Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Hybrid WGWO : whale grey wolf optimization-based novel energy-efficient clustering for EH-WSNs. / Rathore, R.S.; Sangwan, S.; Prakash, S. et al.
In: EURASIP Journal on Wireless Communications and Networking, Vol. 2020, No. 1, 101, 14.05.2020.Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Hybrid WGWO
T2 - whale grey wolf optimization-based novel energy-efficient clustering for EH-WSNs
AU - Rathore, R.S.
AU - Sangwan, S.
AU - Prakash, S.
AU - Adhikari, K.
AU - Kharel, R.
AU - Cao, Y.
PY - 2020/5/14
Y1 - 2020/5/14
N2 - The energy harvesting methods enable WSNs nodes to last potentially forever with the help of energy harvesting subsystems for continuously providing energy, and storing it for future use. The energy harvesting techniques can use various potential sources of energy, such as solar, wind, mechanical, and variations in temperature. Energy-constrained sensor nodes are small in size. Therefore, some mechanisms are required to reduce energy consumption and consequently to improve the network lifetime. The clustering mechanism is used for energy efficiency in WSNs. In the clustering mechanism, the group of sensor nodes forms the clusters. The performance of the clustering process depends on various factors such as the optimal number of clusters formation and the process of cluster head selection. In this paper, we propose a hybrid whale and grey wolf optimization (WGWO)-based clustering mechanism for energy harvesting wireless sensor networks (EH-WSNs). In the proposed research, we use two meta-heuristic algorithms, namely, whale and grey wolf to increase the effectiveness of the clustering mechanism. The exploitation and exploration capabilities of the proposed hybrid WGWO approach are much higher than the traditional various existing metaheuristic algorithms during the evaluation of the algorithm. This hybrid approach gives the best results. The proposed hybrid whale grey wolf optimization-based clustering mechanism consists of cluster formation and dynamically cluster head (CH) selection. The performance of the proposed scheme is compared with existing state-of-art routing protocols. © 2020, The Author(s).
AB - The energy harvesting methods enable WSNs nodes to last potentially forever with the help of energy harvesting subsystems for continuously providing energy, and storing it for future use. The energy harvesting techniques can use various potential sources of energy, such as solar, wind, mechanical, and variations in temperature. Energy-constrained sensor nodes are small in size. Therefore, some mechanisms are required to reduce energy consumption and consequently to improve the network lifetime. The clustering mechanism is used for energy efficiency in WSNs. In the clustering mechanism, the group of sensor nodes forms the clusters. The performance of the clustering process depends on various factors such as the optimal number of clusters formation and the process of cluster head selection. In this paper, we propose a hybrid whale and grey wolf optimization (WGWO)-based clustering mechanism for energy harvesting wireless sensor networks (EH-WSNs). In the proposed research, we use two meta-heuristic algorithms, namely, whale and grey wolf to increase the effectiveness of the clustering mechanism. The exploitation and exploration capabilities of the proposed hybrid WGWO approach are much higher than the traditional various existing metaheuristic algorithms during the evaluation of the algorithm. This hybrid approach gives the best results. The proposed hybrid whale grey wolf optimization-based clustering mechanism consists of cluster formation and dynamically cluster head (CH) selection. The performance of the proposed scheme is compared with existing state-of-art routing protocols. © 2020, The Author(s).
KW - Cluster head (CH)
KW - Clustering
KW - Grey wolf optimization (GWO)
KW - WGWO
KW - Whale optimization algorithm (WOA)
KW - Cluster analysis
KW - Energy efficiency
KW - Energy harvesting
KW - Energy utilization
KW - Heuristic algorithms
KW - Optimization
KW - Power management (telecommunication)
KW - Sensor nodes
KW - Cluster formations
KW - Cluster-head selections
KW - Clustering mechanism
KW - Energy Efficient clustering
KW - Energy-constrained sensor nodes
KW - Exploitation and explorations
KW - Meta heuristic algorithm
KW - Reduce energy consumption
KW - Clustering algorithms
U2 - 10.1186/s13638-020-01721-5
DO - 10.1186/s13638-020-01721-5
M3 - Journal article
VL - 2020
JO - EURASIP Journal on Wireless Communications and Networking
JF - EURASIP Journal on Wireless Communications and Networking
SN - 1687-1472
IS - 1
M1 - 101
ER -