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Cluster Head Selection for Energy Balancing in Wireless Sensor Networks Using Modified Salp Swarm Optimization


Affiliations
1 Department of Electronics and Communication Engineering, University of Technology, Rajasthan, Jaipur, India
2 Department of Electronics and Communication Engineering, Raghu Engineering College (A), Visakhapatnam, Andhra Pradesh, India
 

In today’s realm, Wireless Sensor Network (WSN) has been emerged as a prominent research topic due to the advances in the design of small and low cost sensors for an extensive sort of applications. A battery powers the sensor nodes that make up the WSNs. The restricted quantity of electricity available within WSN nodes is considered as one of the important research issues. Researchers have offered a variety of proposals from various angles to maximize the use of energy resources. Clustering nodes has shown to be one of the most effective ways for WSNs to save energy. The traditional Salp Swarm Algorithm (SSA) has a slow convergence rate and local optima stagnation, and thus produces disappointing results on higher-dimensional issues. Convergence inefficiency is caused by SSA's lack of exploration and exploitation. Improvements to the original population update method are made in this study, and a Modified Salp Swarm Algorithm (MSSA) is provided for achieving energy stability and sustaining network life time through effective cluster head selection throughout the clustering process. Furthermore, the performance of MSSA is validated and equated to other start-of-the art optimization algorithms under different WSN deployments. The suggested model outperforms competing algorithms in terms of sustained operation time, longevity of the network, and total energy consumption, as shown by the simulation results.

Keywords

Wireless Sensor Network, Optimization, Clustering, SSA, LEACH, Network Life, Throughput
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  • H. Benton et al., “Design considerations for ultra-low energy wireless micro sensor nodes”, IEEE Transactions on Computers, vol. 54, p. 727–74, 2005, doi: https://doi.org/10.1109/TC.2005.98.

  • M. A. Yigitel, O. D. Incel and C. Ersoy, “QoS-aware MAC protocols for wireless sensor networks: A survey”, Computer Netwiorks, vol. 55 p.1982–2004. doi: https://doi.org/10.1016/j.comnet.2011.02.007

  • A.Alkhatib and G.S. Baicher, “MAC layer overview for wireless Sensornetworks”, in 2012 International Conference on Computer Networks and Communication Systems (CNCS), vol. 35, 2012, pp. 16–19.doi:https://www.researchgate.net/publication/227352894_MAC_Layer_Overview_for_Wireless_Sensor_Networks.

  • A. A. Abbasi and M. Younis,”A survey on clustering algorithms for wireless sensor networks”, Computer Communications,vol. 30, p. 2826-2841, 2007, doi: https://doi.org/10.1016/j.comcom.2007.05.024

  • W. R. Heinzelman, A. Chandrakasan and H. Balakrishnan, “Energy-efficient communication protocol for wireless microsensor networks”, In Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, 2000, pp. 1–10, doi:https://doi.org/10.1109/HICSS.2000.926982

  • W. R. Heinzelman, A. Chandrakasan and H. Balakrishnan, “An Application-Specific Protocol Architecture for Wireless Microsensor Networks”, IEEE Transactions on Wireless Communications., vol. 1, p .660-670, 2002, doi: https://doi.org/10.1109/TWC.2002.804190

  • G. G. Wang, S. Deb, and Z. Cui, “Monarch butterfly optimization,” Neural Computing and Applications, vol. 31, p. 1995–2014, 2019, doi: 10.1007/s00521-015-1923-y

  • G. G. Wang, S. Deb, X. Z. Gao, and L. D. S. Coelho, “A new metaheuristic optimisation algorithm motivated by elephant herding behaviour”, International Journal of Bio-Inspired Computation, vol. 8, p.394–409, 2016. doi:https://doi.org/10.1504/IJBIC.2016.

  • X. Jiang and S. Li, “BAS: beetle antennae search algorithm for optimization problems,” International Journal of Robotics andControl, vol. 1, p. 1–5, 2018, doi: https://doi.org/10.5430/ijrc.v1n1p1

  • A. Askarzadeh, “A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm,” Computers & Structures, vol. 169, pp. 1–12, 2016, doi: https://doi.org/10.1016/j.compstruc.2016.03.001

  • G. G. Wang, S. Deb, and L. D. S. Coelho, “Earthworm optimization algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems”, International Journal of Bio-Inspired Computation, vol. 12, p. 1–22, 2018, doi:https://doi/abs/10.1504/IJBIC.2018.093328

  • G.-G. Wang, “Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems,” Memetic Computing, vol. 10, p. 151–164, 2018, doi: http://dx.doi.org/10.1007%2Fs12293-016-0212-3.

