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Periyakaruppan, K.
- Improved Energy Efficient Clustering In Manets Using Metaheuristic Optimization
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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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- 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.
- Optimization Of Manet With Mimo For Forest Application Using Advanced Antenna Models
Abstract Views :138 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, SNS College of Engineering, IN
2 Department of Computer Science and Engineering, St. Joseph Institute of Technology, IN
3 Department of Computer Science and Engineering, SNS College of Technology, IN
4 Department of Information Technology, Karpagam Institute of Technology, IN
1 Department of Computer Science and Engineering, SNS College of Engineering, IN
2 Department of Computer Science and Engineering, St. Joseph Institute of Technology, IN
3 Department of Computer Science and Engineering, SNS College of Technology, IN
4 Department of Information Technology, Karpagam Institute of Technology, IN
Source
ICTACT Journal on Microelectronics, Vol 8, No 1 (2022), Pagination: 1318-1322Abstract
In this proposed work, the impulse response of the channel was measured to estimate the PL of an ad hoc network with more than one antenna in different environments, such as indoors, outdoors, in a forest, or in a combination of propagation environments. Some of these places are: Also, a brand-new way to find a PL path with the lowest cost is suggested and put into action. Assuming that all communication nodes are within the network's transmission range, the suggested method can find the path in the network that loses the least amount of data. But this is seen as a problem, and it is planned that it will be fixed in our next project. So, based on the results, the proposed protocol is made and tested to make sure that communication goes through a secure route with the least amount of packet loss. Along with the transmission range limit, the power limit would also need to be worked on in the future because it is thought to be another important part of mobile ad hoc networks. Another important part of mobile ad hoc networks is how much energy they need. MANET runs on batteries, so energy, or power, is one of the most important parts of how it works.Keywords
Antenna, Loss Pattern Analysis, MIMO, MANETsReferences
- David Stuart Muirhead, Muhammad Ali Imran and Kamran Arshad, “A Survey of the Challenges, Opportunities and Use of Multiple Antennas in Current and Future 5G Small Cell Substations”, IEEE Transactions on Antennas and Propagation, Vol. 4, No. 1, pp. 2952-2964, 2016.
- Corbett Rowell and Edmund Y. Lam, “Mobile-Phone Antenna Design”, IEEE Antennas and Propagation Magazine, Vol. 54, No. 4, pp. 14-34, 2012.
- Yu-Shin Wang, Ming- Chou Lee and Shyh-Jong, “Two PIFA- Related Miniaturized Dual Band Antennas”, IEEE Transaction on Antennas and Propagation, Vol. 55, No. 3, pp. 805-811, 2007.
- Nariman Firoozy and Mahmoud Shirazi, “Planar Inverted-F Antenna (PIFA) Design Dissection for Cellular Communication Application”, Journal of Electromagnetic Analysis and Applications, Vol. 3, No. 1, pp. 406-411, 2011.
- H.T. Chattha, Y. Huang, M.K. Ishfaq and S.J. Boyes, “Bandwidth Enhancement Techniques for Planar Inverted F Antenna”, IET Microwaves, Antennas and Propagation, Vol. 5, No. 15, pp. 1872-1879, 2011.
- Dalia Mohammaed Nashaat, Hala K. Elsadek and Hani Ghali, “Single Feed Compact Quad-Band PIFA Antenna for Wireless applications”, IEEE Transactions on Antennas and Propagation, Vol. 53, No. 8, pp. 2631-2625, 2005.
- Saqer Al Jaafreh, Yi Huang and Lei Xing, “Low Profile and Wideband Planar Inverted-F Antenna with Polarisation and Pattern Diversities”, IET Microwaves, Antennas and Propagation, Vol. 10, No. 2, pp. 152-161, 2016.
- Hassan Tariq Chattha, Yi Huang, Stephen J. Boyes and Xu Zhu, “Polarization and Pattern Diversity-Based Dual-Feed Planar Inverted-F Antenna”, IEEE Transactions on Antennas and Propagation, Vol. 60, No. 3, pp. 1532-1540, 2012.
- A. Jain, P.K. Verma and V.K. Singh, “Performance Analysis of PIFA based 4×4 MIMO Antenna”, Electronics Letters, Vol. 48, No. 9, pp. 1-7, 2012.
- S. Chowdhuri, P. Banerjee and S.S. Chaudhuri, “Relay Node Selection Algorithm Consuming Minimum Power of MIMO Integrated MANET”, Advances in Computational Design, Vol. 3, No. 2, pp. 191-200, 2018.
- G.N. Anil and A.V. Reddy, “Optimized Energy Efficient Routing protocol (OEER) in Mobile Ad-hoc Network”, International Journal of Engineering Research and Technology, Vol. 4, pp. 581-587, 2015.
- P. Adasme and L. Szczecinski, “A New Detection Algorithm for Manets based on Product Trellises using Space Time Trellis Codes”, WSEAS Transactions on Mathematics, Vol. 6, No. 2, pp. 348-355, 2007.
