International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

MENU
International Journal of Computer Networks and Applications (IJCNA)

International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

Energy Efficient Cluster Formation and Multihop Routing Based on Improved Harmony Search Algorithm for Wireless Sensor Networks

Author NameAuthor Details

G V Sowmya, R Aparna

G V Sowmya[1]

R Aparna[2]

[1]Department of Information Science and Engineering, Jawaharlal Nehru New College of Engineering (Visvesveraya Technological University, Belagavi), Shimoga, Karnataka, India

[2]Department of Information Science and Engineering, Siddaganga Institute of Technology (Visvesveraya Technological University, Belagavi), Tumakuru, Karnataka, India

Cite this Article

DOI: 10.22247/ijcna/2023/223419

Pages : 706-727

 Download PDF

1092

Views

Abstract

Energy efficiency plays a crucial role in extending the operational lifespan of Wireless Sensor Networks (WSNs). It stands as the foremost objective for any routing algorithm designed for WSNs. This study centers on enhancing communication efficiency through a multihop approach guided by the Harmony Search Algorithm (HSA). The process incorporates Cluster Head (CH) selection through the utilization of the HSA and by assessing the quality of the communication channel. There are instances where a channel possesses high capacity, yet it transmits minimal data, leading to resource underutilization. Therefore, if the communication channel’s quality is pre-determined, then algorithms can be developed to establish an upper limit for channel usage, ensuring congestion free and error free maximum data transmission. In the proposed methodology, parameters such as residual energy, distance and node degree were taken into account for CH selection. Subsequently, clusters were formed based on Shannon Channel Capacity ‘C’ and path loss model. Following the CH selection and cluster formation, a communication was established using HSA. A comparative analysis was conducted on network life span, packets sent to Base Station (BS) and energy utilization for the three algorithms, Energy Efficient Harmony Search Based Routing (EEHSBR), Clustering and Routing in wireless sensor networks using Harmony Search Algorithm (CRHS), and Robust Harmony Search Algorithm based clustering protocol for wireless sensor networks (RHSA).

Index Terms

Wireless Sensor Network

Harmony Search Algorithm

Shannon Channel Capacity ‘C’

