Energy efficient clustering and sink mobility protocol using Improved Dingo and Boosted Beluga Whale Optimization Algorithm for extending network lifetime in WSNs
In Wireless Sensor Networks (WSNs), the potential design challenge of energy efficiency is determined to be handled through the strategies of clustering and routing. The approaches of clustering and routing in WSNs pertain to the problems of Non-deterministic Polynomial (NP)-hard optimization. In th...
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| Vydáno v: | Sustainable computing informatics and systems Ročník 43; s. 101008 |
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| Jazyk: | angličtina |
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01.09.2024
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| ISSN: | 2210-5379 |
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| Abstract | In Wireless Sensor Networks (WSNs), the potential design challenge of energy efficiency is determined to be handled through the strategies of clustering and routing. The approaches of clustering and routing in WSNs pertain to the problems of Non-deterministic Polynomial (NP)-hard optimization. In this context, swarm intelligence-based algorithms are identified to be suitable and ideal for determining near-optimal and optimal solutions in the search space. On the other hand, APTEEN routing protocol possesses the issues that are related to unnecessary energy drain, ineffective overall network coverage and premature death of certain nodes. To address these issues, an attempt to optimize the APTEEN routing protocol using Dingo Optimization Algorithm (DOA) and Beluga Whale Optimization Algorithm (BWOA) is made in this proposed clustering protocol. With this motivation, Improved Dingo and Boosted Beluga Whale Optimization Algorithm (IDBBWOA) is proposed for determining the optimal cluster head and perform energy-efficient routing to minimized the energy consumption and maximize the lifetime of the network. It specifically used Improved Dingo Optimization Algorithm (IDOA) for attaining cluster head (CH) selection and energy efficient routing through the adoption fitness parameters of Residual Energy, Distance within and between Clusters, Network coverage, Node Degree for maximizing the rate of reliable data dissemination. It also incorporated Boosted Beluga Whale Optimization Algorithm (BBWOA) for determining the optimal points over the sink node can be moved to prevent multi-hop between CHs and the sink nodes, since it is essential for addressing the issue of hot-spot and extends the network lifetime. The simulation results of the proposed IDBBWOA approach revealed its efficacy in improving the mean throughput by 18.92 %, sustaining alive nodes by 34.28 %, and maintaining residual energy by 29.34 %, compared to the benchmarked approaches used for evaluation.
•In this paper, Improved Dingo and Boosted Beluga Whale Optimization Algorithm (IDBBWOA) is proposed for determining the optimal cluster head and perform energy-efficient routing to minimize the energy consumption and maximize the lifetime of the network.•It specifically used Improved Dingo Optimization Algorithm (IDOA) for attaining cluster head (CH) selection and energy efficient routing through the adoption fitness parameters of Residual Energy, Distance within and between Clusters, Network coverage, Node Degree for maximizing the rate of reliable data dissemination.•It also incorporated Boosted Beluga Whale Optimization Algorithm (BBWOA) for determining the optimal points over the sink node can be moved to prevent multi-hop between CHs and the sink nodes, since it is essential for addressing the issue of hot-spot and extends the network lifetime.•The simulation results of the proposed IDBBWOA approach revealed its efficacy in improving the mean throughput by 18.92 %, sustaining alive nodes by 34.28 %, and maintaining residual energy by 29.34 %, compared to the benchmarked approaches used for evaluation. |
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| AbstractList | In Wireless Sensor Networks (WSNs), the potential design challenge of energy efficiency is determined to be handled through the strategies of clustering and routing. The approaches of clustering and routing in WSNs pertain to the problems of Non-deterministic Polynomial (NP)-hard optimization. In this context, swarm intelligence-based algorithms are identified to be suitable and ideal for determining near-optimal and optimal solutions in the search space. On the other hand, APTEEN routing protocol possesses the issues that are related to unnecessary energy drain, ineffective overall network coverage and premature death of certain nodes. To address these issues, an attempt to optimize the APTEEN routing protocol using Dingo Optimization Algorithm (DOA) and Beluga Whale Optimization Algorithm (BWOA) is made in this proposed clustering protocol. With this motivation, Improved Dingo and Boosted Beluga Whale Optimization Algorithm (IDBBWOA) is proposed for determining the optimal cluster head and perform energy-efficient routing to minimized the energy consumption and maximize the lifetime of the network. It specifically used Improved Dingo Optimization Algorithm (IDOA) for attaining cluster head (CH) selection and energy efficient routing through the adoption fitness parameters of Residual Energy, Distance within and between Clusters, Network coverage, Node Degree for maximizing the rate of reliable data dissemination. It also incorporated Boosted Beluga Whale Optimization Algorithm (BBWOA) for determining the optimal points over the sink node can be moved to prevent multi-hop between CHs and the sink nodes, since it is essential for addressing the issue of hot-spot and extends the network lifetime. The simulation results of the proposed IDBBWOA approach revealed its efficacy in improving the mean throughput by 18.92 %, sustaining alive nodes by 34.28 %, and maintaining residual energy by 29.34 %, compared to the benchmarked approaches used for evaluation.
