Detecting and Secluding Route Modifiers by Neural Network Approach in Wireless Sensor Networks

Authors: C. N. Vanitha, M. Usha

Abstract:

In a real world scenario, the viability of the sensor networks has been proved by standardizing the technologies. Wireless sensor networks are vulnerable to both electronic and physical security breaches because of their deployment in remote, distributed, and inaccessible locations. The compromised sensor nodes send malicious data to the base station, and thus, the total network effectiveness will possibly be compromised. To detect and seclude the Route modifiers, a neural network based Pattern Learning predictor (PLP) is presented. This algorithm senses data at any node on present and previous patterns obtained from the en-route nodes. The eminence of any node is upgraded by their predicted and reported patterns. This paper propounds a solution not only to detect the route modifiers, but also to seclude the malevolent nodes from the network. The simulation result proves the effective performance of the network by the presented methodology in terms of energy level, routing and various network conditions.

Keywords: Neural networks, pattern learning, security, wireless sensor networks.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1128241

References:

[1] Kyoungsoo Bok,1 Yunjeong Lee,2 Junho Park,3 and Jaesoo Yoo.” An Energy-Efficient Secure Scheme in Wireless Sensor Networks”, Hindawi, Journal of Sensors, Article ID 1321079, 11 pages, Volume 2016 (2016).
[2] C. Zhu, L. Shu, T. Hara, L. Wang, S. Nishio, and L. T. Yang, “A survey on communication and data management issues in mobile sensor networks”, Wireless Communications and Mobile Computing, vol. 14, no. 1, pp. 19–36, 2014.
[3] Tao Li; Pingyi Fan; Zhengchuan Chen; Khaled Ben Letaief, “Optimum Transmission Policies for Energy Harvesting Sensor Networks Powered by a Mobile Control Center”, IEEE Transactions on Wireless Communications, Volume: 15, Issue: 9 Pages: 6132 - 6145, 2016.
[4] P. Annadurai, S. Vijayalakshmi, “Identifying malicious node using trust value in cluster based MANET (IMTVCM), IEEE, International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), 2014 .
[5] Nirav J. Patel, Rutvij H. Jhaveri ,”Detecting packet dropping nodes using machine learning techniques in Mobile ad-hoc network: A survey”, IEEE International Conference on Signal Processing And Communication Engineering Systems (SPACES), 2015.
[6] Jheena Rathore, Venkataramana Badarla, Supratim shit “Consensus- Aware Sociopsychological Trust model for wireless sensor networks”, ACM Transactions on sensor networks(TOSN), Volume 12, Issue 3, August 2016.
[7] CN Vanitha, M Usha,” An Improved Version of Data filtration using Enhanced Routing Control Protocol in Wireless Sensor Networks”, International journal of Applied Engineering and Research, Volume 10, Issue No.46, pp.32036-32043, 2015.
[8] M. Usha, C. N. Vanitha, “Pruning Route Modifiers in Wireless Sensor Networks, Springer, Wireless Personal Communications, Volume 89, Issue 1, pp 27–43, July 2016.
[9] Kyung-Ah Shim,” A Survey of Public-Key Cryptographic Primitives in Wireless Sensor Networks”, IEEE Communications Surveys & Tutorials, Volume: 18, Issue: 1, 2016.
[10] J. Sengathir, R. Manoharan, “Selfish Conscious Mathematical Model based on Reliable Conditional Survivability Co-efficient in MANET Routing”, Elsevier, 2013.