Path Loss model

Cluster Head

Harmony Memory

Reference

  1. 1.
    Semwal, V.B., Mondal, K. & Nandi, and G.C. “Robust and accurate feature selection for humanoid push Recovery and classification: deep learning approach”. Neural Comput&Applic 28, 565-574(2017).https://doi.org/10.1007/s00521-015-2089-3.
  2. 2.
    Gupta, V.; Pandey, R. An improved energy aware distributed unequal clustering protocol for heterogeneous wireless sensor networks. Eng. Sci. Technol. Int. J. 2016, 19, 1050–1058.
  3. 3.
    K. Li, W. Ni, L. Duan, M. Abolhasan and J. Niu, "Wireless Power Transfer and Data Collection in WirelessSensor Networks," in IEEE Transactions on Vehicular Technology, vol. 67, no. 3, pp. 2686-2697, March 2018, doi: 10.1109/TVT.2017.2772895.
  4. 4.
    Zhang P, Xiao G, Tan H-P (2013) Clustering algorithms for maximizing the lifetime of wireless sensor networks with energy harvesting sensors. ComputNetw 57(14):2689–2704.
  5. 5.
    Jiang Wu, Xuefeng Ding, "Using Wireless Sensor Network to Remote Real-Time Monitoring and Tracking of Logistics Status Based on Difference Transmission Algorithm", Journal of Sensors, vol. 2021, Article ID4084288, 10 pages, 2021.
  6. 6.
    Sohraby, K., Minoli, D., &Znati, T. (2007). Wireless sensor networks: Technology, protocols, and applications. New York: Wiley-Interscience.
  7. 7.
    Agarwal PK, Procopiuc CM (2002) Exact and approximation algorithms for clustering. Algorithmica 33(2):201–226.
  8. 8.
    FaizanUllah M, Imtiaz J, Maqbool KQ. Enhanced Three Layer Hybrid Clustering Mechanism for EnergyEfficient Routing in IoT. Sensors (Basel). 2019 Feb 18;19(4):829. doi: 10.3390/s19040829. PMID: 30781595; PMCID: PMC6413009.
  9. 9.
    Jacques Bahi, et al.,Efficient distributed lifetime optimization algorithm for sensor networks, Elsevier Journal of Ad Hoc Networks (16) (2014) 1-12.
  10. 10.
    Keontaek Lee, et al., Satisfying the target network lifetime in wireless sensor networks, Elsevier Journal of Computer Networks (65) (2014) 41-45.
  11. 11.
    Tarach and Amgoth, et al., Energy-aware routing algorithm for wireless sensor networks, Elsevier Journal of Computers and Electrical Engineering (41) (2015) 357367.
  12. 12.
    Zhengmao Ye, et al., Adaptive Clustering Based Dynamic Routing of Wireless Sensor Networks viaGeneralized Ant Colony Optimization International Conference on Future Information Engineering (10), (2014)2-10.
  13. 13.
    M. EmreKeskin, et al., Wireless sensor network lifetime maximization by optimal sensor deployment, activity scheduling, data routing and sink mobility, Elsevier Journal of Ad Hoc Networks (17) (2014) 18-36.
  14. 14.
    Yang, XS. (2009). Harmony Search as a Metaheuristic Algorithm. In: Geem, Z.W. (eds) Music-Inspired Harmony Search Algorithm. Studies in Computational Intelligence, vol 191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00185-7.
  15. 15.
    D. C. Hoang, P. Yadav, R. Kumar and S. K. Panda, "A Robust Harmony Search Algorithm Based ClusteringProtocol for Wireless Sensor Networks," 2010 IEEE International Conference on Communications Workshops, 2010, pp. 1-5, doi: 10.1109/ICCW.2010.5503895.
  16. 16.
    Buddha Singh, et al., A novel energy- aware cluster head selection based on PSO for WSN, Human centric computing and information sciences, (2012) 1-18.
  17. 17.
    David E. Goldberg, Genetic Algorithms in search, Optimization, and Machine Learning, Preason Education, 9th Edition, 2005.
  18. 18.
    Bongale, A.; Bongale, A.; Kumar, S.; Joshi, R.; Bhamidipati, K. Design and Implementation of EOICHD Based Clustered Routing Protocol Variants for Wireless Sensor Networks. Appl. Syst. Innov. 2021, 4, 25.
  19. 19.
    Heinzelman, W. R., Chandrakasan, A., Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Hawaii international conference on system sciences, HICSS ’00, (vol. 8, pp. 8020). Washington, DC: IEEE Computer Society.
  20. 20.
    Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor Networks. IEEE Transactions on Wireless Communications, 1(4),660– 670.
  21. 21.
    Anupkumar M. Bongale, C. R. Nirmala, and Arunkumar M. Bongale. 2019. Hybrid Cluster Head Election for WSN Based on Firefly and Harmony Search Algorithms, Wirel. Pers. Commun., 106, 2 (May 2019), 75–306. DOI:https://doi.org/10.1007/s11277-018-5780-8.
  22. 22.
