Evolution of Swarm Intelligence: A Systematic Review of Particle Swarm and Ant Colony Optimization Approaches in Modern Research

Archives of Computational Methods in Engineering, Год журнала: 2025, Номер unknown

Опубликована: Март 18, 2025

Язык: Английский

---

Ensemble-Based Machine Learning Techniques for Adaptive Wireless Sensor Networks

Advances in computer and electrical engineering book series, Год журнала: 2025, Номер unknown, С. 319 - 360

Опубликована: Фев. 7, 2025

Wireless sensor networks (WSN) have gained popularity in next-generation IoT connectivity due to their sustainability and low maintenance. However, the dynamic nature of energy sources environmental conditions presents challenges security reliability WSNs, particularly mitigating various network attacks. Machine learning offers solutions these by enabling adaptive real-time behaviour. This chapter addresses WSN applying ML techniques a multi-class dataset attacks such as normal, flooding, TDMA, grayhole, blackhole. SMOTE is applied manage class imbalance, an ensemble framework proposed with classifiers logistic regression, random forest, gradient boost, xtreme decision tree, LGBM, SVM, CatBoost were predict WSN-DS dataset. The models are rigorously tested evaluated using accuracy, precision, recall, F1-score. Gradient catboost outperform all other achieving 98% accuracy.

Язык: Английский

---

An energy-aware clustering approach based on Gini coefficient and IPSO applied for energy-constrained applications

Telecommunication Systems, Год журнала: 2025, Номер 88(2)

Опубликована: Апрель 9, 2025

Язык: Английский

---

Optimizing Wireless Sensor Network Node Placement Using Bacterial Foraging Optimization

Communications in computer and information science, Год журнала: 2025, Номер unknown, С. 178 - 187

Опубликована: Янв. 1, 2025

Язык: Английский

---

Advancements in Machine Learning‐Enhanced Green Wireless Sensor Networks: A Comprehensive Survey on Energy Efficiency, Network Performance, and Future Directions

Journal of Sensors, Год журнала: 2025, Номер 2025(1)

Опубликована: Янв. 1, 2025

Wireless sensor networks (WSNs) are a collection of nodes that collect data from the environment using wireless technology. WSNs have many applications in various domains, such as public utilities, industrial monitoring and control, defense military activities. However, limited energy, short network lifetime, high bandwidth requirements, low throughput (TP), unreliable connections. Green (GWSNs) approaches optimize energy consumption enhance sustainable networks. Despite these advancements, nonadaptability to dynamic conditions use static historical necessitates introducing machine learning (ML) techniques address challenges. GWSNs aim reduce environmental impact, while ML will improve processing performance. This paper surveys recent advances ML‐based GWSNs, covering different aspects structure, exchange, location information, quality service (QoS), multiple path support. We also present performance metrics, implementation issues, future trends GWSNs. The introduces new taxonomy categorizing based on architecture, sharing, data, multipath support, QoS. survey findings show can achieve up 50% savings, 30% TP improvement, 40% delay reduction (DR) compared conventional WSNs.

Язык: Английский

---

Hybrid Differential Evolution Algorithm for Optimal WSN Node Deployment

Communications in computer and information science, Год журнала: 2025, Номер unknown, С. 566 - 575

Опубликована: Янв. 1, 2025

Язык: Английский

---

Multi-objective Sand Cat Swarm Optimization for Efficient Multi-hop Routing in Wireless Sensor Networks

Lecture notes on data engineering and communications technologies, Год журнала: 2025, Номер unknown, С. 161 - 171

Опубликована: Янв. 1, 2025

Язык: Английский

---

Policy Framework for Realizing Net-Zero Emission in Smart Cities

Archives of Computational Methods in Engineering, Год журнала: 2024, Номер unknown

Опубликована: Апрель 30, 2024

Язык: Английский

---

Multi-Objective Optimization for Coverage and Connectivity in Wireless Sensor Networks

Опубликована: Июнь 26, 2024

Язык: Английский

---

Graph Neural Networks for Routing Optimization: Challenges and Opportunities

Sustainability, Год журнала: 2024, Номер 16(21), С. 9239 - 9239

Опубликована: Окт. 24, 2024

In this paper, we explore the emerging role of graph neural networks (GNNs) in optimizing routing for next-generation communication networks. Traditional protocols, such as OSPF or Dijkstra algorithm, often fall short handling complexity, scalability, and dynamic nature modern network environments, including unmanned aerial vehicle (UAV), satellite, 5G By leveraging their ability to model topologies learn from complex interdependencies between nodes links, GNNs offer a promising solution distributed scalable optimization. This paper provides comprehensive review latest research on GNN-based methods, categorizing them into supervised learning modeling, optimization, reinforcement tasks. We also present detailed analysis existing datasets, tools, benchmarking practices. Key challenges related real-world deployment, explainability, security are discussed, alongside future directions that involve federated learning, self-supervised online techniques further enhance GNN applicability. study serves first survey aiming inspire practical applications

Язык: Английский

---