HDOMLM: Hybrid Dual Optimized Machine Learning Model for Cluster Head Selection in MANET

Nirmala Bai K.S; Subramanyam M.V

doi:10.54392/irjmt25314

Authors

Nirmala Bai K.S JNTUACEK, Kalikiri, Annamayya, Andhra Pradesh, India Author https://orcid.org/0009-0004-3742-4437
Subramanyam M.V Santhiram Engineering College, Nandyal, Andhra Pradesh, India Author

DOI:

https://doi.org/10.54392/irjmt25314

Keywords:

MANET, Clustering, Network Lifetime, Energy Efficiency, Transmission Cost, PSO Optimization, Machine Learning, Cluster Head Selection

Abstract

The selection of cluster heads with efficient energy awareness is a crucial concern in mobile ad hoc networks (MANETs). MANETs' energy efficiency is enhanced when a suitable cluster head (CH) is selected. Clusters are established in Mobile Ad hoc Networks (MANETs) to enhance communication among nodes. Efficient clustering is necessary to provide the rapid and precise transmission of information in the middle of nodes. In this work, we proposed a hybrid dual optimization of the machine learning model (HDOMLM). In this protocol, we are performing two different optimizations for clustering. Initial clustering is performed using Particle Swarm Optimization (PSO) based on node location and mobility as objection function cost. Once the initial cluster is formed, the selection metric is evaluated from the available network topology for the execution period. Delta difference, average distance, related energy, related mobility, and transmission delay are the five selection metrics used as features in the Optimized Machine Learning Model (O-MLM). By using O-MLM, we can classify that the node belongs to a normal node or cluster head node. To evaluate the effectiveness of our proposed HDOMLM, we are performing the simulation in MATLAB tool. Different performance metrics such as Alive nodes, Residual energy, Energy Tax, Average End-to-End Delay, Number of Successful packets received as BS, and Total processing time. The objective of network lifetime improvement is achieved in the proposed HDOMLM by 200 rounds and residual energy of 56% increased than earlier works.

References

R. Sarumathi, V. Jayalakshmi, (2022) Study of clustering schemes in mobile ad hoc networks. In 2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS), IEEE, India. https://doi.org/10.1109/ICICCS53718.2022.9788134

S. Sanshi, N. Karthik, R. Vatambeti, IoT energy efficiency routing protocol using FHO‐based clustering and improved CSO model‐based routing in MANET. International Journal of Communication Systems, 37(9), (2024) e5756. https://doi.org/10.1002/dac.5756

W. Bednarczyk, P.Gajewski, An enhanced algorithm for MANET clustering based on weighted parameters. Universal Journal of Communications and Network, 1(3), (2013) 88-94. https://doi.org/10.13189/ujcn.2013.010302

Shatendra Dubey, Anurag Shrivastava, Neerja Dubey, Enhanced Cluster Based Routing Protocol for MANET. International Journal of Advances in Engineering and Management, 3(1), (2021) 775-780.

V. Gayatri, M. Kumaran, Energy Efficient Cluster based Multipath Routing Protocol in MANET using Genetic Particle Swarm Optimization Algorithm. International Journal of Intelligent Systems and Applications in Engineering, 10(4), (2022) 231–239.

S. Ramesh, S. Smys, A software-based heuristic clustered (sbhc) architecture for the performance improvement in MANET. Wireless Personal Communications, 97(4), (2017) 6343-6355. https://doi.org/10.1007/s11277-017-4841-8

F. Hamza, S.M.C. Vigila, Cluster head selection algorithm for MANETs using hybrid particle swarm optimization-genetic algorithm. International Journal of Computer Networks and Applications, 8(2), (2021) 119-129. https://doi.org/10.22247/ijcna/2021/208892

R. Suresh Kumar, P. Manimegalai, P.T. Vasanth Raj, R. Dhanagopal, A. Johnson Santhosh, Cluster Head Selection and Energy Efficient Multicast Routing Protocol-Based Optimal Route Selection for Mobile Ad Hoc Networks, Wireless Communications and Mobile Computing, 12 (2022) 5318136. https://doi.org/10.1155/2022/5318136