    T. Shankar, S. Shanmugavel, A. Rajesh, Hybrid HSA and PSO algorithm for energy efficient cluster head Selection in wireless sensor networks, Swarm and Evolutionary Computation, Volume 30, 2016,Pages1-10,ISSN 22106502, http//doi org/10.1016/j.awevo.2016.03.003.
  23. 23.
    Praveen Lalwani, Sagnik Das, Haider Banka, and Chiranjeev Kumar. 2018. CRHS: clustering and routing in wireless sensor networks using harmony search algorithm. Neural Comput. Appl. 30, 2 (July2018), 639–659. DOI:https://doi.org/10.1007/s00521-016-2662-4.
  24. 24.
    B. Zeng and Y. Dong, "An Energy Efficient Harmony Search Based Routing Algorithm for Small-ScaleWireless Sensor Networks," 2014 IEEE 17th International Conference on Computational Science and Engineering, 2014, pp. 362-367, doi: 10.1109/CSE.2014.94.
  25. 25.
    T. Camilo, C. Carreto, J. Silva, F. Boavida, An Energy-Efficient Ant- Based Routing Algorithm for Wireless Sensor Networks, in: M.Dorigo, L. Gambardella, M. Birattari, A. Martinoli, R. Poli, T.Stützle (Eds.) Ant Colony Optimization and Swarm Intelligence, Springer Berlin Heidelberg, 2006, pp. 49-59.
  26. 26.
    Geem, Zong Woo. (2009). Music-Inspired Harmony Search Algorithm:Theory and Applications.10.1007/978-3-642-00185-7.
  27. 27.
    K.S. Lee, Z.W. Geem, A new meta-heuristic algorithm for continues engineering optimization: harmony search theory and practice, Comput. Meth.Appl.Mech.Eng.194, pp.3902-3933, 2004.
  28. 28.
    Bongale, A. M., &Nirmala, C. R. (2016). Eoichd: A routing scheme for wireless sensor network basedon energy and optimal inter cluster Head distance. International Journal of Applied Engineering Research, 11(11), 7256–7266.
  29. 29.
    Z.W. Geem, J.H. Kim, G. Loganathan, A new heuristic optimization algorithm: harmony search, February 2001 SIMULATION: Transactions of the society for modeling and simulation international 76(2):60-68 DOI: 10.1177/003754970107600201.
  30. 30.
    B. Jan, H. Farman, H. Javed, B. Montrucchio, M. Khan, and Sh. Ali, “Energy efficient hierarchical clusteringapproaches in wireless sensor networks: A survey,” Wireless Commun. Mobile Comput. 2017, 6457942 (2017).https://doi.org/10.1155/2017/6457942.
  31. 31.
    Sowmya, G.V., Kiran, M. Improved Harmony Search Algorithm for Multihop Routing in Wireless Sensor Networks. J. Comput. Syst. Sci. Int. 61, 1058–1075 (2022).
  32. 32.
    Rani, R. U. ., Rao, P. S. ., Lavanaya, K. ., Satyanarayana, N. ., Lallitha, S. ., & Prasad J, P. . (2023). Optimization of Energy- Efficient custer Head Selection Algorithm for Internet of Things in Wireless Sensor Networks. International Journal on Recent and Innovation Trends in Computing and Communication, 11(4), 238–248.
  33. 33.
    Wu, Z., Wan, G. An enhanced ACO-based mobile sink path determination for data gathering in wireless sensor networks. J Wireless Com Network 2022, 100 (2022).
  34. 34.
    Y. Liu, D. Jiang, B. Tao, J. Qi, G. Jiang, J. Yun, L. Huang, X. Tong, B. Chen, G. Li, Grasping posture of humanoid manipulator based on target shape analysis and force closure. Alex. Eng. J. 61(5), 3959–3969 (2022).
  35. 35.
    S. Gao, H. Zhang, S.K. Das, Efficient data collection in wireless sensor networks with path-constrained mobile sinks. IEEE Trans. Mob. Comput.10 (4), 592–608 (2011).
  36. 36.
    Jatinder Pal Singh and Anuj Kumar Gupta. An Optimized Routing Technique in Wireless Sensor Network Using Aquila Optimizer. International Journal of Intelligent Engineering and Systems, Vol.15, No.4, 2022. DOI: 10.22266/ijies2022.0831.28.
  37. 37.
    Tyagi, L. K. ., & Kumar, A..(2023). A Hybrid Trust Based WSN protocol to Enhance Network Performance using Fuzzy Enabled Machine Learning Technique. International Journal of Intelligent Systems and Applications in Engineering, 11(9s), 131–144.
  38. 38.
    M. S. Muthukkumar, S. Diwakaran, "Efficient Load Balancing in WSN Using Quasi –oppositional Based Jaya Optimization with Cluster Head Selection", International Journal of Computer Network and Information Security(IJCNIS), Vol.15, No.2, pp.85-96, 2023. DOI:10.5815/ijcnis.2023.02.07.
SCOPUS
SCImago Journal & Country Rank

Announcements

Archives

Downloads

Contact Us

Dr.M. Milton Joe
EverScience Publications,
Nagercoil, Tamilnadu,India

Talk to Us

+91 9442777224

Email Us

editor@ijcna.org

info@ijcna.org

info@everscience.org

Quick Links

Follow Us

Copyright © 2023; EverScience Publications

Designed & Maintained By Shiro Software Solutions