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Periyakaruppan, K.
- Improved Energy Efficient Clustering In Manets Using Metaheuristic Optimization
Abstract Views :234 |
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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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- Elimination of Data Modification in Sensor Nodes of WSN Using Deep Learning Model
Abstract Views :73 |
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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- 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.
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- 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.
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- Detection of Intrusion in Wireless Sensor Networks Using AI Approach
Abstract Views :81 |
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.
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