A new Ant Colony Optimization Routing Approach Based Fuzzy Clustering in Wireless Sensor Network

Mohammad Gholipour


Wireless sensor network (WSN) used to monitor or control a specific environment, consists of numerous sensor nodes which are connected to each other in order to perform some tasks. The sensor nodes have restricted power supply, processing capability and memory capacity. Since nodes’ power supply is not rechargeable in most WSN’s application and network lifetime is severely depend on vivid nodes, energy consumption is one of the main challenges in WSN. Therefore, designed schemas which are used in WSN should be as much as possible energy efficient. Clustering and determine forwarding path in routing are main approaches in design energy efficient algorithms. In this paper, we propose a novel approach in order to clustering and routing. A Fuzzy system in order to clustering and an ant-colony optimization (ACO) approach in order to routing have been used in the proposed approach so that lead to prolong network lifetime by sufficient load distribution. The simulation results illustrate that the proposed approach has more efficiency and prolong network lifetime.


Wireless sensor network, Clustering, Routing, Fuzzy logic, Ant colony optimization

Full Text:



Yang, K., Wireless sensor networks. Principles, Design and Applications, 2014.

Narendra, K. and V. Varun, A comparative analysis of energy-efficient routing protocols in wireless sensor networks, in Emerging Research in Electronics, Computer Science and Technology. 2014, Springer. p. 399-405.

Rao, J. and J. Zhang, On Clustering Algorithm Studies for Ad Hoc Wireless Sensor Network. Applied Mechanics & Materials, 2014.

Tan, Q. and X.J. Yue. Comparative Performance Analysis of Flat and Hierarchical Routing in Wireless Sensor Network. in Applied Mechanics and Materials. 2014. Trans Tech Publ.

Chen, D.Y., et al., Research on Routing Algorithm Based on Ant Colony Optimization for Wireless Sensor Networks. Applied Mechanics and Materials, 2015. 713: p. 1423.

Heinzelman, W.R., A. Chandrakasan, and H. Balakrishnan. Energy-efficient communication protocol for wireless microsensor networks. in System sciences, 2000. Proceedings of the 33rd annual Hawaii international conference on. 2000. IEEE.

Heinzelman, W.B., A.P. Chandrakasan, and H. Balakrishnan, An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on wireless communications, 2002. 1(4): p. 660-670.

Lindsey, S., C. Raghavendra, and K.M. Sivalingam, Data gathering algorithms in sensor networks using energy metrics. IEEE Transactions on parallel and distributed systems, 2002. 13(9): p. 924-935.

Gupta, I., D. Riordan, and S. Sampalli. Cluster-head election using fuzzy logic for wireless sensor networks. in Communication Networks and Services Research Conference, 2005. Proceedings of the 3rd Annual. 2005. IEEE.

Ran, G., H. Zhang, and S. Gong, Improving on LEACH protocol of wireless sensor networks using fuzzy logic. Journal of Information &Computational Science. 7(3): p. 767-775.

Salehpour, A.-A., et al. An energy efficient routing protocol for cluster-based wireless sensor networks using ant colony optimization. in Innovations in Information Technology, 2008. IIT 2008. International Conference on. 2008. IEEE.

Mao, S., et al., An improved fuzzy unequal clustering algorithm for wireless sensor network. Mobile Networks and Applications, 2013. 18(2): p. 206-214.

Bagci, H. and A. Yazici, An energy aware fuzzy approach to unequal clustering in wireless sensor networks. Applied Soft Computing, 2013. 13(4): p. 1741-1749.

MathWorks, I. Matlab Software. Available from: https://www.mathworks.com/.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Revista Publicando.

Licencia de Creative Commons


This Content is available under licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional.