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Chellapraba, B.
- Improved Energy Efficient Clustering In Manets Using Metaheuristic Optimization
Abstract Views :285 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, SNS College of Engineering, IN
2 Department of Information Technology, Karpagam Institute of Technology, IN
3 Department of Computer Science and Engineering, St. Joseph Institute of Technology, IN
1 Department of Computer Science and Engineering, SNS College of Engineering, IN
2 Department of Information Technology, Karpagam Institute of Technology, IN
3 Department of Computer Science and Engineering, St. Joseph Institute of Technology, IN
Source
ICTACT Journal on Communication Technology, Vol 13, No 1 (2022), Pagination: 2616-2620Abstract
A mobile ad hoc network (MANET) is a wireless network created by a radio terminal built into a mesh topology. Each node is deflected with respect to the other nodes. The Temporary networks can self-heal and automatically redirect back around a lost node. There is an MANET networks require different network layout protocols, such as remote hierarchical remote vector navigation, associative-based navigation, ad-assigning remote vector navigation, and dynamic source navigation. An improved meta-heuristic optimization algorithm is proposed in this paper. It works in conjunction with an autonomous mobile network to increase its power and efficiency. It can transmit large amounts of data using less power and its bandwidth speed increases as its data transfer speed increases. This will improve the amount of service generated there and the users can satisfy the provided services.Keywords
MANET, Mesh Topology, Autonomous Mobile Network, Energy Efficiency
References
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- M.U. Bokhari, H.S.A. Hamatta and S.T. Siddigui, “A Review of Clustering Algorithms as Applied in MANETs”, International Journal Advanced Research in Computer Science and Software Engineering, Vol. 2, No. 11, pp. 364369, 2012.
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- S. Jayaraman and U. Mohanakrishnan, “A Trusted Water Fall Model for Efficient Data Transmission in VANET”, Wireless Personal Communications, Vol. 89, pp. 1-28, 2021.
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- P. Krishan, “A Study on Dynamic and Static Clustering Based Routing Schemes for Wireless Sensor Networks”, International Journal of Modern Engineering Research, Vol. 3, No. 2, pp. 1100-1104, 2013.
- M. Malik and Y. Singh, “Analysis of LEACH Protocol in Wireless Sensor Networks”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 3, No. 2, pp. 178-184, 2013.
- A.P. Singh, N. Sharma and N.R Roy, “Residual Energy and Distance based Energy-Efficient Communication Protocol for Wireless Sensor Network”, International Journal of Computer Applications, Vol. 74, No. 12, pp. 11-16, 2013.
- S. Soni and B. Dey, “Dynamic Selection of Cluster Head in Cluster of Cluster Heads within the Cluster in Heterogeneous Wireless Sensor Network”, Proceedings of IEEE International Conference on Advanced Communications, Control and Computing Technologies, pp. 877-881, 2014.
- S. Boopalan and S. Jayasankari, “Dolphin Swarm Inspired Protocol (DSIP) for Routing in Underwater Wireless Sensor Networks”, International Journal of Computer Networks and Applications, Vol. 8, No. 1. 1, pp. 44-52, 2021.
- K. Singh and R. Gupta, “Performance Evaluation of a MANET Based Secure and Energy Optimized Communication Protocol (E2S-AODV) for Underwater Disaster Response Network”, International Journal of Computer Networks and Applications, Vol. 8, No. 1, pp. 1127. 2021.
- 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, pp. 1-16, 2021.
- K. Haseeb, N. Abbas, M.Q. Saleem and T. Salam, “RCER: Reliable Cluster-Based Energy-Aware Routing Protocol for Heterogeneous Wireless Sensor Networks”, PLoS ONE, Vol. 14, No. 9, pp. 1-24, 2019.
- A. Hassan, A. Anter and M. Kayed, “A Robust Clustering Approach for Extending the Lifetime of Wireless Sensor Networks in an Optimized Manner with a Novel Fitness Function”, Sustainable Computing: Informatics and Systems, Vol. 30, pp. 1-10, 2021.