  • N. M. A. Latice, C. C. Tsimenidis and B. S. Sharif, “Energy-Aware Clustering for Wireless Sensor Networks using Particle Swarm Optimization”,In Proceedings of the 2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications, 2007, pp. 1–5, doi: https://doi.org/10.1109/PIMRC.2007.4394521

  • D. G. Zhang, X. Wang, X, X. Song, T. Zhang and Y. Zhu, “A new clustering routing method based on PECE for WSN”, EURASIP Journal on Wireless Communications and Networking, vol. 2015, p.1-13, 2015, doi: https://doi.org/10.1186/s13638-015-0399-x

  • A. A. Salehpour, B. Mirmobin, A. A. Kusha and S. Mohammadi, “An energy efficient routing protocol for cluster-based wireless sensor networks using ant colony optimization”, In Proceedings of the 2008 International Conference on Innovations in Information Technology, 2008, pp. 455–459,doi:https://doi.org/10.1109/INNOVATIONS.2008.4781748

  • J. C. Bansal, H. Sharma, S. S. Jadon, and M. Clerc. “Spider monkey optimization algorithm for numerical Optimization”, Memetic Computing, vol. 6, p. 31-47, 2014,doi:http://dx.doi.org/10.1007%2Fs12293-013-0128-0.

  • T. Gui, C. Ma, F. Wang and D. E. Wilkins, “A novel cluster-based routing protocol wireless sensor networks using spider monkey optimization, in Industrial Electronics Society”, In Proceedings of the IECON 2016-42nd Annual Conference of the IEEE, 2016, pp. 5657–5662, doi:https://doi.org/10.1109/IECON.2016.7794106

  • P. Subramanian, J. M. Sahayaraj, S. Senthilkumar and D. S. Alex, “A hybrid grey wolf and crow search optimization algorithm-based optimal cluster head selection scheme for wireless sensor networks”, Wireless Personal Communications, vol. 113, p. 905–925, 2020http://doi.org/10.1007/s11277-020-07259-5.

  • A. Ahmed, J. Ali, A. Raza, and G. Abbas, “Wired vs wireless deployment support for wireless sensor networks,” in TENCON 2006 – 2006 IEEE Region 10 Conference, pp. 1–3, 2006, doi: https://doi.org/10.1109/TENCON.2006.343679.

  • N. Lavanya and T. Shankar, “Energy Efficient Cluster Head Selection using Hybrid Squirrel Harmony Search Algorithm in WSN” . (IJACSA) International Journal of Advanced Computer Science and Applications, Vol.10,p.477-487,2019,doi: https://dx.doi.org/10.14569/ijacsa.2019.0101265

  • P.C. S. Rao, P. K. Jana, and H. Banka, “A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks”. Wireless networks, vol. 23, p.2005-2020, 2017, doi: https://link.springer.com/article/10.1007/s11276-016-1270-7

  • P. S. Mann, and S. Singh, “Energy efficient clustering protocol based on improved metaheuristic in wireless sensor networks”. Journal of Network and Computer Applications, vol. 83, p.40-52, 2017, doi:http://dx.doi.org/10.1016/j.jnca.2017.01.031

  • T. S. Murugan and A. Sarkar,” Optimal cluster head selection by hybridisation of firefly and grey wolf optimisation”, International Journal of Wireless and Mobile Computing, Vol. 14, p.209-305, 2018, doi: http://dx.doi.org/10.1504/IJWMC.2018.10013576.

  • N. A. Al-Aboody and H. S. Al-Raweshidy, “Grey Wolf Optimization-Based Energy-Efficient Routing Protocol for Heterogeneous Wireless Sensor Networks”, 2016 4th International Symposium on Computational and Business Intelligence (ISCBI), 2016, pp.101-107,doi: https://doi.org/10.1109/ISCBI.2016.7743266.

