Advanced generative adversarial network for optimizing layout of wireless sensor networks DOI Creative Commons
Sumit Kumar,

Setu Garg,

Eatedal Alabdulkreem

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Dec. 30, 2024

The best layout design related to the sensor node distribution represents one among major research questions in Wireless Sensor Networks (WSNs). It has a direct impact on WSNs' cost, detection capabilities, and monitoring quality. optimization of several conflicting objectives, including as load balancing, coverage, lifetime, connection, energy consumption nodes, is necessary for optimization. Layout an NP-hard combinatorial issue. A number meta-heuristic strategies have been put out address this issue past ten years. Nevertheless, these methods only addressed subset objectives-combinations consumption, count area lifetime-or they offered computationally costly solutions. Therefore, paper presents problem using novel intelligent deep learning-based methodology. Here, objective cover numerous objectives associated with optimal layouts homogeneous WSNs that involves connectivity, nodes. handled by Advanced Generative Adversarial Network (AGAN), where parameter tuning performed nature inspired algorithm called Piranha Foraging Optimization Algorithm (PFOA), consideration deriving function. Simulation findings revealed proposed AGAN-PFOA generated Pareto front non-dominated solutions having better hyper-volumes well spread than state-of-the-art WSN terms PDR, alive count, delay, routing overhead 61.46%, 15.12%, 12.67%, 65.91%, 70.59%, 44.88%, 68.86% existing respectively.

Language: Английский

Enhanced Deep Learning-Based Optimization Model for the Coverage Optimization in Wireless Sensor Networks DOI Creative Commons
Sudhir Kumar,

M. V. S. S. Nagendranath,

Jamal Alsamri

et al.

International Journal of Computational Intelligence Systems, Journal Year: 2025, Volume and Issue: 18(1)

Published: April 14, 2025

Language: Английский

Citations

0
Advanced generative adversarial network for optimizing layout of wireless sensor networks DOI Creative Commons
Sumit Kumar,

Setu Garg,

Eatedal Alabdulkreem

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Dec. 30, 2024

The best layout design related to the sensor node distribution represents one among major research questions in Wireless Sensor Networks (WSNs). It has a direct impact on WSNs' cost, detection capabilities, and monitoring quality. optimization of several conflicting objectives, including as load balancing, coverage, lifetime, connection, energy consumption nodes, is necessary for optimization. Layout an NP-hard combinatorial issue. A number meta-heuristic strategies have been put out address this issue past ten years. Nevertheless, these methods only addressed subset objectives-combinations consumption, count area lifetime-or they offered computationally costly solutions. Therefore, paper presents problem using novel intelligent deep learning-based methodology. Here, objective cover numerous objectives associated with optimal layouts homogeneous WSNs that involves connectivity, nodes. handled by Advanced Generative Adversarial Network (AGAN), where parameter tuning performed nature inspired algorithm called Piranha Foraging Optimization Algorithm (PFOA), consideration deriving function. Simulation findings revealed proposed AGAN-PFOA generated Pareto front non-dominated solutions having better hyper-volumes well spread than state-of-the-art WSN terms PDR, alive count, delay, routing overhead 61.46%, 15.12%, 12.67%, 65.91%, 70.59%, 44.88%, 68.86% existing respectively.

Language: Английский

Citations

1