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Sathiya, M.
- An Intelligent Resnets Resource Allocation Framework for 5G Networks
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Authors
Affiliations
1 Department of Computer Science and Engineering, Hindusthan Institute of Technology, India., IN
2 Department of Information Technology, Karpagam Institute of Technology, India., IN
1 Department of Computer Science and Engineering, Hindusthan Institute of Technology, India., IN
2 Department of Information Technology, Karpagam Institute of Technology, India., IN
Source
ICTACT Journal on Communication Technology, Vol 14, No 1 (2023), Pagination: 2837-2842Abstract
This paper presents a resource allocation technique for industrial applications for 6G networks, which are characterised by the presence of many heterogeneous parameters that have an effect on the quality of data transmission. The purpose of the project is to achieve the greatest possible efficiency in the application of the resources that are presently while achieving a higher level of control over a diverse collection of sensing nodes operating within a hybrid network. The system model that has been proposed is a workable option for efficient resource allocation. The performance of the proposed method, in addition to similarities to the performance of other methods has been analysed. The proposed methods offer performance that is comparable to or better than the baseline, while simultaneously significantly reducing the SI exchange overhead and improving the system resilience to sensing intervals, some of which may be unavoidable in practise.Keywords
ResNets, Resource Allocation, 6G, IoT.
References
- V. Saravanan, D. Saravanan and H.P. Sultana, “Design of Deep Learning Model for Radio Resource Allocation in 5G for Massive IoT Device”, Sustainable Energy Technologies and Assessments, Vol. 56, pp. 103054-103064, 2023.
- M. Sheng and J. Li, “Coverage Enhancement for 6G Satellite-Terrestrial Integrated Networks: Performance Metrics, Constellation Configuration and Resource Allocation”, Science China Information Sciences, Vol. 66, No. 3, pp. 1-20, 2023.
- J. Singh, J. Deepika and J. Sathyendra Bhat, “EnergyEfficient Clustering and Routing Algorithm Using Hybrid Fuzzy with Grey Wolf Optimization in Wireless Sensor Networks”, Security and Communication Networks, Vol. 2022, pp. 1-12, 2022.
- J. Huan and K. Yu, “Opportunistic Capacity based Resource Allocation for 6G Wireless Systems with Network Slicing”, Future Generation Computer Systems, Vol. 140, pp. 390- 401, 2023.
- Y. Robinson, E.G. Julie and P.E. Darney, “Enhanced Energy Proficient Encoding Algorithm for Reducing Medium Time in Wireless Networks”, Wireless Personal Communications, Vol. 131, pp. 3569-3588, 2021.
- R. Indhumathi and A. Pandey, “Design of Task Scheduling and Fault Tolerance Mechanism Based on GWO Algorithm for Attaining Better QoS in Cloud System”, Wireless Personal Communications, Vol. 95, pp. 1-19, 2022.
- P. Qin and S. Geng, “Content Service Oriented Resource Allocation for Space-Air-Ground Integrated 6G Networks: A Three-Sided Cyclic Matching Approach”, IEEE Internet of Things Journal, Vol. 10, No. 1, pp. 828-839, 2022.
- S.U. Jamil, “Resource Allocation and Task Off-Loading for 6G Enabled Smart Edge Environments”, IEEE Access, Vol. 10, pp. 93542-93563, 2022.
- T. Karthikeyan, K. Praghash and K.H. Reddy, “Binary Flower Pollination (BFP) Approach to Handle the Dynamic Networking Conditions to Deliver Uninterrupted Connectivity”, Wireless Personal Communications, Vol. 121, No. 4, pp. 3383-3402, 2021.
- T.Q. Duong and H. Shin, “Quantum-Inspired Machine Learning for 6G: Fundamentals, Security, Resource Allocations, Challenges, and Future Research Directions”, IEEE Open Journal of Vehicular Technology, Vol. 3, pp. 375-387, 2022.
- F.D.O. Torres, D.L. Cardoso and R.C. Oliveira, “Radio Resource Allocation in a 6G D-OMA Network with Imperfect SIC: A Framework Aided by a Bi-Objective Hyper-Heuristic”, Engineering Applications of Artificial Intelligence, Vol. 119, pp. 105830-105843, 2023.
- D.H. Tran and B. Ottersten, “Satellite-and Cache-Assisted UAV: A Joint Cache Placement, Resource Allocation, and Trajectory Optimization for 6G Aerial Networks”, IEEE Open Journal of Vehicular Technology, Vol. 3, pp. 40-54, 2022.
- H.B. Salameh and A. Al-Ajlouni, “Energy-Efficient Power-Controlled Resource Allocation for MIMO-based Cognitive-enaBled B5G/6G Indoor-Flying Networks”, IEEE Access, Vol. 10, pp. 106828-106840, 2022.
