Refine your search
Collections
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Yuvaraj, B.
- Enhanced Analysis of Blockchain Based Security Systems In Financial Institutions
Abstract Views :86 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, Karpaga Vinayaga College of Engineering and Technology, India., IN
2 Department of Computer Science and Engineering, Sphoorthy Engineering College, India., IN
3 Department of Master of Computer Application, Thirumalai Engineering College, India., IN
4 Department of Computer Science and Engineering, Thirumalai Engineering College, India., IN
1 Department of Computer Science and Engineering, Karpaga Vinayaga College of Engineering and Technology, India., IN
2 Department of Computer Science and Engineering, Sphoorthy Engineering College, India., IN
3 Department of Master of Computer Application, Thirumalai Engineering College, India., IN
4 Department of Computer Science and Engineering, Thirumalai Engineering College, India., IN
Source
ICTACT Journal on Communication Technology, Vol 14, No 1 (2023), Pagination: 2861-2867Abstract
The blockchain technology has provided a revolutionary way of secure data storage and transfer. It is a distributed ledger technology (DLT) that creates a secure and immutable record of transactions. This technology has been used to create secure and reliable systems for financial institutions. The blockchain based security system provides a secure platform for financial institutions. It ensures that the transactions are secure and immutable. This technology has been used to create digital assets that can be securely stored and transferred. It also ensures that the transactions are transparent and secure. The blockchain technology can be used to enable secure transactions between financial institutions. By using the blockchain, financial institutions can create a secure platform for transactions. This will ensure that the transactions are secure and immutable. It also ensures that the transactions are transparent and secure. The blockchain technology can also be used to create a secure system for digital identity management. This will ensure that the digital identities of the users are secure and immutable. It also ensures that the users’ identity is protected and secure. The blockchain technology can also be used to create a secure platform for digital payments. This will ensure that the payments are secure and immutable. It also ensures that the payments are transparent and secure.Keywords
Blockchain, Secure, Data Storage,Technology,Transactions, Financial Institutions.References
- A.S. Hosen, S. Singh, P.K. Sharma and G.H. Cho, “Blockchain-Based Transaction Validation Protocol for a Secure Distributed IoT Network”, IEEE Access, Vol. 8, pp. 117266-117277, 2020.
- S.M.H. Bamakan, A. Motavali and A.B. Bondarti, “A Survey of Blockchain Consensus Algorithms Performance Evaluation Criteria”, Expert Systems with Applications, Vol. 154, pp. 1-19, 2020.
- S. Trivedi and R. Sharma, “Systematic Literature Review on Application of Blockchain Technology in E-Finance and Financial Services”, Journal of Technology Management and Innovation, Vol. 16, No. 3, pp. 89-102, 2021.
- S.B. Patel and N. Kumar, “Kirti: A Blockchain-Based Credit Recommender System for Financial Institutions”, IEEE Transactions on Network Science and Engineering, Vol. 8, No. 2, pp. 1044-1054, 2020.
- N. Kabra and S. Tyagi, “MudraChain: Blockchain-based Framework for Automated Cheque Clearance in Financial Institutions”, Future Generation Computer Systems, Vol. 102, pp. 574-587, 2020.
- K. Fanning and D.P. Centers, “Blockchain and its Coming Impact on Financial Services”, Journal of Corporate Accounting and Finance, Vol. 27, No. 5, pp. 53-57, 2016.
- M. Peterson, “Blockchain and the Future of Financial Services”, The Journal of Wealth Management, Vol. 21, No. 1, pp. 124-131, 2018.
- Y. Guo and C. Liang, “Blockchain Application and Outlook in the Banking Industry”, Financial Innovation, Vol. 2, pp. 1-12, 2016.
- H. Rathore, A. Mohamed and M. Guizani, “A Survey of Blockchain Enabled Cyber-Physical Systems”, Sensors, Vol. 20, No. 1, pp. 282-291, 2020.
- N. Jiwani and K. Gupta, “Exploring Business Intelligence Capabilities for Supply Chain: A Systematic Review”, Transactions on Latest Trends in IoT, Vol. 1, No. 1, pp. 1- 10, 2018.
- B. Gobinathan, M.A. Mukunthan, S. Surendran, and V.P. Sundramurthy, “A Novel Method to Solve Real Time Security Issues in Software Industry using Advanced Cryptographic Techniques”, Scientific Programming, Vol. 2021, pp. 1-7, 2021.
- N. Jiwani and K. Gupta, “Comparison of Various Tools and Techniques used for Project Risk Management”, International Journal of Machine Learning for Sustainable Development, Vol. 1, No. 1, pp. 51-58, 2019.
- R. Chaudhary, A. Jindal, G.S. Aujla and K.K.R. Choo, “Best: Blockchain-Based Secure Energy Trading in SDNEnabled Intelligent Transportation System”, Computers and Security, Vol. 85, pp. 288-299, 2019.
