for optimizing layout of wireless sensor networks.

S Praveen Kumar1, Setu Garg2, Eatedal Alabdulkreem3

  • 1Department of Computer Science and Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu, 611002, India. asv.praveen@gmail.com.

Scientific Reports
|December 31, 2024
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

SELAM: selective ECC-based lightweight authentication for the internet of medical things.

Scientific reports·2026
Same author

Examining the impact of artificial intelligence on physicians' performance: mediating effects of innovative work behavior and skills enhancement.

Journal of health organization and management·2026
Same author

Towards precision agriculture for assessing germination rates and density of rice seedling using hierarchical convolutional neural network on drone imagery.

Scientific reports·2026
Same author

GDT-SwinKid: A hybrid model for precise renal lesion analysis.

PloS one·2026
Same author

Antituberculous Therapy-Induced Rare Skin Toxicity: Bullous Pemphigoid.

Journal of pharmacy & bioallied sciences·2026
Same author

Improving spectral efficiency in distributed massive MIMO in multi-user downlink millimeter wave.

Scientific reports·2026
Same journal

Therapeutic potential of crude protein extracts from two Egyptian freshwater snails Lanistes carinatus and Bellamya unicolor.

Scientific reports·2026
Same journal

Microbial contamination of donor corneas and post-keratoplasty endophthalmitis: a comparison between Japanese and U.S. eye banks using cold storage.

Scientific reports·2026
Same journal

Prevalence and contributing factors of virological non-suppression among adult patients on first-line antiretroviral therapy in tertiary hospitals in Ethiopia.

Scientific reports·2026
Same journal

An in vitro comparison of color stability between alkasite and different restorative materials in various staining solutions.

Scientific reports·2026
Same journal

Toward accessible mRNA LNP formulation: systematic evaluation of mixing strategies and key parameters.

Scientific reports·2026
Same journal

A network analysis of personality traits, mentalizing, and psychological health in Chinese college students.

Scientific reports·2026
See all related articles
This summary is machine-generated.

This study introduces an Advanced Generative Adversarial Network with Piranha Foraging Optimization (AGAN-PFOA) for optimizing Wireless Sensor Networks (WSNs) layouts. The novel method significantly enhances WSN performance across multiple objectives like coverage, lifetime, and energy efficiency.

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless Sensor Networks (WSNs) layout optimization is crucial for cost, detection, and monitoring quality.
  • Existing meta-heuristic methods address only subsets of objectives or are computationally expensive.
  • Layout optimization is an NP-hard combinatorial problem with conflicting objectives.

Purpose of the Study:

  • To present a novel deep learning-based methodology for WSN layout optimization.
  • To address multiple objectives including connectivity, coverage, energy consumption, lifetime, and node count.
  • To develop an efficient and effective solution for the complex WSN layout problem.

Main Methods:

  • A novel Advanced Generative Adversarial Network (AGAN) is employed for layout optimization.
Keywords:
Advanced generative adversarial networkLayout optimizationPiranha foraging optimization algorithmWireless sensor network

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

426
The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.5K

Related Experiment Videos

Last Updated: May 7, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

451
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

426
The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.5K
  • Parameter tuning is performed using the nature-inspired Piranha Foraging Optimization Algorithm (PFOA).
  • The methodology integrates objective function derivation for comprehensive optimization.

    • Main Results:

    • The proposed AGAN-PFOA generated an optimal Pareto front of non-dominated solutions.
    • Achieved superior hyper-volumes and spread of solutions compared to state-of-the-art methods.
    • Demonstrated significant improvements in Packet Delivery Ratio (PDR), coverage, energy consumption, lifetime, alive node count, delay, and routing overhead.

    Conclusions:

    • The AGAN-PFOA methodology offers a powerful approach for WSN layout optimization.
    • The method effectively balances multiple conflicting objectives for improved network performance.
    • Significant performance gains over existing methods highlight the potential of deep learning in WSN design.