Brown Boosted Expectation Maximization Ensemble Node Clustering Based Energy Efficient And Reliable Data Routing In Manet With 5g And Nxtg Technology
Keywords:
Bandwidth Availability, Brown Boosting, Expectation Maximization Cluster, Potential Loss, Residual Energy and TrustAbstract
MANET and fifth-generation mobile network (5G) are attained greater significance in next-generation because of their simplicity and efficiency in handling real problems in diverse applications. The data communication in 5G networks has improved the number of users and the information transmission rate among the nodes in network. Though, the connection among these nodes has to continually renew because of mobility, connection failure, routing overhead and low battery energy. Therefore, it utilizes more energy in searching and connecting the mobile nodes during the process of data routing. Therefore, Brown Boosted Expectation Maximization Ensemble Node Clustering (BBEMENC) Technique is planned in this paper with objective of decreasing the energy employment during the processes of reliable data sharing in MANET using 5G. In the BBEMENC Technique, more numbers of mobile nodes are taken as training samples. Furthermore, Expectation Maximization (EM) clusters are assumed weak clusters. In BBEMENC Technique, EM clusters is applied for grouping all the nodes in network as strong strength or weak node based on their residual energy, trust and bandwidth availability. Subsequently, weak clusters outcomes are united to get strong cluster results where it accurately finds strong strength and weak nodes in MANET. Finally, BBEMENC Technique choose the nearest strong strength nodes as optimal in order to efficiently route the data packets between the source and destination in 5G mobile network. From that, BBEMENC Technique gains a higher routing performance with better packet delivery ratio and latency. The simulation of BBEMENC Technique is done by considering the metrics such as energy utilization, packet delivery ratio, latency and scalability with varying numbers of input mobile nodes and data packets.
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A. Sangeetha, T. Rajendran, “Supervised vector machine learning with brown boost energy efficient data delivery in MANET, Sustainable Computing: Informatics and Systems, Volume 35, 2022
Ujwala Ravale, Gautam M. Borkar, “Secure and efficient trust enabled routing in mobile ad hoc network using game theory and grey wolf optimization techniques”, IET Wireless Sensor Systems, 2024; Pages 1–26
Devi, E.A., Radhika, S. & Chandrasekar, A. An energy-efficient MANET relay node selection and routing using a fuzzy-based analytic hierarchy process. Telecommun Syst 83, 209–226 (2023)
S, A., & A, R. (2024), Hybrid Secure Cluster-Based Routing Algorithm for Enhanced Security and Efficiency in Mobile Ad Hoc Networks, Applied Artificial Intelligence, 38(1)
Vu, K.Q., Solanki, V.K., Le, A.N. (2022). A Saving Energy MANET Routing Protocol in 5G. In: Velliangiri, S., Gunasekaran, M., Karthikeyan, P. (eds) Secure Communication for 5G and IoT Networks, EAI/Springer Innovations in Communication and Computing, Springer, pp 213–220
Rajendra Prasad P, Shivashankar, “Enhanced Energy Efficient Secure Routing Protocol for Mobile Ad-Hoc Network”, Global Transitions Proceedings, Volume 3, Issue 2, 2022, Pages 412-423
H. Riasudheen, K. Selvamani, Saswati Mukherjee, I.R. DivyasreeAuthors Info & Claims, “An efficient energy-aware routing scheme for cloud-assisted MANETs in 5G”, Ad Hoc Networks, Volume 97, Issue C, 2020
M. Umar Khan, M. Azizi, A. García-Armada and J. J. Escudero-Garzás, "Unsupervised Clustering for 5G Network Planning Assisted by Real Data," in IEEE Access, vol. 10, pp. 39269-39281, 2022
M. Ahmad, A. Hameed, A. A. Ikram and I. Wahid, "State-of-the-Art Clustering Schemes in Mobile Ad Hoc Networks: Objectives, Challenges, and Future Directions," in IEEE Access, vol. 7, pp. 17067-17081, 2019
Y. Song, H. Luo, S. Pi, C. Gui and B. Sun, "Graph Kernel Based Clustering Algorithm in MANETs," in IEEE Access, vol. 8, pp. 107650-107660, 2020
X. Chen, G. Sun, T. Wu, L. Liu, H. Yu and M. Guizani, "RANCE: A Randomly Centralized and On-Demand Clustering Protocol for Mobile Ad Hoc Networks," in IEEE Internet of Things Journal, vol. 9, no. 23, pp. 23639-23658, 1 Dec.1, 2022
N. Veeraiah et al., "Trust Aware Secure Energy Efficient Hybrid Protocol for MANET," in IEEE Access, vol. 9, pp. 120996-121005, 2021
Seyed Ali Sharifi, Seyed Morteza BabamirAuthors Info & Claims, “The clustering algorithm for efficient energy management in mobile ad-hoc networks”, Computer Networks: The International Journal of Computer and Telecommunications Networking, Volume 166, Issue C, 2020
H. Safa, O. Mirza, “A load balancing energy efficient clustering algorithm for MANETs”, International Journal of Communication Systems, International Journal of Communication Systems, 2010
Alghamdi, S.A. Stable zone-based 5G clustered MANET using interest-region-based routing and gateway selection. Peer-to-Peer Netw. Appl. 14, 3559–3577 (2021)
Rajeswari AR, Lai W-C, Kavitha C, Balasubramanian PK, Srividhya SR. A Trust-Based Secure Neuro Fuzzy Clustering Technique for Mobile Ad Hoc Networks. Electronics. 2023; 12(2):274
Reza Sookhtsaraei, Mohsen Nejadkheirallah, Mohammad Saber Iraji, “MMF Clustering: A On-demand One-hop Cluster Management in MANET Services Executing Perspective”, Wireless Personal Communications, Volume 125, Issue 2, Pages 1973 – 2002, 2022
Ibrahim Aqeel, “HarborSync: An Advanced Energy-efficient Clustering-based Algorithm for Wireless Sensor Networks to Optimize Aggregation and Congestion Control”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 15, No. 1, Pages 799-808, 2024
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