Energy efficient adaptive clustering with QoS-aware CBRP and grey wolf optimization clustering algorithm for mobile ad-hoc network (MANET)
Abstract In the ad-hoc network, where we have mobile and wireless connections, traditional grouping, generally faced with many challenges, including the weakness of memory in Local and ineffective energy use. These problems lead to the reduction of network life and service quality (QOS). Moreover, i...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-07-01
|
| Series: | Discover Computing |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s10791-025-09685-0 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849234518061350912 |
|---|---|
| author | Minal Patil Manish Chawhan Roshan Umate Abhishek Madankar Bhumika Neole |
| author_facet | Minal Patil Manish Chawhan Roshan Umate Abhishek Madankar Bhumika Neole |
| author_sort | Minal Patil |
| collection | DOAJ |
| description | Abstract In the ad-hoc network, where we have mobile and wireless connections, traditional grouping, generally faced with many challenges, including the weakness of memory in Local and ineffective energy use. These problems lead to the reduction of network life and service quality (QOS). Moreover, in a large network that has a high movement of node changes, frequent weaving, and energy levels that require volunteers to increase complexity to maintain the stability of the network and effective communication. To meet these challenges, the algorithm of the gray wolf enhancement (GWOCA) focuses on the use of energy and choosing the best cluster (CHS). GWOCA procures the ideal cluster center. Guaranteed a balanced load distribution and the ability to adjust the size quickly, even for large and dynamic networks. Moreover, the cluster definition protocol (CBRP) has been developed to improve the efficiency of determination by organizing the structure itself as a group and selecting CHs for internal communication and between effective groups. The preliminary simulation operated using the NS2 model, showing that the GWOCA cluster is significantly higher than the traditional grouping methods, including cluster, cluster, Parent Cluster, and CBRP, in the main network performance. Our research states that GWOCA can reduce the delay by 30.66%, reduce energy consumption by 4.55% and the PDR (PDR) ratio is reduced by 10% compared to general methods. When compared to the method, the method that is presented is also evaluated for the ability to expand, which effectively helps to ensure the use of reliable energy and sending packets. And show stable performance under the effective node density. In addition, in-depth energy analysis for different network phases—Creating a cluster. The route and maintenance have been implemented. By emphasizing the importance of energy saving that can be done by our modified group. These results confirm that the proposed GWOCA-based clustering method presents a robust, scalable solution for energy-efficient, QoS-aware MANETs, capable of addressing the critical challenges of energy depletion, topology changes, and scalability, making it suitable for real-world applications in dynamic, mobile, and resource-constrained environments. |
| format | Article |
| id | doaj-art-0cd541955d0349e7a0f3957017aaa1f5 |
| institution | Kabale University |
| issn | 2948-2992 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Computing |
| spelling | doaj-art-0cd541955d0349e7a0f3957017aaa1f52025-08-20T04:03:07ZengSpringerDiscover Computing2948-29922025-07-0128114410.1007/s10791-025-09685-0Energy efficient adaptive clustering with QoS-aware CBRP and grey wolf optimization clustering algorithm for mobile ad-hoc network (MANET)Minal Patil0Manish Chawhan1Roshan Umate2Abhishek Madankar3Bhumika Neole4Research Scholar, Department of Electronics Engineering, Yeshwantrao Chavan College of EngineeringDepartment of Electronics and Telecommunication Engineering, Yeshwantrao Chavan College of EngineeringCentre of Early Childhood Development-Stepping Stones Project, Datta Meghe Institute of Higher Education and ResearchDepartment of Electronics and Telecommunication Engineering, Yeshwantrao Chavan College of EngineeringSchool of Electrical and Electronics Engineering, Department of Electronics and Communication Engineering, Ramdeobaba UniversityAbstract In the ad-hoc network, where we