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Jayanthiladevi, A.
- Handoff in 5g Ultra Dense Networks Using Fixed Sphere Precoding
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Authors
Affiliations
1 College of Computer Science and Information Science, Srinivas University, IN
1 College of Computer Science and Information Science, Srinivas University, IN
Source
ICTACT Journal on Communication Technology, Vol 13, No 2 (2022), Pagination: 2689-2693Abstract
It is anticipated that the millimetrewave, often known as mm-wave, technology that will be used in 5G networks will greatly enhance network capacity. The mm-wave signals, on the other hand, are prone to obstructions than the ones at lower bands; this demonstrates the impact that route loss has on the network coverage. Because of the fractal nature of cellular coverage and the different path loss exponents that apply to different directions, it has been suggested that a route loss model in a multi-directional manner for 5G UDN networks. This is due to the fact that different directions have path loss exponents. In addition, the proposed loss model is applied to the 5G ultra-dense network in order to calculate the coverage probability, association probability, and handoff probability (UDN). According to the numerical findings of this research, in 5G UDN, the influence of anisotropic path loss increases the association probability with long link distance. It has also come to light that the performance of the handoff suffers tremendously as a consequence of the anisotropic propagation environment. A new difficulty has arisen for 5G UDN as a consequence of the substantial handoff overhead that has been produced.Keywords
Fractal Characteristics, Multi-Directional Path Loss, Cellular Coverage Ultra-Dense Network
References
- M. Ramalingam, S. Vinothkumar and P. Ramya, “Predictive Handoff Management in Vehicular Networks Using both Weight Value Based and K-Means Algorithm Based Clustering Algorithm to Meet Desired QoS”, Journal of Physics: Conference Series, Vol. 1969, No. 1, pp. 1-12, 2021.
- S.A. Syed, K. Sheela Sobana Rani and V.P. Sundramurthy, “Design of Resources Allocation in 6G Cybertwin Technology using the Fuzzy Neuro Model in Healthcare Systems”, Journal of Healthcare Engineering, Vol. 2022, pp. 1-13, 2022.
- H. Luo, K. Cao and Y. Zhou, “DQN-Based Predictive Spectrum Handoff via Hybrid Priority Queuing Model”, IEEE Communications Letters, Vol. 26, No. 3, pp. 701-705, 2021.
- T. Karthikeyan and K. Praghash, “An Improved Task Allocation Scheme in Serverless Computing using Gray Wolf Optimization (GWO) based Reinforcement Learning (RIL) Approach”, Wireless Personal Communications, Vol. 117, No. 3, pp. 2403-2421, 2021.
- L. Thomas and J. Paulose, “A Survey on Various Handoff Methods in Mobile Ad Hoc Network Environment”, Proceedings of International Conference on Smart Computing Paradigms: New Progresses and Challenges, pp. 27-60, 2020.
- I. Rabet, S.P. Selvaraju and M. Bjorkman, “Poster: Particle Filter for Handoff Prediction in SDN-based IoT Networks”, Proceedings of International Conference on Wireless Networks, pp. 172-173, 2020.
- A.C. Marino and J.A. Mazer, “Saccades Trigger Predictive Updating of Attentional Topography in Area V4”, Neuron, Vol. 98, No. 2, pp. 429-438, 2018.
- S. Kunarak and R. Suleesathira, “Multi-Criteria Vertical Handoff Decision Algorithm for Overlaid Heterogeneous Mobile IP Networks”, Journal of the Franklin Institute, Vol. 357, No. 10, pp. 6321-6351, 2020.
- S.A. Patil and B.K. Singh, “Prediction of IoT Traffic using the Gated Recurrent Unit Neural Network-(GRU-NN-) based Predictive Model”, Security and Communication Networks, Vol. 2021, pp. 1-14, 2021.
- K.M. Awan and K. Rabie, “Smart Handoff Technique for Internet of Vehicles Communication using Dynamic Edge-Backup Node”, Electronics, Vol. 9, No. 3, pp. 524-534, 2020.
- S. Koley, D. Bepari and D. Mitra, “Predictive Multi-user Dynamic Spectrum Allocation Using Hidden Semi-Markov Model”, Journal of Communications Technology and Electronics, Vol. 63, No. 12, pp. 1393-1405, 2018.
- A.K. Gupta, V. Goel and M. Sain, “A Fuzzy based Handover Decision Scheme for Mobile Devices using Predictive Model”, Electronics, Vol. 10, No. 16, pp. 1-13, 2021.
