Trusted-Differential Evolution Algorithm for Mobile Ad Hoc Networks
Mobile ad hoc networks are established and deployed spontaneously without any infrastructure in geographical area. The performance of network is satisfied only when all the member nodes have intensity to work in cooperative manner. But due to lack of any centralized unit, it is vulnerable to various attacks of malicious nodes. To overcome these types of attacks, the network has to be enhanced to provide secure delivery services. Our proposed Trusted-Differential Evolution algorithm deals with malicious node and inhibits them to become a member of data transmission path. It has two components: one to find the fittest path and other to deal with fluctuating credibility of nodes through trust. The dynamic of trust is handled by new trust-updation scheme along with punishment factor for malicious node. The proposed algorithm is compared with DSR and genetic algorithm.
KeywordsDifferential evolution Trust Punishment factor Fitness function Genetic algorithm Objective function
- 1.Storn, R., & Price, K. (1995). Differential evolution—A simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012, ICSI.Google Scholar
- 2.Chakraborty, U., Das, S., & Abbott, U. (2011). Clustering in mobile ad hoc networks with differential evolution. In IEEE Congress on Evolutionary Computation (CEC) (pp. 2223–2228).Google Scholar
- 3.Govindan, K., & Mohapatra, P. (2012). Trust computation and trust dynamics in mobile ad hoc networks: A survey. IEEE Communication Surveys & Tutorials, 14(2). Second Quarter (2012).Google Scholar
- 5.Ahn, C. W., & Ramakrishna, R. S. (2002). A genetic algorithm for shortest path routing problem and the sizing of populations. IEEE Transactions on Evolutionary Computation, 1(1), 511–579.Google Scholar
- 6.Johnson, D., & Maltz, D. (1996). Dynamic source routing in ad-hoc mobile wireless networks. In I. Tomasz, K. Hank (Eds.), Mobile computing (pp. 153–181) (1st ed.). Kluwer Academic Press.Google Scholar
- 7.Gundry, S., Zou, J., Kusyk, J., Uyar, M. U., Sahin, C. S. (2012). Fault tolerance bio-inspired topology control mechanism for autonomous mobile node distribution in MANETS. In Military Communications Conference, 2012 Milcom. https://doi.org/10.1109/milcom.2012.6415743.
- 8.Gundry, S., Kusyk, J., Zou, J., Sahin, C. S., Uyar, M. U. (2013). Differential evolution based fault tolerant topology control in MANETs. In Military Communications Conference, MILCOM 2013.Google Scholar
- 9.Ren, J., Wang, J., Xu, Y., Cao, & L. (2015). Applying differential evolution algorithm to deal with optimal path issues in wireless sensor networks. In IEEE International Conference on Mechatronics and Automation (ICMA). https://doi.org/10.1109/icma.2015.7237748.
- 10.Anjum, A., & Mohammed, G. N. (2012). Optimal routing in ad-hoc network using genetic algorithm. International Journal of Advanced Networking and Applications, 03(05), 1323–1328.Google Scholar
- 11.Yetgin, H., Cheung, K. T. K., & Hanzo, L. (2012). Multi-objective routing optimization using evolutionary algorithms. In IEEE Wireless Communication and Networking Conference (WCNC). https://doi.org/10.1109/wcnc.2012.6214324.
- 14.Sun Y. L., Yu, W., Han, Z., & Liu, K. J. R. (2006). Information theoretic framework of trust modeling and evaluation for ad hoc networks. IEEE Journal on Selected Areas in Communications, 24(2) (2006).Google Scholar
- 15.Xia, H., Wang, G.-D., & Pan, Z.-K. (2016). Node trust prediction framework in mobile ad hoc networks (pp. 50–56). IEEE: Trustcom/BigDataSE/ISPA.Google Scholar