have mobile and wireless connections, traditional grouping, generally faced with many challenges, including the weakness of memory in Local and ineffective energy use. These problems lead to the reduction of network life and service quality (QOS). Moreover, in a large network that has a high movement of node changes, frequent weaving, and energy levels that require volunteers to increase complexity to maintain the stability of the network and effective communication. To meet these challenges, the algorithm of the gray wolf enhancement (GWOCA) focuses on the use of energy and choosing the best cluster (CHS). GWOCA procures the ideal cluster center. Guaranteed a balanced load distribution and the ability to adjust the size quickly, even for large and dynamic networks. Moreover, the cluster definition protocol (CBRP) has been developed to improve the efficiency of determination by organizing the structure itself as a group and selecting CHs for internal communication and between effective groups. The preliminary simulation operated using the NS2 model, showing that the GWOCA cluster is significantly higher than the traditional grouping methods, including cluster, cluster, Parent Cluster, and CBRP, in the main network performance. Our research states that GWOCA can reduce the delay by 30.66%, reduce energy consumption by 4.55% and the PDR (PDR) ratio is reduced by 10% compared to general methods. When compared to the method, the method that is presented is also evaluated for the ability to expand, which effectively helps to ensure the use of reliable energy and sending packets. And show stable performance under the effective node density. In addition, in-depth energy analysis for different network phases—Creating a cluster. The route and maintenance have been implemented. By emphasizing the importance of energy saving that can be done by our modified group. These results confirm that the proposed GWOCA-based clustering method presents a robust, scalable solution for energy-efficient, QoS-aware MANETs, capable of addressing the critical challenges of energy depletion, topology changes, and scalability, making it suitable for real-world applications in dynamic, mobile, and resource-constrained environments.https://doi.org/10.1007/s10791-025-09685-0Local optimaCluster-headsMANETNS2PDRQoS |
| spellingShingle | Minal Patil Manish Chawhan Roshan Umate Abhishek Madankar Bhumika Neole Energy efficient adaptive clustering with QoS-aware CBRP and grey wolf optimization clustering algorithm for mobile ad-hoc network (MANET) Discover Computing Local optima Cluster-heads MANET NS2 PDR QoS |
| title | Energy efficient adaptive clustering with QoS-aware CBRP and grey wolf optimization clustering algorithm for mobile ad-hoc network (MANET) |
| title_full | Energy efficient adaptive clustering with QoS-aware CBRP and grey wolf optimization clustering algorithm for mobile ad-hoc network (MANET) |
| title_fullStr | Energy efficient adaptive clustering with QoS-aware CBRP and grey wolf optimization clustering algorithm for mobile ad-hoc network (MANET) |
| title_full_unstemmed | Energy efficient adaptive clustering with QoS-aware CBRP and grey wolf optimization clustering algorithm for mobile ad-hoc network (MANET) |
| title_short | Energy efficient adaptive clustering with QoS-aware CBRP and grey wolf optimization clustering algorithm for mobile ad-hoc network (MANET) |
| title_sort | energy efficient adaptive clustering with qos aware cbrp and grey wolf optimization clustering algorithm for mobile ad hoc network manet |
| topic | Local optima Cluster-heads MANET NS2 PDR QoS |
| url | https://doi.org/10.1007/s10791-025-09685-0 |
| work_keys_str_mv | AT minalpatil energyefficientadaptiveclusteringwithqosawarecbrpandgreywolfoptimizationclusteringalgorithmformobileadhocnetworkmanet AT manishchawhan energyefficientadaptiveclusteringwithqosawarecbrpandgreywolfoptimizationclusteringalgorithmformobileadhocnetworkmanet AT roshanumate energyefficientadaptiveclusteringwithqosawarecbrpandgreywolfoptimizationclusteringalgorithmformobileadhocnetworkmanet AT abhishekmadankar energyefficientadaptiveclusteringwithqosawarecbrpandgreywolfoptimizationclusteringalgorithmformobileadhocnetworkmanet AT bhumikaneole energyefficientadaptiveclusteringwithqosawarecbrpandgreywolfoptimizationclusteringalgorithmformobileadhocnetworkmanet |