- N. Arivazhagan, K. Somasundaram, D. Vijendra Babu and V. Prabhu Sundramurthy, “Cloud-Internet of Health Things (IOHT) Task Scheduling using Hybrid Moth Flame Optimization with Deep Neural Network Algorithm for E Healthcare Systems”, Scientific Programming, Vol. 2022, pp. 1-8, 2022.
- A Fuzzy Based Deep Learning Model to Identify the Pattern Recognition for Licensed Plates in Smart Vehicle Management System
Abstract Views :309 |
PDF Views:132
Authors
Affiliations
1 Department of Information Technology, Karpagam Institute of Technology, IN
2 Department of Computer Science and Engineering, St. Joseph’s Institute of Technology, IN
3 Department of Computer Science and Engineering, SNS College of Engineering,, IN
1 Department of Information Technology, Karpagam Institute of Technology, IN
2 Department of Computer Science and Engineering, St. Joseph’s Institute of Technology, IN
3 Department of Computer Science and Engineering, SNS College of Engineering,, IN
Source
ICTACT Journal on Soft Computing, Vol 12, No 3 (2022), Pagination: 2614-2618Abstract
In general, vehicle management is based on the proper maintenance and safety of a vehicle. Based on this the quality of the vehicle is calculated. Most of the older vehicles are currently of poor quality and are producing high levels of pollution. Thus, it is necessary to find information about those vehicles. The number plate is helpful to find the information about the vehicle. In this paper, the number blood detection method is proposed. It is based on the fuzzy model and developed in the way of deep learning. Its main purpose is to provide accurate vehicle details from a given set of data. It has also been upgraded to provide its safety measures to its owner based on the vehicle data. Thus, this proposed model prevents major accidents. These functions can also be very helpful in recovering vehicles based on data from stolen vehicles.Keywords
Vehicle Management, Fuzzy Model, Deep Learning, Number PlateFull Text
References
- J. Shashirangana, H. Padmasiri and C. Perera, “Automated License Plate Recognition: A Survey on Methods and Techniques”, IEEE Access, Vol. 9, pp. 11203-11225, 2020.
- L. Zheng, T. Sayed and F. Mannering, “Modeling Traffic Conflicts for Use in Road Safety Analysis: A Review of Analytic Methods and Future Directions”, Analytic Methods in Accident Research, Vol. 29, pp. 1-13, 2020.
- T. Karthikeyan 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.
- 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. 121, No. 3, pp. 1-16, 2021.
- B. Bhushan, S. Singh and R. Singla, “License Plate Recognition System using Neural Networks and Multithresholding Technique”, International Journal of Computer Applications, Vol. 84, No. 5, pp. 45-50, 2013.
- R.A. Raja, T. Karthikeyan and K. Praghash, “Improved Authentication in Secured Multicast Wireless Sensor Network (MWSN) Using Opposition Frog Leaping Algorithm to Resist Man-in-Middle Attack”, Wireless Personal Communications, Vol. 121, No. 1, pp. 1-17, 2021.
- A.S. Kumar, L.T. Jule and A.H. Gandomi, “Analysis of False Data Detection Rate in Generative Adversarial Networks using Recurrent Neural Network”, Academic Press, pp. 289-312, 2021.
- A. Puranic, K. Deepak and V. Umadevi, “Vehicle Number Plate Recognition System: A Literature Review and Implementation using Template Matching”, International Journal of Computer Applications, Vol. 134, No. 1, pp. 12-16, 2016.
- R.A. Raja, T. Karthikeyan and K. Praghash, “An Investigation of Garbage Disposal Electric Vehicles (GDEVs) Integrated with Deep Neural Networking (DNN) and Intelligent Transportation System (ITS) in Smart City Management System (SCMS)”, Wireless Personal Communications, Vol. 121, No. 4, pp. 1-20, 2021.
- Y.T. Chen, J.H. Chuang and H.T. Chen, “Robust License Plate Detection in Nighttime Scenes using Multiple Intensity Ir-Illuminator”, Proceedings of IEEE International Symposium on Industrial Electronics, pp 893-898, 2012.
