Formulation of Fuzzy Correlated System for Node Behavior Detection in WSN


  • Noor Shahidah Faculty of Science and Technology, Universiti Sains Islam Malaysia (USIM).
  • A. H Azni Faculty of Science and Technology, Universiti Sains Islam Malaysia (USIM).


Correlated Node Behavior, Fuzzy Logic System, Neural Network, Wireless Sensor Network,


Wireless Sensor Network depends highly upon the cooperation among the nodes behavior in transmission of packet data, messages and route discovery. Over open medium environment, nodes are free to move and may change their behavior arbitrarily. In the presence of misbehavior node in some cases, it may instigate its neighboring nodes to compromise with the misbehaved node. Thus, this has resulted to a spreading of correlated node behavior and the impact of this event may result in high severity in network performance. Therefore, fuzzy logic model is proposed to formulate the correlated node behavior in WSN. The formulation of correlated node behavior based on fuzzy logic function of peer nodes real parameter measurement is investigated to determine the status of the node and then the fuzzy neural network will model the correlated node behavior occurrence. The accuracy of the results is established via sensor network simulation. The result of this study is providing a fundamental guideline for network designer in order to understand the fault-tolerance in network topology.


J. Sen, “Sustainable Wireless Sensor Networks,” Sustain. Wirel. Sens. Networks, pp. 279–309, 2010.

T. Thanakornworakij, R. Nassar, C. B. Leangsuksun, and M. Paun, “The Effect of Correlated Failure on the Reliability of HPC Systems,” Parallel Distrib. Process. with Appl. Work. (ISPAW), 2011 Ninth IEEE Int. Symp., pp. 284–288, 2011.

Y. Xu and W. Wang, “Characterizing the spread of correlated failures in large wireless networks,” Proc. - IEEE INFOCOM, 2010.

L. A. Zadeh, “Fuzzy logic systems: origin, concepts, and trends,” 2004.

B. Nisha U, U. Maheswari N, V. R, and Y. Abdullah R, “Improving Data Accuracy Using Proactive Correlated Fuzzy System in Wireless Sensor Networks,” vol. 9, no. 9, pp. 3515–3538, 2015.

S. A. Khan, B. Daachi, and K. Djouani, “Application of fuzzy inference systems to detection of faults in wireless sensor networks,” Neurocomputing, vol. 94, pp. 111–120, 2012.

K. Salahshoor, M. S. Khoshro, and M. Kordestani, “Simulation Modelling Practice and Theory Fault detection and diagnosis of an industrial steam turbine using a distributed configuration of adaptive neuro-fuzzy inference systems,” Simul. Model. Pract. Theory, vol. 19, no. 5, pp. 1280–1293, 2011.

S. Shamshirband et al., “Co-FAIS: Cooperative fuzzy artificial immune system for detecting intrusion in wireless sensor networks,” J. Netw. Comput. Appl., vol. 42, pp. 102–117, 2014.

T. V. P. Sundararajan and A. Shanmugam, “Modeling the Behavior of Selfish Forwarding Nodes to Stimulate Cooperation in MANET,” Int. J. Netw. Secur. Its Appl., vol. 2, no. 2, 2010.

C. G. Looney and S. Dascalu, “A Simple Fuzzy Neural Network,” In CAINE, pp. 12–16, 2007.

V. Duraisamy, N. Devarajan, D. Somasundareswari, a. A. M. Vasanth, and S. N. Sivanandam, “Neuro fuzzy schemes for fault detection in power transformer,” Appl. Soft Comput., vol. 7, no. 2, pp. 534–539, 2007.

J. Tian and M. Gao, “Intelligent community intrusion detection system based on wireless sensor network and fuzzy neural network,” 2009 ISECS Int. Colloq. Comput. Commun. Control. Manag., pp. 102–105, 2009.




How to Cite

Shahidah, N., & Azni, A. H. (2017). Formulation of Fuzzy Correlated System for Node Behavior Detection in WSN. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(2-9), 101–104. Retrieved from