•In this paper, Improved Dingo and Boosted Beluga Whale Optimization Algorithm (IDBBWOA) is proposed for determining the optimal cluster head and perform energy-efficient routing to minimize the energy consumption and maximize the lifetime of the network.•It specifically used Improved Dingo Optimization Algorithm (IDOA) for attaining cluster head (CH) selection and energy efficient routing through the adoption fitness parameters of Residual Energy, Distance within and between Clusters, Network coverage, Node Degree for maximizing the rate of reliable data dissemination.•It also incorporated Boosted Beluga Whale Optimization Algorithm (BBWOA) for determining the optimal points over the sink node can be moved to prevent multi-hop between CHs and the sink nodes, since it is essential for addressing the issue of hot-spot and extends the network lifetime.•The simulation results of the proposed IDBBWOA approach revealed its efficacy in improving the mean throughput by 18.92 %, sustaining alive nodes by 34.28 %, and maintaining residual energy by 29.34 %, compared to the benchmarked approaches used for evaluation. |
| ArticleNumber | 101008 |
| Author | Gunasekaran, K. Verma, S. Kishore Dhurgadevi, M. Sahayaraj, J. Martin |
| Author_xml | – sequence: 1 givenname: J. Martin surname: Sahayaraj fullname: Sahayaraj, J. Martin email: josemartin2k6@gmail.com organization: Department of Electronics & Communication Engineering, Sri Indu College of Engineering and Technology, Hyderabad, Telangana, India – sequence: 2 givenname: K. surname: Gunasekaran fullname: Gunasekaran, K. email: drgunak@yahoo.com organization: Department of Data Science, Sri Indu College of Engineering and Technology, Hyderabad, Telangana, India – sequence: 3 givenname: S. Kishore surname: Verma fullname: Verma, S. Kishore email: kishore.saj3@gmail.com organization: Department of Computer Science & Engineering, Sri Indu College of Engineering and Technology, Hyderabad, Telangana, India – sequence: 4 givenname: M. surname: Dhurgadevi fullname: Dhurgadevi, M. email: devi.durga@gmail.com organization: Department of Computer Science & Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India |
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| Cites_doi | 10.1016/j.eswa.2021.115131 10.1109/JIOT.2017.2711248 10.1109/ACCESS.2019.2923882 10.1002/dac.4569 10.1007/978-981-16-7118-0_68 10.1007/978-981-19-8825-7_46 10.3390/su15075759 10.1007/978-981-99-3932-9_50 10.1016/j.eswa.2022.117334 10.1016/j.adhoc.2023.103110 10.1016/j.knosys.2022.109215 10.1007/s11063-022-10852-3 10.1093/comjnl/bxac002 10.1109/ACCESS.2019.2954182 10.1109/TNNLS.2017.2673241 10.1016/j.dsp.2022.103737 10.1049/cmu2.12072 10.1109/ACCESS.2022.3146374 10.1016/j.knosys.2018.08.003 10.1007/s12083-019-00758-8 10.1007/s11277-021-08875-5 10.3390/s100504521 10.1103/PhysRevE.49.4677 10.4108/eetsis.v10i3.2680 10.1002/dac.5482 10.1002/dac.5372 10.1016/B978-0-12-817133-2.00011-2 10.1109/TII.2021.3082576 10.1002/cpe.7585 10.3390/sym15020438 10.3390/app13052801 10.1108/SR-03-2021-0094 10.1007/s11277-022-10010-x 10.1007/s11277-021-08821-5 10.1080/09540091.2021.2004997 10.3390/computers12020035 10.3390/s23010231 10.1155/2021/9107547 10.1186/1687-1499-2012-83 10.3390/s22176405 10.1007/s11042-022-14261-5 10.1109/CIMCA.2005.1631345 10.1016/j.comnet.2020.107376 10.3390/s18113938 10.1002/cpe.2838 10.1007/s11276-018-1863-4 10.1007/s11276-019-02038-y |
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| Keywords | Wireless Sensor Networks (WSNs) Sink node mobility Network lifetime Cluster Head (CH) Clustering Boosted Beluga Whale Optimization Algorithm (BBWOA) Dingo Optimization Algorithm (IDOA) |
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