- Design of Efficient Routing Paths Using Similarity Estimation Based Stochastic Gradient Descent in Wireless Sensor Network
Abstract Views :34 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science and Engineering, Kings Engineering College, IN
1 Department of Computer Science and Engineering, Kings Engineering College, IN
Source
ICTACT Journal on Communication Technology, Vol 14, No 3 (2023), Pagination: 2988-2991Abstract
Wireless Sensor Networks (WSNs) offer versatile deployment options, particularly in battery-powered scenarios, addressing energy consumption concerns among sensor nodes. However, the data-intensive nature of WSNs poses challenges in routing, particularly in maintaining balanced paths while accommodating rapid data acquisition. This paper presents an innovative approach called Similarity Estimation-Based Stochastic Gradient Descent (SESGD) routing for WSNs, designed to establish stable routing paths that align with the speed of data acquisition. Sensor nodes play a crucial role in data collection and acquisition, while WSNs facilitate data routing through multiple hops from source to sink nodes. SESGD effectively manages data routing, synchronizing it with data acquisition rates, thereby ensuring network stability. Simulation results assess key performance metrics, including average delay, throughput, and network energy efficiency. The findings demonstrate that the proposed machine learning method outperforms existing algorithms, achieving superior network throughput.Keywords
Machine Learning, WSN, Stochastic Gradient, Routing, Energy Efficiency.References
- V. Saravanan and R. Rajkumar, “Secure Source-Based Loose RSA Encryption for Synchronization (SSOBRSAS) and Evolutionary Clustering Based Energy Estimation for Wireless Sensor Networks”, International Journal of Advanced Research in Computer Science, Vol. 5, No. 5, pp. 1-12, 2014.
- R. Sabitha and V. Saravanan, “Network Based Detection of IoT Attack using AIS-IDS Model”, Wireless Personal Communications, Vol. 128, No. 3, pp. 1543-1566, 2023.
- 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.
- K. Praghash and A.A. Stonier, “An Artificial Intelligence Based Sustainable Approaches-IoT Systems for Smart Cities”, Springer, 2023
- H. AlAamri, M. Abolhasan and T. Wysocki, “On Optimizing Route Discovery in Absence of Previous Route Information in MANETs”, Proceedings of IEEE International Conference on Vehicular Technology, pp. 1-5, 2009.
- X.M. Zhang, E.B. Wang, J.J. Xia and D.K. Sung, “An Estimated Distance based Routing Protocol for Mobile Ad Hoc Networks”, IEEE Transactions on Vehicular Technology, Vol. 60, No. 7, pp. 3473-3484, 2011.
- Y. Liu, K. Ota, K. Zhang, M. Ma and N. Xiong, “QTSAC: An Energy-Efficient MAC Protocol for Delay Minimization in Wireless Sensor Networks”, IEEE Access, Vol. 6, pp. 8273-8291, 2018.
- X. Deng, Z. Tang, L.T. Yang, M. Lin and B. Wang, “Confident Information Coverage Hole Healing in Hybrid Industrial Wireless Sensor Networks”, IEEE Transactions on Industrial Informatics, Vol. 14, No. 5, pp. 2220-2229, 2017.
- L. Kong, J.S. Pan and V. Snasel, “An Energy-Aware Routing Protocol for Wireless Sensor Network based on Genetic Algorithm”, Telecommunication Systems, Vol. 67, No. 3, pp. 451-463, 2018.
- A. Agrawal, V. Singh, S. Jain and R.K. Gupta, “GCRP: Grid-Cycle Routing Protocol for Wireless Sensor Network with Mobile Sink”, AEU-International Journal of Electronics and Communications, Vol. 94, No. 2, pp. 1-11, 2018.
- T.E. Bogale and L. Vandendorpe, “Max-Min SNR Signal Energy based Spectrum Sensing Algorithms for Cognitive Radio Networks with Noise Variance Uncertainty”, IEEE Transactions on Wireless Communications, Vol. 13, No. 1, pp. 280-290, 2014.
- D.M. Martinez and A.G. Andrade, “On the Reduction of the Noise Uncertainty Effects in Energy Detection for Spectrum Sensing in Cognitive Radios”, Proceedings of IEEE Annual International Symposium Personal, Indoor and Mobile Radio Communications, pp. 1975-1979, 2014.
- K.M. Thilina, K.W. Choi, N. Saquib and E. Hossain, “Pattern Classification Techniques for Cooperative Spectrum Sensing in Cognitive Radio Networks: SVM and W-KNN Approaches”, Proceedings of IEEE International Conference on Global Communications, pp. 1260-1265, 2012.
- Soumitra Das and Sanjeev Wagh, “Prolonging the Lifetime of the Wireless Sensor Network Based on Blending of Genetic Algorithm and Ant Colony Optimization”, Journal of Green Engineering, Vol. 4, No. 3, pp. 245-260, 2015.
- Mehr Moslem Afrashteh, “Design and Implementation a New Energy Efficient Clustering Algorithm using Genetic Algorithm for Wireless Sensor Networks”, World Academy of Science, Engineering and Technology, Vol. 5, No. 4, pp. 373-376, 2011.