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Kumar, Manoj
- Elliptic Curve Point Multiplication Using MBNR and Point Halving
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Authors
Affiliations
1 Department of Mathematics, Banasthali University, Jaipur-304022, Rajasthan, IN
2 Department of Computer Science, Banasthali University, Jaipur-304022, Rajasthan, IN
3 Department of Computer Science, KNIT, UPTU, Sultanpur-228188, Uttar Pradesh, IN
1 Department of Mathematics, Banasthali University, Jaipur-304022, Rajasthan, IN
2 Department of Computer Science, Banasthali University, Jaipur-304022, Rajasthan, IN
3 Department of Computer Science, KNIT, UPTU, Sultanpur-228188, Uttar Pradesh, IN
Source
International Journal of Advanced Networking and Applications, Vol 3, No 5 (2012), Pagination: 1329-1337Abstract
The fast implementation of elliptic curve cryptosystems relies on the efficient computation of scalar multiplication. As generalization of double base number system of a number k to multi-base number system (MBNR) provides a faster method for the scalar multiplication is most important and costly operation (in terms of time) in ECC, there is always a need of developing a faster method with lower cost. In this paper we optimize the cost of scalar multiplication using halving and add method instead doubling and tripling methods. The cost is reduced from 40% to 50% with respect to the other fastest techniques.Keywords
Double Base Number System, Elliptic Curve Cryptography, Multi-Base Number System, Point Halving, WSN.Full Text
- Randomization of Node Scheme with Optimization in Wireless Sensor Network
Abstract Views :194 |
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Authors
Affiliations
1 Computer Science engineering, IKG Punjab Technical University, Kapurthala 144601,(Punjab), IN
2 Khalsa College of Engineering & Technology, Amritsar 143001, (Punjab), IN
3 DAV institute of Engineering, Management & Technology, Jalandhar 144001 (Punjab), IN
1 Computer Science engineering, IKG Punjab Technical University, Kapurthala 144601,(Punjab), IN
2 Khalsa College of Engineering & Technology, Amritsar 143001, (Punjab), IN
3 DAV institute of Engineering, Management & Technology, Jalandhar 144001 (Punjab), IN
Source
International Journal of Advanced Networking and Applications, Vol 10, No 5 (2019), Pagination: 3999-4006Abstract
Swarm Intelligence has nature inspired intelligence dependent on aggregate conduct of swarms having self-sorted out nature. Different techniques are being planned as far as ACO, PSO, Fish Swarm, Bats Swarm, Bacterial Foraging, TABU search and so forth.TABU search is regarded as heuristic method derived by Glover in 1986 depends on the memory structure. TABU search is used to determine the engineering design problems having continuous and real number variables. TABU is utilized to take care of different discrete issues in various regions of sciences and engineering. Since its advancement TABU has pulled in loads of specialists to take up its calculations and apply to take care of different complex issues and has been demonstrated the best strategy to get enhanced outcomes. The objective of this research paper is to implement TABU search to make the protocol more efficient and effective. This paper proposed MSEEC (multilevel stable and energy efficient clustering protocol) utilizing TABU mechanism in which normal nodes are randomly changed after each round in the territory of 200m×200m.The recreation is done under the MATLAB environment and observed the performance of TABU-MSEEC against MSEEC protocol on 4% increase in the case of first node dead (FND) and 20% increase in the case of last node dead (LND) and average remaining energy is delayed by 12% in advance nodes and 20% in super nodes respectively.Keywords
Wireless Sensor Network, Heterogeneity, TABUmechanism, MATLAB, FND, LND and Average Remaining Energy.Full Text
References
- Farouk, F., Rizk, R., & Zaki, F. W. (2014). Multi-level stable and energy-efficient clustering protocol in heterogeneous wireless sensor networks. IET Wireless Sensor Systems, 4(4), 159-169.
- Wang, X., Qian, L., Wu, J., & Liu, T. (2010). An energy and distance based clustering protocol for wireless sensor networks. In Novel Algorithms and Techniques in Telecommunications and Networking (pp. 409-412). Springer, Dordrecht.
