Energy-Efficient Routing in Underwater Wireless Sensor Networks Through Cluster-Dragonfly Optimization

In this paper, develop a power control scheme for underwater wireless sensor networks that takes into account the impact of communication ranges on network performance, including energy utilization and delay overhead. The primary objective of our scheme is to assign a differentiated optimal communic...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:2024 Third International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) s. 1 - 5
Hlavní autoři: D, Anitha, Husain, Saif O., Velpula, Srikanth, N, Rajani, Dineshkumar, R.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 26.04.2024
Témata:
Chaotic communication
chaotic search-and-rescue optimization
cluster-dragonfly optimization
Clustering algorithms
Energy consumption
hybrid cat cheetah optimization algorithm
Packet loss
Particle swarm optimization
Routing
self-adaptive glow worm swarm optimization and underwater wireless sensor network
Wireless sensor networks
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:In this paper, develop a power control scheme for underwater wireless sensor networks that takes into account the impact of communication ranges on network performance, including energy utilization and delay overhead. The primary objective of our scheme is to assign a differentiated optimal communication radius for each sensor node, enhancing overall network efficiency. The proposed method utilizes energy-aware Cluster-Dragonfly Optimization (CDFO), but it might face challenges in handling large-scale optimization, and the scalability of the algorithm may be limited when dealing with high-dimensional or complex optimization. Although the proposed CDFO method exhibits high performance compared to existing methods, such as Self-Adaptive Glow Worm Swarm Optimization (SA-GWO), Improved Coyote Optimization Algorithm (ICOA), Hybrid Cat Cheetah Optimization algorithm (HCCOA), and Chaotic Search-and-Rescue Optimization (CSRO), it may still face challenges in terms of handling large-scale optimization. Nevertheless, the proposed CDFO yields impressive results, achieving a Packet Delivery Ratio (PDR) of 99%, Packet Loss Ratio (PLR) of 4%, Energy Consumption of 9%, and Alive Node count of 330. These outcomes outperform existing methods in the context of Underwater WSN.
DOI:10.1109/ICDCECE60827.2024.10548883