  • T. Gui, M. Christopher, F. Wang, J. Li and D. E. Wilkins, “A Novel Cluster-based Routing Protocol Wireless Sensor Networks using Spider Monkey Optimization”. 42nd Annual Conference of the IEEE Industrial Electronicssociety, 2016, pp.5657–5662, doi:https://doi.org/10.1109/IECON.2016.7794106.

  • M. Razzaq, D. D. Ningombam and S. Shin, “Energy efficient K-means clustering-based routing protocol for WSN using optimal packet size”, 2018 International Conference on Information Networking (ICOIN),2018, pp. 632-635, doi:https://doi.ieeecomputersociety.org/10.1109/ICOIN.2018.834319

  • P. Kumari, M.P. Singh and P. Kumar, “Survey of clustering algorithms using fuzzy logic in wireless sensor network,” In 2013 International conference on energy efficient technologies for sustainability, 2013, pp. 924-928, doi: https://doi.org/10.1109/ICEETS.2013.6533511

  • N. P. R. Kumar and G. J. Bala, “A cognitive knowledged energy-efficient path selection using centroid and ant colony optimized hybrid protocol for WSN-assisted IoT”, Wireless Personal Communications, vol.124, p.1993-2028,2014,doi: https://doi.org/10.1007/s11277-021-09440-w

  • V. K. Arora, V. Sharma and M, Sachdeva, “ACO optimized self-organized tree - based energy balance algorithm for wireless sensor network”, Journal of Ambient Intelligence Humanized Computing, vol.10, p. 4963–4975, 2019, doi: http://doi.org/10.1007/s12652-019-01186-5.

  • G. Han and L. Zhang L, “WPO-EECRP: Energy-Efficient Clustering Routing Protocol Based on Weighting and Parameter Optimization in WSN”, Wireless Personal Communications, vol. 98, p.1171–1205, 2018, doi: https://link.springer.com/article/10.1007/s11277-017-4914-8

  • Z. Zou and Y.Qian, “Wireless sensor network routing method based on improved ant colony algorithm”, Journal of Ambient Intelligence Humanized and Computing, vol. 10, p.991–998, 2019, doi: https://link.springer.com/article/10.1007/s12652-018-0751-1.

  • A. Jamatia, K. Chakma, N. Kar, D. Rudrapal and D. Swapan D,“Performance Analysis of Hierarchical and Flat Network Routing Protocols in Wireless Sensor Network Using Ns-2”, International Journal ofModeling and Optimization., vol. 5, p.40-43,2015, doi: https://www.academia.edu/attachments/81910438.

  • A.Muqeet, L. Tianrui, Z. Khan , F. Khurshid and A. Mushtaq, “A Novel Connectivity-Based LEACH-MEEC Routing Protocol for Mobile Wireless Sensor Network”, Sensor Networks. Vol. 18, p. 4278-4299, doi: https://doi.org/10.3390/s18124278.

  • R. Sharma, V. Vashisht and U. Singh, “EEFCM-DE: Energy efficient clustering based on fuzzy C means and differential evolution algorithm in WSNs”, IET Communications, vol.13,p.996-1007,2019, doi: http://dx.doi.org/10.1049/iet-com.2018.5546

  • P. Kathiroli and K. Selvadurai, “Energy efficient cluster head selection using improved Sparrow Search Algorithm in Wireless Sensor Networks”, Journal of King Saud University - Computer and Information Sciences, vol. 33, p;1-12, 2021 , doi: https://doi.org/10.1016/j.jksuci.2021.08.031

  • K. N. Dattatrayaand K. RaghavaRao, “Hybrid based cluster head selection for maximizing network lifetime and energy efficiency in WSN”,Journal of King Saud University – Computer and Information Sciences, vol. 34, p:716–726, 2023, doi: https://doi.org/10.1016/j.jksuci.2019.04.003.

  • P. Rajpoot and P. Dwivedi, “Optimized and load balanced clustering for wireless sensor networks to increase the lifetime of WSN using MADM approaches”,Wireless Networks, Vol. 26, p.215-251, 2020, doi: https://link.springer.com/article/10.1007/s11276-018-1812-2.

