International Journal of Computer Networks and Applications

Open Access Open Access  Restricted Access Subscription Access

Cluster Head Selection Algorithm for MANETs Using Hybrid Particle Swarm Optimization-Genetic Algorithm


Affiliations
1 Department of Computer Applications, Noorul Islam Centre for Higher Education, Kumarakovil, Tamil Nadu, India
2 Department of Information Technology, Noorul Islam Centre for Higher Education, Kumarakovil, Tamil Nadu, India
 

The Mobile Ad-hoc Network (MANET) is a decentralized system that consists of mobile nodes. Wireless connections are used to connect these nodes. The primary issues of concern of MANETs are mobility and limited battery lifetime. Advanced techniques for improving MANET energy efficiency and extending network lifespan are critical. Clustering is one of the tried-and-true methods for increasing network lifetime by lowering and balancing energy consumption. Choosing a suitable cluster head from the cluster improves the network’s energy efficiency even further. Because of the additional workloads, the cluster heads (CHs) utilize more energy than non-cluster heads. A novel algorithm for CH selection with a Hybrid Particle Swarm Optimization-Genetic Algorithm (PSO-GA) is proposed to improve the MANET network’s energy efficiency and lifetime. The proposed method is implemented using the NS-2 platform for the analysis. The proposed model outperforms the existing OSCA, EP-MBO, GBTC, SM-WCA, CM-BCA, and FCO methods in terms of network performance. The model’s performance has achieved a low Bit Error Rate (BER) of 7% for 100 nodes with 99.38% Packet Delivery Ratio (PDR) with minimized delay in the range 2.01sec with the energy efficiency of 99.03%. The validation indicates that the Hybrid PSO-GA approach is more efficient than the other methods.

Keywords

Mobile Ad-hoc Network, Clustering, Soft k-Means, Cluster Head Selection, PSO-GA Optimization Algorithm.
User
Notifications
Font Size

  • N. Raza, M. Umar Aftab, M. Qasim Akbar, O. Ashraf, and M. Irfan, "Mobile Ad-Hoc Networks Applications and Its Challenges,"Communications and Network, vol. 08, no. 03, pp. 131–136, 2016, doi: 10.4236/cn.2016.83013.

  • A. Khelil, C. Becker, J. Tian, and K. Rothermel, "An epidemic model for information diffusion in MANETs," In Proceedings of the International Workshop on Modeling, Analysis and Simulation of Wireless and Mobile Systems, pp. 54–60, 2002, doi:10.1145/570758.570768.

  • I. Ahmad, U. Ashraf, and A. Ghafoor, "A comparative QoS survey of mobile ad hoc network routing protocols," Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers, Series A, vol. 39, no. 5, pp. 585–592, 2016, doi: 10.1080/02533839.2016.1146088.

  • T. Alam and B. Rababah, "Convergence of MANET in Communication among Smart Devices in IoT," International Journal of Wireless and Microwave Technologies, vol. 9, no. 2, pp. 1–10, Mar. 2019, doi: 10.5815/ijwmt.2019.02.01.

  • T. Alam, "Efficient and Secure Data Transmission Approach in Cloud-MANET-IoT Integrated Framework," Journal of Telecommunication, Electronic and Computer Engineering, vol. 12, no. 1, pp. 33–38, Mar. 2020, doi: 10.2139/ssrn.3639058.

  • W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, "Edge Computing: Vision and Challenges," IEEE Internet of Things Journal, vol. 3, no. 5, pp.637–646, Oct. 2016, doi: 10.1109/jiot.2016.2579198.

  • M. B. Dsouza and D. H. Manjaiah, "Energy and Congestion Aware Simple Ant Routing Algorithm for MANET," 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, pp. 744–748, Nov. 2020, doi: 10.1109/iceca49313.2020.9297470.

  • S. Sharma, S. Rimt, and A. Kumar Gupta, "A Comprehensive Review of Security Issues in Manets," International Journal of Computer Applications, vol. 69, no. 21, pp. 975–8887, 2013, doi: 10.5120/12097-8277.

  • M. Ichaba, "Security Threats and Solutions in Mobile Ad Hoc Networks; A Review," Universal Journal of Communications and Network, vol. 6, no. 2, pp. 7–17, 2018, doi: 10.13189/ujcn.2018.060201.

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

  • B. U. I. Khan, F. Anwar, R. F. Olanrewaju, B. R. Pampori, and 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, vol. 8, pp. 124097–124109, 2020. doi: 10.1109/access.2020.3006043.

  • M. Gavhale and P. D. Saraf, "Survey on Algorithms for Efficient Cluster Formation and Cluster Head Selection in MANET," Physics Procedia, vol. 78, no. December 2015, pp. 477–482, 2016, doi: 10.1016/j.procs.2016.02.091.

  • A. Roy, M. Hazarika, and M. K. Debbarma, "Energy Efficient Cluster based routing in MANET." In 2012 International Conference on Communication, Information & Computing Technology (ICCICT), pp.1-5. IEEE, 2012, doi: 10.1109/iccict.2012.6398228.

  • K. Gomathi and B. Parvathavarthini, "A Secure Clustering in MANET through Direct Trust Evaluation Technique," In 2015 International Conference on Cloud Computing (ICCC), Riyadh-Saudi Arabia, pp. 1-6. IEEE, 2015, doi: 10.1109/cloudcomp.2015.7149624.

  • S. A. Sharifi and S. M. Babamir, "The clustering algorithm for efficient energy management in mobile ad-hoc networks," Computer Networks, vol. 166, p. 106983, Jan. 2020, doi: 10.1016/j.comnet.2019.106983.

