Cognitive Road Traffic Controller Using Fuzzy Logic in Iot
||International Journal of Computer & Organization Trends (IJCOT)||
|© 2017 by IJCOT Journal|
|Volume - 7 Issue - 2
|Year of Publication : 2017|
|Authors : K. S. Arikumar, R. Swetha, D. Swathy|
|DOI : 10.14445/22492593/IJCOT-V41P301|
K. S. Arikumar, R. Swetha, D. Swathy "Cognitive Road Traffic Controller Using Fuzzy Logic in Iot ", International Journal of Computer & organization Trends (IJCOT), V7(2):1-5 Mar - Apr 2017, ISSN:2249-2593, www.ijcotjournal.org. Published by Seventh Sense Research Group.
Road Traffic congestion is a critical problem in many cities which causes major distress to road users. This is due to increase use of vehicles which waits endlessly and causes traffic deadlock. Many traffic control systems have been developed to mitigate this problem. Now-a-days traffic demands are high and increasing due to increase in number of vehicles. We proposed a technique called Cognitive Road Traffic Controller (CRTC) which efficiently reduces the waiting time in traffic signal. This paper gives a brief discussion of the procedures we adopted to develop an intelligent fuzzy control system for dealing with the road traffic congestion problem. Specialized node known as Local Cognitive Node (LCN) implements the learning components and decision making. The system was developed using fuzzy logic technology and Cognitive sensor node where these nodes use learning mechanism to take decisions at LCN. Simulation results shows that problem of traffic congestion is efficiently reduced in the traffic network by using the proposed mechanism.
 Askerzade, I.N, Mustafa S.Mahmood “Design and Implementation of Intelligent Traffic Control by Using Fuzzy Logic”, Talk in 1st International Fuzzy Systems Symposium October 1-2, Ankara, pp.52- 59, 2009.
 O. C. Akinyokun, Neuro-Fuzzy Expert System for Evaluation of Human Resources Performance. First Bank of Nigeria PLC Endowment Fund Lecture Series 1, Delivered at the Federal University of Technology, Akure, Nigeria, 2002.
 L. GiYoung, J. Kang & Y. Hong, The optimization of traffic signal light using artificial intelligence. Proceedings of the 10th IEEE International Conference on Fuzzy Systems.Barisban Australia, 2001.
 J. Niittymäki & M. Pursula, Signal Control using Fuzzy Logic, Fuzzy Sets and Systems, Vol. 116, 2000, pp. 11-22.
 C. P. Pappis & E. H. Mamdani, A Fuzzy Logic Controller for a Traffic Junction, IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-7, No. 10, 1977, pp. 707-717.
 L.A. Zadeh, Fuzzy Sets, Information and Control, 8, 1965, 338-353.
 L. Zadeh Applied soft computing- foreword. Appl. Soft Comput. 1: 1-2, 2001.
 S. Horikawa, T. Furuhashi, & Y. Uchikawa, On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm,” IEEE Transactions on Neural Networks, 3, 1992, 801- 806
 G. K. Mann & R.G. Gosine, “Adaptive hierarchical tuning of fuzzy controllers,” Expert Systems, 19(1), 34-45, 2002.
 J. Chen, & Y. Xi, Nonlinear System Modeling by Competitive Learning and Adaptive Fuzzy Inference System,” IEEE Transactions on Systems, Man, and Cybernetics,Part C: Applications and Reviews,vol. 28, no. 2, 1998, pp. 231-238.
 K. Tan, M. Khalid & R. Yusof, Intelligent traffic lights control by fuzzy logic. Malaysian Journal of Computer Science, 9(2): 29-35, 1996.
 U. C. Osigwe, F. O. Oladipo, E. A. Onibere, Design and Simulation of an Intelligent Traffic Control System. International Journal of Advances in Engineering & Technology Vol. 1, Issue 5, 2011, pp. 47-57.
 Avhad Kalyani B. "Congestion Control in Wireless Sensor Network-A Survey" . International Journal of Computer & organization Trends (IJCOT),V2(4):99-101 2012.ISSN 2249-2593.www.ijcotjournal.org. Published by Seventh Sense Research Group.
 Pouya Bolourchi and Sener Uysal , Forest Fire Detection in Wireless Sensor Network Using Fuzzy Logic , 2013 fifth international conference on computational intelligence communication systems and networks
Wireless Sensor Networks, Traffic Congestion, fuzzy logic, fuzzy rules, Cognitive node.