- 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
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- 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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- Detection of Intrusion in Wireless Sensor Networks Using AI Approach
Abstract Views :147 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science and Engineering, SNS College of Engineering, IN
2 Department of Computer Science and Engineering, SNS College of Technology, IN
3 Department of Information Technology, Karpagam Institute of Technology, IN
4 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 Computer Science and Engineering, SNS College of Technology, IN
3 Department of Information Technology, Karpagam Institute of Technology, IN
4 Department of Computer Science and Engineering, St Joseph Institute of Technology, IN
Source
ICTACT Journal on Communication Technology, Vol 13, No 3 (2022), Pagination: 2763-2766Abstract
We were able to solve the problem of discovering K-barriers with interpretability concerns for use in intrusion detection by collecting data from wireless sensor networks. This gave us the information we needed to find a solution. This paper contains the solution that we came up with. As a result of the results of the suggested model in the paper being assertive and interpretable in this context, vital information pertaining to the matter that was being researched could be gathered. The results were obtained through the process of showing how the model can be interpreted. The challenge is trying to figure out how many barriers are required for adequate territorial defense. It is therefore possible to bring the expenses involved with anticipating and installing equipment in these regions down to a level that is more tolerable. The approach that has been offered provides a fresh perspective on the nature of the underlying issue behavior and is expected to be of assistance in the distribution of relevant data and discoveries. Constructing expert systems that are relevant to the subject matter is doable if one makes use of these fuzzy principles.Keywords
Intrusion Detection, Wireless Sensor Networks, Model InterpretabilityReferences
- T.M. Ghazal, “Data Fusion-based Machine Learning Architecture for Intrusion Detection”, Computers, Materials and Continua, Vol. 70, No. 2, pp. 3399-3413, 2022.
- N.M. Alruhaily and D.M. Ibrahim, “A Multi-Layer Machine Learning-based Intrusion Detection System for Wireless Sensor Networks”, International Journal of Advanced Computer Science and Applications, Vol. 12, No. 4, pp. 281-288, 2021.
- M. Maheswari and R.A. Karthika, “A Novel QoS based Secure Unequal Clustering Protocol with Intrusion Detection System in Wireless Sensor Networks”, Wireless Personal Communications, Vol. 118, No. 2, pp. 1535-1557, 2021.
- P. Srividya and A.N. Rao, “A Trusted Effective Approach for Forecasting the Failure of Data Link and Intrusion in Wireless Sensor Networks”, Theoretical Computer Science, Vol. 23, No. 1, pp .1-13, 2022.
- M. Imran and S. Anwar, “Intrusion Detection in Networks using Cuckoo Search Optimization”, Soft Computing, Vol. 89, pp. 1-13, 2022.
- H.O. Ahmed, “17.16 GopsW Sustainable FLS-Based Wireless Sensor Network for Surveillance System using FPGA”, Proceedings of International Conference on Integrated Communications Navigation and Surveillance, pp. 1-10, 2021.
- R. Indhumathi, K. Amuthabala and G. Kiruthiga, “Design of Task Scheduling and Fault Tolerance Mechanism Based on GWO Algorithm for Attaining Better QoS in Cloud System”, Wireless Personal Communications, Vol. 132, pp. 1-19, 2022.
- P. Joshi and A.S. Raghuvanshi, “Hybrid Approaches to Address Various Challenges in Wireless Sensor Network for IoT Applications: Opportunities and Open Problems”, International Journal of Computer Networks and Applications, Vol. 8, No. 3, pp. 151-187, 2021.
- A. Singh and C.C. Lee, “LT-FS-ID: Log-Transformed Feature Learning and Feature-Scaling-based Machine Learning Algorithms to Predict the K-Barriers for Intrusion Detection using Wireless Sensor Network”, Sensors, Vol. 22, No. 3, pp. 1070-1087, 2022.
- M. Mounica, R. Vijayasaraswathi and R. Vasavi, “Detecting Sybil Attack in Wireless Sensor Networks using Machine Learning Algorithms”, IOP Conference Series: Materials Science and Engineering, Vol. 1042, No. 1, pp. 1-12, 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.
- A.S.M. Htet and H.J. Lee, “TripletGAN VeinNet: Palm Vein Recognition Based on Generative Adversarial Network and Triplet Loss”, Proceedings of International Conference on Computer Engineering and Artificial Intelligence, pp. 454-458, 2021.
- S. Athisayamani, A. Robert Singh and A. Sivanesh Kumar, “Recurrent Neural Network-Based Character Recognition System for Tamil Palm Leaf Manuscript using Stroke Zoning”, Proceedings of International Conference on Inventive Communication and Computational Technologies, pp. 165-176, 2021.
- X. Liang and D. Zhang, “CompNet: Competitive Neural Network for Palmprint Recognition Using Learnable Gabor Kernels”, IEEE Signal Processing Letters, Vol. 28, pp. 1739-1743, 2021.
- K. Zhang and Tao, “Class Constraint-based Discriminative Features Learning Algorithm for Palm Print and Palm Vein Fusion Recognition”, Proceedings of International Conference on Signal and Image Processing, pp. 275-280, 2022.
- G. Ananthi and S. Arivazhagan, “Human Palm Vein Authentication using Curvelet Multiresolution Features and Score Level Fusion”, The Visual Computer, Vol. 38, No. 6, pp. 1901-1914, 2022.