- Li, X., Li, N., Chen, L., Shen, Y., Wang, Z., & Zhu, Z. (2010, March). An improved LEACH for clustering protocols in wireless sensor networks. In 2010 International Conference on Measuring Technology and Mechatronics Automation (Vol. 1, pp. 496-499). IEEE.
- Orojloo, H., & Haghighat, A. T. (2016). A Tabu search based routing algorithm for wireless sensor networks. Wireless Networks, 22(5), 1711-1724.
- Messaoud, R. B., & Ghamri-Doudane, Y. (2015, September). QoI and energy-aware mobile sensing scheme: A tabu-search approach. In 2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall) (pp. 16). IEEE.
- Amuthan, A., & Thilak, K. D. (2016, October). Survey on Tabu search meta-heuristic optimization. In 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES) (pp. 1539-1543). IEEE.
- Habib, S. J., & Marimuthu, P. N. (2017, April). Reputation analysis of sensors’ trust within tabu search. In World Conference on Information Systems and Technologies (pp. 343-352). Springer, Cham.
- Kuo, S. Y., & Chou, Y. H. (2017). Entanglementenhanced quantum-inspired tabu search algorithm for function optimization. IEEE Access, 5, 13236-13252.
- Vijayalakshmi, K., & Anandan, P. (2018). A multi objective Tabu particle swarm optimization for effective cluster head selection in WSN. Cluster Computing, 1-8.
- Glover, F. (1990). Tabu search: A tutorial. Interfaces, 20(4), 74-94.
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- Vaithyanathan, S., Burke, L. I., & Magent, M. A. (1996). Massively parallel analog tabu search using neural networks applied to simple plant location problems. European Journal of Operational Research, 93(2), 317-330.
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- Kinney, G. W., Barnes, J. W., & Colletti, B. W. (2007). A reactive tabu search algorithm with variable clustering for the unicost set covering problem. International Journal of Operational Research, 2(2), 156-172.
- Glover, F., & McMillan, C. (1986). The general employee scheduling problem. An integration of MS and AI. Computers & operations research, 13(5), 563-573. [5] Linn, R., & Zhang, W. (1999). Hybrid flow shop scheduling: a survey. Computers & industrial engineering, 37(1-2), 57-61.
- Glover, F., McMillan, C., & Novick, B. (1985). Interactive decision software and computer graphics for architectural and space planning. Annals of operations research, 5(3), 557-573.
- Hansen, P., Jaumard, B., & Da Silva, E. (1991). Average-linkage divisive hierarchical clustering. Cahiers du GERAD.
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- Glover, F., & Taillard, E. (1993). A user's guide to tabu search. Annals of operations research, 41(1), 1-28.
- Beyer, D. A., & Ogier, R. G. (1991, November). Tabu learning: a neural network search method for solving nonconvex optimization problems. In [Proceedings] 1991 IEEE International Joint Conference on Neural Networks (pp. 953-961). IEEE.
- Ganesh, S., & Amutha, R. (2010). Real Time and Energy Efficient Transport Protocol for Wireless Sensor Networks.International Journal of Advanced Networking and Applications. arXiv preprint arXiv:1006.2691.
- Basavaraj, G. N., & Jaidhar, C. D. (2019). Intersecting Sensor Range Cluster-based Routing Algorithm for Enhancing Energy in WSN. International Journal of Advanced Networking and Applications, 10(4), 3938-3943.