- T.K. Rodrigues and N. Kato, “Network Slicing with Centralized and Distributed Reinforcement Learning for Combined Satellite/Ground Networks in a 6G Environment”, IEEE Wireless Communications, Vol. 29, No. 1, pp. 104-110, 2022.
- Fuzzy Based Optimization for Improving the Trust Score in Manets
Abstract Views :103 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, Karpagam Institute of Technology, India., IN
2 Department of Information Technology, Karpagam Institute of Technology, India., IN
3 Department of Information Technology, Sri Ramakrishna Engineering College, India., IN
4 Department of Computer Science and Engineering, Sona College of Technology, India., IN
1 Department of Computer Science and Engineering, Karpagam Institute of Technology, India., IN
2 Department of Information Technology, Karpagam Institute of Technology, India., IN
3 Department of Information Technology, Sri Ramakrishna Engineering College, India., IN
4 Department of Computer Science and Engineering, Sona College of Technology, India., IN
Source
ICTACT Journal on Communication Technology, Vol 14, No 1 (2023), Pagination: 2854-2860Abstract
In this paper, research develop a method for identifying abnormal behavior based on two inputs: the trustworthiness of the user, as well as the reliability of the recommendations that they make. Specifically, research look at the reliability of the user recommendations. The next thing that needs to be done is to calculate the node general trust value in order to determine if there has been any kind of malicious attack. This will show whether or not the node has been compromised in any way. It is conceivable that this could lessen the amount of power that is needed for the communication that takes place between different networks. Additionally, it demonstrates that the model is better able to utilize the evaluation results of the common neighbor nodes to synthesize the confidence value when fewer nodes are deployed in the network. This is demonstrated by the fact that fewer nodes are deployed in the network. The reliability of the trust assessment improves while the number of trusts for which recommendations are made decreases.Keywords
Fuzzy Optimization, Trust, Score, MANETs, Direct TrustReferences
- R. Sabitha, V. Anusuya and V. Saravanan, “Network Based Detection of IoT Attack Using AIS-IDS Model”, Wireless Personal Communications, Vol. 98, pp. 1-24, 2022.
- N. Khandelwal and S. Gupta, “A Review: Trust based Secure IoT Architecture in Mobile Ad-hoc Network”, Proceedings of International Conference on Applied Artificial Intelligence and Computing, pp. 1464-1472, 2022.
- V. Thirunavukkarasu, and P. Prakasam, “Cluster and Angular based Energy Proficient Trusted Routing Protocol for Mobile Ad-Hoc Network”,Peer-to-Peer Networking and Applications, Vol. 15, No. 5, pp. 2240-2252, 2022.
- J. Singh and S. Sakthivel, “Energy-Efficient Clustering and Routing Algorithm Using Hybrid Fuzzy with Grey Wolf Optimization in Wireless Sensor Networks”, Security and Communication Networks, Vol. 2022, pp. 1-13, 2022.
- Y. Wang and L.C. Kho, “Towards Strengthening the Resilience of IoV Networks-A Trust Management Perspective”, Future Internet, Vol. 14, No. 7, pp. 202-215, 2022.
- J. Kuriakose and A.K. Bairwa, “EMBN-MANET: A Method to Eliminating Malicious Beacon Nodes in Ultra-Wideband (UWB) based Mobile Ad-Hoc Network”, Ad Hoc Networks, Vol. 140, pp. 103063-103076, 2023.
- S. Ayed and L. Chaari, “Blockchain and Trust-Based Clustering Scheme for the IoV”, Ad Hoc Networks, Vol. 140, pp. 103093-103108, 2023.
- Y.H. Robinson, V. Saravanan and P.E. Darney, “Enhanced Energy Proficient Encoding Algorithm for Reducing Medium Time in Wireless Networks”, Wireless Personal Communications, Vol. 119, pp. 3569-3588, 2021.
- N. El Ioini and C. Pahl, “Trust Management for Service Migration in Multi-Access Edge Computing Environments”, Computer Communications, Vol. 194, pp. 167-179, 2022.
- M. Kandasamy and A.S. Kumar, “QoS Design using Mmwave Backhaul Solution for Utilising Underutilised 5G Bandwidth In GHz Transmission”, Proceedings of International Conference on Artificial Intelligence and Smart Energy, pp. 1615-1620, 2023.
- N.M.M. Hiraide and N. Yoshida, “Trust Management in Growing Decentralized Networks”, Journal of Computations and Modelling, Vol. 12, No. 3, pp. 1-12, 2022.
- J. Kundu and S. Pal, “Trust-Based Efficient Computational Scheme for MANET in Clustering Environment”, Proceedings of International Conference on Mathematical Modeling and Computational Science, pp. 305-314, 2022.