- P.S. Yawada and M.T. Dong, “Intelligent Process of Spectrum Handoff/Mobility in Cognitive Radio Networks”, Journal of Electrical and Computer Engineering, Vol. 2019, pp. 1-8, 2019.
- S.H. Alsamhi and B. Lee, “Predictive Estimation of Optimal Signal Strength from Drones over IoT Frameworks in Smart Cities”, IEEE Transactions on Mobile Computing, Vol. 89, pp. 1-8, 2021.
- Design of Categorical Data Clustering Using Machine Learning Ensemble
Abstract Views :70 |
PDF Views:2
Authors
Affiliations
1 Institute of Computer Science and Information Science, Srinivas University, IN
1 Institute of Computer Science and Information Science, Srinivas University, IN
Source
ICTACT Journal on Soft Computing, Vol 12, No 4 (2022), Pagination: 2729-2734Abstract
Cluster analysis of data is a crucial tool for discovering and making sense of a dataset underlying structure. It has been put to use in many contexts and many different fields with great success. In addition, new innovations in the last decade have piqued the interest of clinical researchers, scientists, and biologists. As the number of dimensions in a data set grows, the consensus function of traditional ensemble clustering often fails to generate final clusters. The main problem with conventional ensemble clustering is exactly this. The proposed work employs a similarity measure between links to identify which clusters contain the unknown datasets. To this end, this study proposes employing an improved ensemble framework for clustering categorical datasets. More specifically, it employs ensemble machine learning methods to categorize data. Multiple machine learning algorithms are incorporated into this model. Objective performance indicators are used to compare a model to more traditional approaches to determine how effective each the proposed method is.Keywords
Base Clustering, Ensemble Clustering Clusters, Accuracy, PrecisionReferences
- L. Bai and J. Liang, “A Categorical Data Clustering Framework on Graph Representation”, Pattern Recognition, Vol. 128, pp. 1-13, 2022.
- R. Brnawy and N. Shiri, “Improving Quality of Ensemble Technique for Categorical Data Clustering Using Granule Computing”, Proceedings of International Conference on Database and Expert Systems Applications, pp. 261-272, 2021.
- G. Pole and P. Gera, “Cluster-Based Ensemble Using Distributed Clustering Approach for Large Categorical Data”, Proceedings of International Conference on ICT Analysis and Applications, pp. 671-680, 2021.
- I. Khan and R. Hedjam, “Ensemble Clustering using Extended Fuzzy k-Means for Cancer Data Analysis”, Expert Systems with Applications, Vol. 172, pp. 114622-114633, 2021.
- D.T. Dinh, V.N. Huynh and S. Sriboonchitta, “Clustering mixed Numerical and Categorical Data with Missing Values”, Information Sciences, Vol. 571, pp. 418-442, 2021.
- I. Singh, N. Kumar and S. Jain, “A Multi-Level Classification and Modified PSO Clustering based Ensemble Approach for Credit Scoring”, Applied Soft Computing, Vol. 111, pp. 107687-107698, 2021.
- B.A. Hassan and T.A. Rashid, “A Multidisciplinary Ensemble Algorithm for Clustering Heterogeneous Datasets”, Neural Computing and Applications, Vol. 33, No. 17, pp. 10987-11010, 2021.
- K. Parish Venkata Kumar and M. Jogendra Kumar, “Concept Summarization of Uncertain Categorical Data Streams Based on Cluster Ensemble Approach”, Proceedings of International Conference on Pervasive Computing and Social Networking, pp. 385-398, 2022.
- V. Shorewala, “Early Detection of Coronary Heart Disease using Ensemble Techniques”, Informatics in Medicine Unlocked, Vol. 26, pp. 1-16, 2022.
- I.B. Ayinla and S.O. Akinola, “An Improved Ensemble Model using Random Forest Branch Clustering Optimisation Approach”, University of Ibadan Journal of Science and Logics in ICT Research, Vol. 7, No. 2, pp. 8-19, 2021.