  • F. Hamza and S. Maria Celestin Vigila, "Review of machine learning-based intrusion detection techniques for MANETs," Lecture Notes in Networks and Systems, vol. 75, pp. 367–374, 2019, doi: 10.1007/978-981-13-7150-9_39.

  • A. Choukri, A. Habbani, and M. el Koutbi, "An energy efficient clustering algorithm for MANETs, " In International Conference on Multimedia Computing and Systems -Proceedings, pp. 819–824, Sep. 2014, doi: 10.1109/icmcs.2014.6911232.

  • H. Safa, O. Mirza, and H. Artail, "A dynamic energy efficient clustering algorithm for MANETs," In Proceedings - 4th IEEE International Conference on Wireless and Mobile Computing, Networking and Communication, pp. 51–56, 2008, doi:10.1109/WiMob.2008.67.

  • H. Ali, W. Shahzad, and F. A. Khan, "Energy-efficient clustering in mobile ad-hoc networks using multi-objective particle swarm optimization," Applied Soft Computing Journal, vol. 12, no. 7, pp. 1913–1928, Jul. 2012, doi: 10.1016/j.asoc.2011.05.036.

  • S. Z. H. Zahidi, F. Aloul, A. Sagahyroon, and W. El-Hajj, "Optimizing complex cluster formation in MANETs using SAT/ILP techniques," IEEE Sensors Journal, vol. 13, no. 6, pp. 2400–2412, 2013, doi: 10.1109/jsen.2013.2254234.

  • D. Amagata, T. Hara, Y. Sasaki, and S. Nishio, "Efficient cluster-based top-k query routing with data replication in MANETs," Soft Computing, vol. 21, no. 15, pp. 4161–4178, 2017, doi: 10.1007/s00500-015-1867-2.

  • S. Pathak and S. Jain, "An optimized stable clustering algorithm for mobile ad hoc networks," Eurasip Journal on Wireless Communications and Networking, vol. 2017, no. 1, pp. 1–11, Dec. 2017, doi: 10.1186/s13638-017-0832-4.

  • D. Sundaranarayana and K. Venkatachalapathy, "Using a Modified Butterfly Optimization with Associative Cluster Head Load Distribution," 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, no. Icoei, pp. 448–453, 2018. doi: 10.1109/ICOEI.2018.8553907.

  • M. Ahmad, A. Hameed, F. Ullah, I. Wahid, S. U. Rehman, and 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, vol. 11, no. 11, pp. 4347–4361, 2020, doi: 10.1007/s12652-018-1141-4.

  • N. Khatoon and Amritanjali, "A node stability based multi-metric weighted clustering algorithm for mobile ad hoc networks," in Lecture Notes in Electrical Engineering, 2019, vol. 476, pp. 63–77, 2019, doi: 10.1007/978-981-10-8234-4_7.

  • S. Amutha, B. Kannan, and M. Kanagaraj, "Energy‐ efficient cluster manager‐ based cluster head selection technique for communication networks," International Journal of Communication Systems, vol. 34, no. 5, p. e4741, Mar. 2021, doi: 10.1002/dac.4741.

  • A. S. Mohammed, S. Balaji B, S. B. M. S, A. P. N, and V. K, "FCO Fuzzy constraints applied Cluster Optimization technique for Wireless AdHoc Networks," Computer Communications, vol. 154, no. February, pp. 501–508, 2020, doi: 10.1016/j.comcom.2020.02.079.


Abstract Views: 420

PDF Views: 1




  • Cluster Head Selection Algorithm for MANETs Using Hybrid Particle Swarm Optimization-Genetic Algorithm

Abstract Views: 420  |  PDF Views: 1

Authors

Fouziah Hamza
Department of Computer Applications, Noorul Islam Centre for Higher Education, Kumarakovil, Tamil Nadu, India
S. Maria Celestin Vigila
Department of Information Technology, Noorul Islam Centre for Higher Education, Kumarakovil, Tamil Nadu, India

Abstract


The Mobile Ad-hoc Network (MANET) is a decentralized system that consists of mobile nodes. Wireless connections are used to connect these nodes. The primary issues of concern of MANETs are mobility and limited battery lifetime. Advanced techniques for improving MANET energy efficiency and extending network lifespan are critical. Clustering is one of the tried-and-true methods for increasing network lifetime by lowering and balancing energy consumption. Choosing a suitable cluster head from the cluster improves the network’s energy efficiency even further. Because of the additional workloads, the cluster heads (CHs) utilize more energy than non-cluster heads. A novel algorithm for CH selection with a Hybrid Particle Swarm Optimization-Genetic Algorithm (PSO-GA) is proposed to improve the MANET network’s energy efficiency and lifetime. The proposed method is implemented using the NS-2 platform for the analysis. The proposed model outperforms the existing OSCA, EP-MBO, GBTC, SM-WCA, CM-BCA, and FCO methods in terms of network performance. The model’s performance has achieved a low Bit Error Rate (BER) of 7% for 100 nodes with 99.38% Packet Delivery Ratio (PDR) with minimized delay in the range 2.01sec with the energy efficiency of 99.03%. The validation indicates that the Hybrid PSO-GA approach is more efficient than the other methods.

Keywords


Mobile Ad-hoc Network, Clustering, Soft k-Means, Cluster Head Selection, PSO-GA Optimization Algorithm.

References





DOI: https://doi.org/10.22247/ijcna%2F2021%2F208892