- H. Vashishtha, G. Sharma and A.M. Tripathi, “Vehicle Owner Identification from Number Plate”, Proceedings of International Conference on Recent Trends in Computing, pp. 131-138, 2022.
- Y. Wen, Y. Lu, J. Yan and P. Shi, “An Algorithm for License Plate Recognition Applied to Intelligent Transportation System”, IEEE Transactions on Intelligent Transportation Systems, Vol. 12, No. 3, pp. 830-845, 2011.
- F. Ullah and D. Kwak, “Barrier Access Control using Sensors Platform and Vehicle License Plate Characters Recognition”, Sensors, Vol. 19, No. 13, pp. 3015-3024, 2019.
- S. Anekar, S. Yeginwar and H. Sonune, “Automated Gate System using Number Plate Recognition (NPR)”, Proceedings of International Conference on ICT Systems and Sustainability, pp. 413-420, 2022.
- Elimination of Data Modification in Sensor Nodes of WSN Using Deep Learning Model
Abstract Views :133 |
PDF Views:1
Authors
Affiliations
1 Department of Information Technology, Karpagam Institute of Technology, IN
2 Department of Computer Science & Engineering, SNS College of Technology, IN
3 Department of Computer Science and Engineering, St. Joseph’s Institute of Technology, IN
1 Department of Information Technology, Karpagam Institute of Technology, IN
2 Department of Computer Science & Engineering, SNS College of Technology, IN
3 Department of Computer Science and Engineering, St. Joseph’s Institute of Technology, IN
Source
ICTACT Journal on Communication Technology, Vol 13, No 2 (2022), Pagination: 2712-2717Abstract
This study focuses on removing the possibility of malicious data manipulation in wireless sensor networks (WSN) by utilising a deep learning method. When training deep neural networks, datasets that have been the subject of an attack that alters the data are used as the building blocks. This is done in preparation for putting the networks to the test in the real world. We find out through simulation with a 70:30 cross-validation across a 10-fold sample size that the proposed technique is superior to the current state of the art in terms of the packet delivery rate, latency and throughput.Keywords
Deep Learning Model, Data Modification, WSN, ThroughputReferences
- S. Sundaram, M. Pajic and G.J. Pappas, “The Wireless Control Network: Monitoring for Malicious Behavior”, Proceedings of IEEE Conference on Decision and Control, pp. 5979-5984, 2010.
- L.M. Abdulrahman and K.H. Sharif, “A State of Art for Smart Gateways Issues and Modification”, Asian Journal of Research in Computer Science, Vol. 63, pp. 1-13, 2021.
- Z.H. Pang and G.P. Liu, “Detection of Stealthy False Data Injection Attacks against Networked Control Systems via Active Data modification”, Information Sciences, Vol. 546, pp. 192-205, 2021.
- M. Soni and D.K. Singh, “LAKA: Lightweight Authentication and Key Agreement Protocol for Internet of Things based Wireless Body Area Network”, Wireless Personal Communications, Vol. 122, 1-18, 2021.
- T. Karthikeyan and K. Praghash, “Improved Authentication in Secured Multicast Wireless Sensor Network (MWSN) using Opposition Frog Leaping Algorithm to Resist Man-in-Middle Attack”, Wireless Personal Communications, Vol. 123, No. 2, pp. 1715-1731, 2022.
- T.H. Hadi, “Types of Attacks in Wireless Communication Networks”, Webology, Vol. 19, No. 1, pp. 1-13, 2022.
- R. Rajendran, “An Optimal Strategy to Countermeasure the Impersonation Attack in Wireless Mesh Network”, International Journal of Information Technology, Vol. 13, No. 3, pp. 1033-1038, 2021.
- X.S. Shen and B. Ying, “Data Management for Future Wireless Networks: Architecture, Privacy Preservation, and Regulation”, IEEE Network, Vol. 35, No. 1, pp. 8-15, 2021.