- Detection of Cyber Attack on Internet of Vehicle Commuters
Abstract Views :103 |
PDF Views:0
Authors
Affiliations
1 Institute of Computer Science and Information Science, Srinivas University, India., IN
2 Department of Mathematics and Computer Science, University of Africa, Nigeria., NG
1 Institute of Computer Science and Information Science, Srinivas University, India., IN
2 Department of Mathematics and Computer Science, University of Africa, Nigeria., NG
Source
ICTACT Journal on Communication Technology, Vol 14, No 1 (2023), Pagination: 2876-2881Abstract
The Internet of Vehicles (IoV) is a massive interactive network that can be extended into the realm of smart transportation by utilizing IoV at scale because it is capable of attaining unified management. It is well known that the gathered contents not only contain personal information, but also certain critical data, such as a vehicle running parameter, which is strongly related to traffic safety. This study explains how a network intrusion detection system (IDS) based on artificial intelligence can be deployed over various datasets. The simulation is carried out in an extensive way and the results show that the proposed method achieves a higher rate of accuracy in detecting the instances than the other existing methods.Keywords
Internet of Vehicles, Intrusion Detection System, Traffic System, Vehicle Commuters.References
- S. Wasserman and K. Faust, “Social Network Analysis: Methods and Applications”, Cambridge University Press, 1994.
- K. Praghash and T. Karthikeyan, “Data Privacy Preservation and Trade-off Balance Between Privacy and Utility using Deep Adaptive Clustering and Elliptic Curve Digital Signature Algorithm”, Wireless Personal Communications, Vol. 78, 1-16, 2021.
- N.V. Kousik, M. Sivaram and R. Mahaveerakannan, “Improved Density-Based Learning to Cluster for User Web Log in Data Mining”, Proceedings of International Conference on Inventive Computation and Information Technologies, pp. 813-830, 2021.
- G. Dhiman, A.V. Kumar, R. Nirmalan and S. Sujitha, “Multi-Modal Active Learning with Deep Reinforcement Learning for Target Feature Extraction in Multi-Media Image Processing Applications”, Multimedia Tools and Applications, Vol. 23, pp. 1-25, 2022.
- V. Saravanan and A. Neeraja, “Security Issues in Computer Networks and Stegnography”, Proceedings of International Conference on Intelligent Systems and Control, pp. 363-366, 2013.
- B. Zou, “Cyber Resilience of Autonomous Mobility Systems: Cyber-Attacks and Resilience-Enhancing Strategies”, Journal of Transportation Security, Vol. 2021, 1-19, 2021.
- L. Yang and A. Shami, “A Transfer Learning and Optimized CNN based Intrusion Detection System for Internet of Vehicles”, Proceedings of IEEE International Conference on Communications, pp. 2774-2779, 2022.
- V. Saravanan and P. Jayashree, “Security Issues in Protecting Computers and Maintenance”, Journal of Global Research in Computer Science, Vol. 4, No. 1, pp. 55-58, 2013.
- S. Ullah and W.J. Buchanan, “HDL-IDS: A Hybrid Deep Learning Architecture for Intrusion Detection in the Internet of Vehicles”, Sensors, Vol. 22, No. 4, pp. 1340-1354, 2022.
- A. Zacharaki and D. Tzovaras, “Complex Engineering Systems as an Enabler for Security in Internet of Vehicles: The nIoVe Approach”, Proceedings of International Conference on Societal Automation, pp. 1-8, 2019.
- M.M. Moussa and L. Alazzawi, “Cyber Attacks Detection based on Deep Learning for Cloud-Dew Computing in Automotive IoT Applications”, Proceedings of IEEE International Conference on Smart Cloud, pp. 55-61, 2020.
- M. Elsisi and M.Q. Tran, “Development of an IoT Architecture based on a Deep Neural Network against Cyber Attacks for Automated Guided Vehicles”, Sensors, Vol. 21, No. 24, pp. 8467-8474, 2021.
- A. Castiglione, “Securing the Internet of Vehicles through Lightweight Block Ciphers”, Pattern Recognition Letters, Vol. 135, pp. 264-270, 2020.