S. Vemuri, S. Mirkar, (2021) A Performance Comparison of MANET Routing Protocols. Innovations in Power and Advanced Computing Technologies (i-PACT), IEEE, Malaysia. https://doi.org/10.1109/i-PACT52855.2021.9696785

T.A. Ramya, J.M. Mathana, R. Nirmala, R. Gomathi, Exploration on enhanced Quality of Services for MANET through modified Lumer and Fai-eta algorithm with modified AODV and DSR protocol. Materials Today: Proceedings, 80, (2023) 1765-1771. https://doi.org/10.1016/j.matpr.2021.05.601

V. Gayatri, M.S. Kumaran, Energy Efficient Cluster based Multipath Routing Protocol in MANET using Genetic Particle Swarm Optimization Algorithm. International Journal of Intelligent Systems and Applications in Engineering, 10(4), (2022) 231-239.

X. Chen, G. Sun, T. Wu, L. Liu, H. Yu, M. Guizani, RANCE: A randomly centralized and on-demand clustering protocol for mobile ad hoc networks. IEEE Internet of Things Journal, 9(23), (2022) 23639-23658. https://doi.org/10.1109/JIOT.2022.3188679

P. Kamboj, S. Pal, S. Bera, S. Misra, QoS-Aware Multipath Routing in Software-Defined Networks. IEEE Transactions on Network Science and Engineering, 10(2), (2023) 723-732. https://doi.org/10.1109/TNSE.2022.3219417

O. Lemeshko, O. Yeremenko, M. Yevdokymenko, V. Lemeshko, M. Persikov, (2023) QoS-Aware Adaptation Traffic Engineering Solution for Multipath Routing in Communication Network. Information and Communication Technologies and Sustainable Development. ICT&SD 2022. Lecture Notes in Networks and Systems, Springer. https://doi.org/10.1007/978-3-031-46880-3_9

V. Sehrawat, S. Goyal S, NaISEP: Neighborhood Aware Clustering Protocol for WSN Assisted IOT Network for Agricultural Application. Wireless Personal Communications: An International Journal. 130(1), (2023) 347-362. https://doi.org/10.1007/s11277-023-10288-5

T. Alam, B. Rababah, Convergence of MANET in Communication among Smart Devices in IoT. International Journal of Wireless and Microwave Technologies, 9(2), (2019) 1–10. https://doi.org/10.5815/ijwmt.2019.02.01

R. Vatambeti, S. Sanshi, D.P. Krishna, An efficient clustering approach for optimized path selection and route maintenance in mobile ad hoc networks. Journal of Ambient Intelligence and Humanized Computing, 14(1), (2023) 305-319. https://doi.org/10.1007/s12652-021-03298-3

T. Alam, Efficient and Secure Data Transmission Approach in Cloud- MANET-IoT Integrated Framework, Journal of Telecommunication, Electronic and Computer Engineering, 12(1), (2020) 33–38. https://dx.doi.org/10.2139/ssrn.3639058

M.B. Dsouza, D.H. Manjaiah, (2020) Energy and Congestion Aware Simple Ant Routing Algorithm for MANET, 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India. https://doi.org/10.1109/ICECA49313.2020.9297470

H. Ziani, N. Enneya, J. A. Chentoufi, J. Laassiri, Mobility Condition to Study Performance of MANET Routing Protocols. In Emerging Technologies for Connected Internet of Vehicles and Intelligent Transportation System Networks, 242, (2020) 73–82. https://doi.org/10.1007/978-3-030-22773-9_6

B. U. I. Khan, F. Anwar, R. F. Olanrewaju, B. R. Pampori, R.N. Mir, A Game Theory-Based Strategic Approach to Ensure Reliable Data Transmission with Optimized Network Operations in Futuristic Mobile Adhoc Networks, IEEE Access, 8, (2020) 124097–124109. https://doi.org/10.1109/ACCESS.2020.3006043