- An Energy-Efficient Routing Protocol based on TABU-Genetic Strategy in Wireless Sensor Network
Abstract Views :197 |
PDF Views:0
Authors
Affiliations
1 Computer Science engineering, IKG Punjab Technical University, Kapurthala 144601,(Punjab), IN
2 Khalsa College of Engineering & Technology, Amritsar 143001, (Punjab), IN
3 DAV institute of Engineering, Management & Technology, Jalandhar 144001 (Punjab), IN
1 Computer Science engineering, IKG Punjab Technical University, Kapurthala 144601,(Punjab), IN
2 Khalsa College of Engineering & Technology, Amritsar 143001, (Punjab), IN
3 DAV institute of Engineering, Management & Technology, Jalandhar 144001 (Punjab), IN
Source
International Journal of Advanced Networking and Applications, Vol 10, No 6 (2019), Pagination: 4099-4104Abstract
In Swarm Intelligence, various techniques are being planned as far as ACO, PSO, Fish Swarm, Bats Swarm, Bacterial Foraging, TABU, GA search and so forth. TS and GA is a single algorithm that firstly creates a set of random valid solutions, and for several iterations it optimizes them using a TS-based method. Afterwards, it takes this set of optimized solutions as the initial population for the GA, and iterates until the adopted stop criteria have been met. The objective of this research paper is to implement TABU-GA search to make the protocol more efficient and effective. This paper proposed MSEEC (multilevel stable and energy efficient clustering protocol) utilizing TABU-GA mechanism in the territory of 200m×200m.The recreation is done under the MATLAB 2013 a environment and observed the performance of TABU-GA MSEEC against MSEEC protocol on 4% increase in the case of first node dead (FND) and 28% increase in the case of last node dead (LND).Keywords
Wireless Sensor Network, Heterogeneity, TABU-GA Mechanism, MATLAB, FND, LND.Full Text
References
- Farouk, F., Rizk, R., &Zaki, F. W. (2014). Multi-level stable and energy-efficient clustering protocol in heterogeneous wireless sensor networks. IET Wireless Sensor Systems, 4(4), 159-169.
- Wang, X., Qian, L., Wu, J., & Liu, T. (2010). An energy and distance based clustering protocol for wireless sensor networks. In Novel Algorithms and Techniques in Telecommunications and Networking (pp. 409-412).Springer, Dordrecht.
- Li, X., Li, N., Chen, L., Shen, Y., Wang, Z., & Zhu, Z. (2010, March). An improved LEACH for clustering protocols in wireless sensor networks. In 2010 International Conference on Measuring Technology and Mechatronics Automation (Vol. 1, pp. 496-499).IEEE.
- Orojloo, H., &Haghighat, A. T. (2016). A Tabu search based routing algorithm for wireless sensor networks. Wireless Networks, 22(5), 1711-1724.
- Messaoud, R. B., &Ghamri-Doudane, Y. (2015, September). QoI and energy-aware mobile sensing scheme: A tabu-search approach. In 2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall) (pp. 1-6).IEEE.
- Amuthan, A., &Thilak, K. D. (2016, October). Survey on Tabu search meta-heuristic optimization.In 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES) (pp. 1539-1543).IEEE.
- Habib, S. J., &Marimuthu, P. N. (2017, April). Reputation analysis of sensors’ trust within tabu search.In World Conference on Information Systems and Technologies (pp. 343-352).Springer, Cham.
- Kuo, S. Y., & Chou, Y. H. (2017). Entanglementenhanced quantum-inspired tabu search algorithm for function optimization. IEEE Access, 5, 13236-13252.
- Vijayalakshmi, K., &Anandan, P. (2018). A multi objective Tabu particle swarm optimization for effective cluster head selection in WSN. Cluster Computing, 1-8.
- Qu, W., & Yang, M. (2014, June). An energy-efficient routing control strategy based on genetic optimization.In Proceeding of the 11th World Congress on Intelligent Control and Automation (pp. 2038-2041).IEEE.
- Ganesh, S., &Amutha, R. (2010). Real Time and Energy Efficient Transport Protocol for Wireless Sensor Networks.International Journal of Advanced Networking and Applications. arXiv preprint arXiv:1006.2691.
- Basavaraj, G. N., &Jaidhar, C. D. (2019). Intersecting Sensor Range Cluster-based Routing Algorithm for Enhancing Energy in WSN. International Journal of Advanced Networking and Applications, 10(4), 3938-3943.