- M. Ponnusamy, P. Bedi and T. Suresh, “Design and Analysis of Text Document Clustering using Salp Swarm Algorithm”, The Journal of Supercomputing, Vol. 87, pp. 1-17, 2022.
- Z. Bin and H. Jian Feng, “Design and Implementation of Incremental Data Capturing in Wireless Network Planning based on Log Mining”, Proceedings of International Conference on Advanced Information Technology, Electronic and Automation Control, pp. 2757-2761, 2021.
- D. Dilmurod, S. Norkobilov and I. Jamshid, “Features of Using the Energy-Saving LEACH Protocol to Control the Temperature of Stored Cotton Piles via a Wireless Network of Sensors”, International Journal of Discoveries and Innovations in Applied Sciences, Vol. 1, No. 5, pp. 278-283, 2021.
- W. Sun and Y. Gao, “The Design of University Physical Education Management Framework based on Edge Computing and Data Analysis”, Wireless Communications and Mobile Computing, Vol. 122, pp. 1-16, 2021.
- H. Khalid, S.J. Hashim and M.A. Chaudhary, “Cross-SN: A Lightweight Authentication Scheme for a Multi-Server Platform using IoT-Based Wireless Medical Sensor Network”, Electronics, Vol. 10, No. 7, pp. 790-810, 2021.
- A.N. Kadhim and S.B. Sadkhan, “Security Threats in Wireless Network Communication-Status, Challenges, and Future Trends”, Proceedings of International Conference on Advanced Computer Applications, pp. 176-181, 2021.
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- Palm Vein Classification from Large Datasets Using Deep Convolutional Fusion Learning
Abstract Views :126 |
PDF Views:1
Authors
Affiliations
1 Department of Information Technology, Karpagam Institute of Technology, IN
2 Department of Computer Science and Engineering, SNS College of Engineering, IN
3 Department of Computer Science and Engineering, St. Joseph’s Institute of Technology, IN
1 Department of Information Technology, Karpagam Institute of Technology, IN
2 Department of Computer Science and Engineering, SNS College of Engineering, IN
3 Department of Computer Science and Engineering, St. Joseph’s Institute of Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 1 (2022), Pagination: 2802-2805Abstract
Biometric techniques are currently among the most widely used methods all over the world for determining a person identity. This trend is expected to continue in the near future. In this study, we focused on palm vein is used as a strategy to improve biometric authentication systems by combining a method that is based on texture with a method that is based on a convolutional neural network (CNN). The simulation is used to test the performance of the model on several different datasets. In simulations, the suggested method routinely achieves better results than the current best practice on each and every dataset.Keywords
Palm Vein, Classification, Convolutional Neural Network.References
- Y.Y. Fanjiang, “Palm Vein Recognition based on Convolutional Neural Network”, Informatica, Vol. 32, No. 4, pp. 687-708, 2021.
- S. Li and B. Zhang, “Joint Discriminative Sparse Coding for Robust Hand-Based Multimodal Recognition”, IEEE Transactions on Information Forensics and Security, Vol. 16, pp. 3186-3198, 2021.
- R. Hernandez-Garcia and N. Guil, “Large-Scale Palm Vein Recognition on Synthetic Datasets”, Proceedings of International Conference of the Chilean Computer Science Society, p. 1-8, 2021.
- R.A. Mustafa, “Palm Print Recognition based on Harmony Search Algorithm”, International Journal of Electrical and Computer Engineering, Vol. 11, No. 5, pp. 1-12, 2021.
- H.N. Bendini and D.D.M. Valeriano, “Exploring a Deep Convolutional Neural Network and Geobia for Automatic Recognition of Brazilian Palm Swamps (Veredas) using Sentinel-2 Optical Data”, Proceedings of IEEE International Conference on Geoscience and Remote Sensing, pp. 5401-5404, 2021.
- A.S. Al Jaberi and A.M. Al-juboori, “Palm Vein Recognition based on Convolution Neural Network”, Journal of Al-Qadisiyah for Computer Science and Mathematics, Vol. 13, No. 3, pp. 1-6, 2021.
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