- Energy Aware Geographic Routing Protocol using Evolutionary Algorithms for Improving QOS in MANET
Abstract Views :47 |
PDF Views:1
Authors
Affiliations
1 Post-Doctoral Research Fellow, Institute of Computer Science and Information Science, Srinivas University, Mangalore, Karnataka, IN
2 Professor, Institute of Computer Science and Information Science, Srinivas University, Karnataka, IN
1 Post-Doctoral Research Fellow, Institute of Computer Science and Information Science, Srinivas University, Mangalore, Karnataka, IN
2 Professor, Institute of Computer Science and Information Science, Srinivas University, Karnataka, IN
Source
International Journal of Advanced Networking and Applications, Vol 15, No 2 (2023), Pagination: 5882-5886Abstract
An energy-saving geographic routing is a crucial problem when trying to increase QoS and network life. In order for their adjacent neighbours to be able to reach effective routing performance, geographical routing nodes are necessary. However, the network's lifetime for efficient transmission has not been improved. In order to dynamically regulate the frequency of the position updates according to node movement dynamics, an updated position strategy for geographical routing is implemented. Various optimized geographical routing protocols have been designed to prevent interference between nodes, so that the data transmission did not improve easily. Nodes cannot easily save energy when transmitting data, which results in reduced network lifetime. On other hand, the reduction in the packet delivery ratio affects the overall throughput of the network. Anevolutionary technique based on geographical routing technology is introduced in this work to address the above limitations in current methods. To adopt evolutionary algorithms on Geographic Routing Protocol (GRP) to find optimal routing paths with reduced energy consumption and increased network lifetime. The work also carries out effective ways to avoid latency in delivering the packets from source to destination nodes.Keywords
MANET, Geographic Routing Protocol, Evolutionary Technique, OptimizationFull Text
References
- Al-Mashaqbeh, G. A., Al-Karaki, J. N., AlRousan, M., Raza, A., Abbas, H., & Pasha, M. (2018). Joint Geographic and Energy-aware Routing Protocol for Static and Mobile Wireless Sensor Networks. Adhoc& Sensor Wireless Networks, 41.
- Anand, N., Varma, S., Sharma, G., &Vidalis, S. (2018). Enhanced reliable reactive routing (ER3) protocol for multimedia applications in 3D wireless sensor networks. Multimedia Tools and Applications, 77(13), 16927-16946.
- Benzerbadj, A., Kechar, B., Bounceur, A., & Pottier, B. (2018). Cross-Layer Greedy positionbased routing for multihop wireless sensor networks in a real environment. Ad Hoc Networks, 71, 135-146.
- Chikh, A., &Lehsaini, M. (2018). Multipath routing protocols for wireless multimedia sensor networks: a survey. International Journal of Communication Networks and Distributed Systems, 20(1), 60-81.
- Gao, Z. M., & Zhao, J. (2019). An Improved Grey Wolf Optimization Algorithm with Variable Weights. Computational Intelligence and Neuroscience, 2019.
- Hadi, K. (2019). Analysis of Exploiting Geographic Routing for Data Aggregation in Wireless Sensor Networks. Procedia Computer Science, 151, 439-446.
- Hao, K., Shen, H., Liu, Y., Wang, B., & Du, X. (2018). Integrating localization and energyawareness: A novel geographic routing protocol for underwater wireless sensor networks. Mobile Networks and Applications, 23(5), 1427-1435.
- Hao, K., Shen, H., Liu, Y., Wang, B., & Du, X. (2018). Integrating localization and energyawareness: A novel geographic routing protocol for underwater wireless sensor networks. Mobile Networks and Applications, 23(5), 1427-1435.
- Lu, T., Chang, S., & Li, W. (2018). Fog computing enabling geographic routing for urban area vehicular network. Peer-to-Peer Networking and Applications, 11(4), 749- 755.
- Manjunath, D. R., &Thimmaraju, S. N. (2019). A Path Blind Approach to Secure Geographical Routing in Energy Aware Wireless Sensor Networks. Journal of Computational and Theoretical Nanoscience, 16(5-6), 2555-2566.
- Qureshi, T. N., & Javaid, N. (2018, December). Enhanced adaptive geographic opportunistic routing with interference avoidance assisted with mobile sinks for underwater wireless sensor network. In 2018 International Conference on Frontiers of Information Technology (FIT) (pp. 367- 372). IEEE.
- Majid Alotaibi. 2022. Geographic routing in mobile ad hoc networks (MANET) using hybrid optimization model: a multiobjective perspective. Applied Intelligence 53, 9 (May 2023), 11214–11228. https://doi.org/10.1007/s10489-022-03885- 7
- Agrawal, Dr & Kapoor, Dr Monit. (2021). A comparative study on geographic‐based routing algorithms for flying ad‐hoc networks. Concurrency and Computation Practice and Experience. 33. 10.1002/cpe.6253.
- Linqi Li, Xiaoyin Wang, Xinhua Ma, Design of a location-based opportunistic geographic routing protocol, Computer Communications, Volume 181, 2022, Pages 357- 364,https://doi.org/10.1016/j.comcom.2021 .10.030
- Hamdy H. El-Sayed(2023). Minimizing Energy Hole Problem Comparisons in some Hierarchical WSN Routing Protocols, International Journal of Advanced Networking and Applications, Volume 15 Issue 5, Pages 5590-5595, https://doi.org/10.35444/IJANA.2023.1450 2