An Energy Efficient Wireless Sensor Network with Flamingo Search Algorithm Based Cluster Head Selection

Wireless sensor networks (WSN) are emerging versatile and low-cost solutions for several applications. However, energy efficiency is a major issue in WSNs. The sensor nodes typically have limited energy but the energy consumption exceeds during data transmission. An energy efficient cluster based ro...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Wireless personal communications Ročník 130; číslo 3; s. 1503 - 1525
Hlavní autoři: Abraham, Robin, Vadivel, M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.06.2023
Springer Nature B.V
Témata:
Batteries
Clustering
Communication
Communications Engineering
Complexity
Computer Communication Networks
Data transmission
Energy consumption
Energy efficiency
Engineering
Internet of Things
Networks
Nodes
Optimization algorithms
Protocol
Residual energy
Search algorithms
Sensors
Shortest-path problems
Signal,Image and Speech Processing
Wireless sensor networks
Wireless sensor networks
Ultra-scalable ensemble clustering
Q-learning
Cluster head selection
Routing protocol
ISSN:0929-6212, 1572-834X
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Wireless sensor networks (WSN) are emerging versatile and low-cost solutions for several applications. However, energy efficiency is a major issue in WSNs. The sensor nodes typically have limited energy but the energy consumption exceeds during data transmission. An energy efficient cluster based routing protocol reduces the transmission distance among the base station (BS) and the sensor nodes in terms of organizing the nodes in the form of clusters and evade the nodes with lower energy. Therefore, energy efficient Ultra-Scalable Ensemble Clustering technique is introduced in this work to cluster the nodes for handling large data. Then, the Flamingo Search Algorithm is employed for cluster head (CH) selection due to its less computational complexity and high stability. Finally, Q-Learning approach is adopted to select the shortest path between CHs and BS as it is capable of path selection at complex network conditions. The reward points in this approach are generated based on the objective function that considers the distance among the CH and BS, coverage area and energy consumption. Experiments are evaluated and analyzed with existing approaches in terms of alive nodes, time consumption, rounds for last node dead, first node dead, half node dead, throughput and total residual energy. The consequences prove that the offered technique can enhance the energy efficiency of WSN compared to similar existing approaches.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0929-6212
1572-834X
DOI:10.1007/s11277-023-10342-2