CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Dynamic Secure Multipath Routing in Wireless Sensor Networks using Modified Simulated Annealing based Particle Swarm Optimization

by V. Upendran, R. Dhanapal
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 154 - Number 5
Year of Publication: 2016
Authors: V. Upendran, R. Dhanapal
10.5120/ijca2016912135

V. Upendran, R. Dhanapal . Dynamic Secure Multipath Routing in Wireless Sensor Networks using Modified Simulated Annealing based Particle Swarm Optimization. International Journal of Computer Applications. 154, 5 ( Nov 2016), 18-23. DOI=10.5120/ijca2016912135

@article{ 10.5120/ijca2016912135,
author = { V. Upendran, R. Dhanapal },
title = { Dynamic Secure Multipath Routing in Wireless Sensor Networks using Modified Simulated Annealing based Particle Swarm Optimization },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2016 },
volume = { 154 },
number = { 5 },
month = { Nov },
year = { 2016 },
issn = { 0975-8887 },
pages = { 18-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume154/number5/26487-2016912135/ },
doi = { 10.5120/ijca2016912135 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:59:25.160166+05:30
%A V. Upendran
%A R. Dhanapal
%T Dynamic Secure Multipath Routing in Wireless Sensor Networks using Modified Simulated Annealing based Particle Swarm Optimization
%J International Journal of Computer Applications
%@ 0975-8887
%V 154
%N 5
%P 18-23
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Routing in wireless sensor network has entirely different requirements compared to the routing requirements of a normal network. The complexity arises from the requirements of security, load balancing, resource constraints and failure handling. This paper presents a fast, secure and dynamic route generation technique for wireless sensor networks using metaheuristics for route generation. A modified form of Particle Swarm Optimization technique is used for route generation. In-order to overcome the problem of local optima, PSO is hybridized by incorporating Simulated Annealing in its local selection process. Hybridization also speeds up the route selection mechanism, thereby reducing the time overhead to a maximum extent.

References
  1. Li, B., Li, H., Wang, W., Yin, Q., Liu, H. 2013. Performance analysis and optimization for energy-efficient cooperative transmission in random wireless sensor network. IEEE Trans. Wireless Commun.12 (9), 4647–4657.
  2. Ganesh, S., Amutha, R., 2013. Efficient and secure routing protocol for wireless sensor networks through snr based dynamic clustering mechanisms. J. Commun. Networks 15 (4), 422–429.
  3. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., and Cayirci, E. 2002. Wireless sensor networks: a survey, Computer Network, Vol. 38, No. 4, pp. 393–422.
  4. Yick, J., Mukherjee, B., and Ghosal, D.2008. Wireless sensor network survey, Comput Network, Vol. 52, No. 12, pp. 2292–2330.
  5. Kenndy, J., and Eberhart, R.C.1995. Particle swarm optimization. InProceedings of IEEE International Conference on Neural Networks (Vol. 4, pp. 1942-1948).
  6. Elhoseny, M., Elminir, H., Riad, A., and Yuan, X., 2015. A secure data routing schema for WSN using Elliptic Curve Cryptography and homomorphic encryption. Journal of King Saud University-Computer and Information Sciences.
  7. Kaur, R., and Singh, K.P. 2015. An Efficient Multipath Dynamic Routing Protocol for Mobile WSNs. Procedia Computer Science, 46, pp.1032-1040.
  8. Zhang, D.G., Zheng, K., Zhang, T., and Wang, X. 2015. A novel multicast routing method with minimum transmission for WSN of cloud computing service. Soft Computing, 19(7), pp.1817-1827.
  9. Lee, S.J., Su, W. and Gerla, M., 2002. On-demand multicast routing protocol in multihop wireless mobile networks. Mobile networks and applications, 7(6), pp.441-453.
  10. Garcia-Luna-Aceves, J.J. and Madruga, E.L., 1999. The core-assisted mesh protocol. IEEE Journal on selected Areas in Communications, 17(8), pp.1380-1394.
  11. Raja, P., and Dananjayan, P. 2015. Game Theory Based Cooperative MIMO Routing Scheme for Lifetime Enhancement of WSN. International Journal of Wireless Information Networks, 22(2), pp.116-125.
  12. Ilango, P., 2015. Secure authentication and integrity techniques for randomized secured routing in WSN. Wireless Networks, 21(2), pp.443-451.
  13. Fan, X., and Gong, G. 2012. Accelerating signature-based broadcast authentication for wireless sensor networks. Ad Hoc Networks, 10(4), pp.723-736.
  14. Shim, K.A., Lee Y.R., and Park, C.M. (2013). EIBAS: An efficient identity-based broadcast authentication scheme in wireless sensor networks. Ad Hoc Networks, 11(1), 182–189.
  15. Marappan, P., and Rodrigues, P. 2016. An energy efficient routing protocol for correlated data using CL-LEACH in WSN. Wireless Networks, 22(4), pp.1415-1423.
  16. Dedeoglu, V. et al. (2012). Cross-layer energy minimization in correlated data gathering wireless sensor networks. In IEEE 13th international workshop on signal processing advances in wireless communications (SPAWC), 2012, pp. 304–308.
  17. Zeydan, E., et al. 2012. Energy-efficient routing for correlated data in wireless sensor networks. Ad Hoc Networks, 10(6), 962–975.
  18. Ahmed, A.M., and Paulus, R. 2016. Congestion detection technique for multipath routing and load balancing in WSN. Wireless Networks, pp.1-8.
  19. Kaur, G., and Kaur, S. 2016. Enhanced M-Gear Protocol for Lifetime Enhancement in Wireless Clustering System. International Journal of Computer Applications, 147(14).
  20. Kumar, A., Vaid, R., Katiyar, S., and Shumaila Rizwan, S. 2016. Adaptive Data Size Compressed Algorithm to Reduce Energy and Provide Route Security using AOMDV Protocol in WSN. International Journal of Computer Applications 147(2):32-38.
  21. Khachaturyan, A., Semenovskaya, S., Vainshtein, B. (1979). Statistical-Thermodynamic Approach to Determination of Structure Amplitude Phases. Sov.Phys. Crystallography.24 (5): 519–524.
  22. Khachaturyan, A., Semenovskaya, S., Vainshtein, B. (1981). The Thermodynamic Approach to the Structure Analysis of Crystals. Acta Crystallographica (A37): 742–754.doi:10.1107/S0567739481001630.
  23. Upendran V., Dhanapal R. (2015) A Study of Security Challenges, Solutions and Research Directions for High Performance Secure Routing in WSNs. International Journal of Applied Engineering Research.;10(73)
  24. Upendran V, Dhanapal R. (2015) Firefly Algorithm based Secure and Energy Efficient Routing (FASER). Advances in Natural and Applied Sciences. 9(8):29-36.
  25. Upendran V, Dhanapal R. (2016) Secure and Distributed On-Demand Randomized Routing in WSN, International Journal of Computers & Technology, 15(6)
Index Terms

Computer Science
Information Sciences

Keywords

WSN Routing Secure routing PSO Simulated Annealing Multipath Routing

Powered by PhDFocusTM