  • R. P. Kumar, J. S. Rajand S.Smys, “ Performance Analysis of Hybrid Optimization Algorithm for Virtual Head Selection in Wireless Sensor Networks”, Wireless-Personal-Communications, vol. 123, p. 1-16, 2022, doi: https://link.springer.com/article/10.1007%2Fs11277-021-09222-4.

  • M. Santhosh and P. Sudhakar , :Fast Convergence Grasshopper Optimization Algorithm based Clustering with Multihop Routing Protocol for Wireless Sensor Networks.” International Journal of Advanced Science and Technology, vol. 29, p.11254 – 11270, 2020, doi: http://sersc.org/journals/index.php/IJAST/issue/view/263.

  • Z. Qi-Ye, S. Ze-Ming and Z. Feng (2014), “ A clustering routing protocol for wireless sensor networks based on type-2 fuzzy logic and ACO”, in 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) 2014 , pp. 1060–1067, doi: https://doi.org/10.1109/FUZZ-IEEE.2014.6891584

  • W. Heinzelman, A. Chandrakasan and H. Balakrishna, "Energy-Efficient Communication Protocols for Wireless Microsensor Networks", Proceedings of the 33rd Hawaaian International Conference on Systems Science (HICSS),2000,pp. 1-10.

  • N. A. Al-Aboody and H. S. Al-Raweshidy, “Grey wolf optimization-based energy-efficient routing protocol for heterogeneous wireless sensor networks”, In 4th International Symposium on Computational and Business Intelligence (ISCBI), 2016, pp. 101-107, doi: https://doi.org/10.1109/ISCBI.2016.7743266.

  • L. P. Madin, “Aspects of jet propulsion in Salps”, Canadian Journal of Zoology, vol. 68, p. 765–777, 1990. http://dx.doi.org/10.1139/z90-111

  • S. Mirjalili, A. H. Gandomi, S. Z. Mirjalili, S. Saremi, H. Faris, and S. M. Mirjalili, “Salp swarm algorithm: a bio-inspired optimizer for engineering design problems,” Advances inEngineering Software, vol. 114, pp. 163–191, 2017, doi: https://doi.org/10.1016/j.advengsoft.2017.07.002

  • H. T. Ibrahim, W. J. Mazher, O. N. Ucan, and O. Bayat, “Feature selection using salp swarm algorithm for real biomedical datasets”, (IJCSNS) International Journal of Computer Science and Network Security, vol. 17, p. 13–20, 2017, doi: https://hdl.handle.net/20.500.12939/913.


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  • Cluster Head Selection for Energy Balancing in Wireless Sensor Networks Using Modified Salp Swarm Optimization

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Authors

G. Sunil Kumar
Department of Electronics and Communication Engineering, University of Technology, Rajasthan, Jaipur, India
Gupteswar Sahu
Department of Electronics and Communication Engineering, Raghu Engineering College (A), Visakhapatnam, Andhra Pradesh, India
Mayank Mathur
Department of Electronics and Communication Engineering, University of Technology, Rajasthan, Jaipur, India

Abstract


In today’s realm, Wireless Sensor Network (WSN) has been emerged as a prominent research topic due to the advances in the design of small and low cost sensors for an extensive sort of applications. A battery powers the sensor nodes that make up the WSNs. The restricted quantity of electricity available within WSN nodes is considered as one of the important research issues. Researchers have offered a variety of proposals from various angles to maximize the use of energy resources. Clustering nodes has shown to be one of the most effective ways for WSNs to save energy. The traditional Salp Swarm Algorithm (SSA) has a slow convergence rate and local optima stagnation, and thus produces disappointing results on higher-dimensional issues. Convergence inefficiency is caused by SSA's lack of exploration and exploitation. Improvements to the original population update method are made in this study, and a Modified Salp Swarm Algorithm (MSSA) is provided for achieving energy stability and sustaining network life time through effective cluster head selection throughout the clustering process. Furthermore, the performance of MSSA is validated and equated to other start-of-the art optimization algorithms under different WSN deployments. The suggested model outperforms competing algorithms in terms of sustained operation time, longevity of the network, and total energy consumption, as shown by the simulation results.

Keywords


Wireless Sensor Network, Optimization, Clustering, SSA, LEACH, Network Life, Throughput

References





DOI: https://doi.org/10.22247/ijcna%2F2023%2F218508