M. Ahmad, A. Hameed, F. Ullah, I. Wahid, S.U. Rehman, H. A. Khattak, A bio-inspired clustering in mobile ad-hoc networks for internet of things based on honeybee and genetic algorithm, Journal of Ambient Intelligence and Humanized Computing, 11(11), (2020) 4347–4361. https://doi.org/10.1007/s12652-018-1141-4

S. Amutha, B. Kannan, M. Kanagaraj, Energy‐efficient cluster manager‐based cluster head selection technique for communication networks. International Journal of Communication Systems,34(5), (2021) e4741. https://doi.org/10.1002/dac.4741

A.S. Mohammed, S. Basha, P.N. Asha, K. Venkatachalam, FCO—fuzzy constraints applied cluster optimization technique for wireless adhoc networks. Computer Communications, 154, (2020) 501-508. https://doi.org/10.1016/j.comcom.2020.02.079

V. Quy, P. Chuan, V. Nam, D. Linh, N. Ban, N.A.Han, A High-Performance Routing Protocol Based on Mobile Agent for Mobile Ad Hoc Networks. International Journal of Interactive Mobile Technologies, 15(3), (2021) 30-42. https://doi.org/10.3991/ijim.v15i03.13007

A. Kirimtat, O. Krejcar, A. Kertesz, M.F. Tasgetiren, Future Trends and Current State of Smart City Concepts: A Survey. IEEE Access, 8, (2020) 86448-86467. https://doi.org/10.1109/ACCESS.2020.2992441

D. Ma, G. Lan, M. Hassan, W. Hu, S.K. Das, Sensing, Computing, and Communications for Energy Harvesting IoTs: A Survey. IEEE Communications Surveys & Tutorials, 22(2), (2020), 1222-1250. https://doi.org/10.1109/COMST.2019.2962526

A. Bhardwaj, H. El-Ocla, Multipath routing protocol using genetic algorithm in mobile ad hoc networks. IEEE Access, 8, (2020) 177534-177548. https://doi.org/10.1109/ACCESS.2020.3027043

I.U. Khan, I.M. Qureshi, M.A. Aziz, T.A. Cheema, S.B.H. Shah, Smart IoT control-based nature inspired energy efficient routing protocol for flying ad hoc network (FANET). IEEE Access, 8, (2020) 56371-56378. https://doi.org/10.1109/ACCESS.2020.2981531

Shiliang Sun, Zehui Cao, A Survey of Optimization Methods from a Machine Learning Perspectiv”, arXiv:1906.06821, 2021

B. Silpa, and M.K. Hota, OVME-REG: Harris hawks optimization algorithm based optimized variational mode extraction for eye blink artifact removal from EEG signal, Medical & Biological Engineering & Computing, 62 (2024) 955–972. https://doi.org/10.1007/s11517-023-02976-y

K. Radhika, Y. Murali Mohan Babu, J.K. Periasamy, and T.R. Saravanan, Service Oriented Virtual Machine for Maximising Quality of Service in Wireless Networks, Journal of Physics: Conference Series, 1964 (2021) 1-7. https://doi.org/10.1088/1742-6596/1964/4/042086

S. Secherla (2021) Understanding Optimization Algorithms in Machine Learning. Towards Data Science, Available at: https://towardsdatascience.com/understanding-optimization-algorithms-in-machine-learning-edfdb4df766b

N. Mahendran, Z. Wang, F. Hamze, N. De Freitas, (2012) Adaptive MCMC with Bayesian optimization. In Artificial Intelligence and Statistics, PMLR.

J. Bergstra, Y. Bengio, Random search for hyper-parameter optimization. The journal of machine learning research, 13(1), (2012) 281-305.

L.V. Rajathi, An advancement in energy efficient clustering algorithm using cluster coordinator-based CH election mechanism (CCCH). Measurement: Sensors, 25, (2023) 100623. https://doi.org/10.1016/j.measen.2022.100623