Adjust Fuzzy Model Parameters for Head Election in Wireless Sensor Network Protocols

Author:

Afifi Walaa Abd el Aal1,Hefny Hesham Ahmed1

Affiliation:

1. ISSR-Cairo University, Egypt

Abstract

The clustering routing protocols attract many research papers that result from their well topology control, less demand resources, and less energy dissipation. The cluster routing protocols consist of single hop communication and multi hop communication. Single hop is applied between sensor node and its related cluster head. Multi hop is applied between cluster heads to base station. The previous two communication modes depend on the cluster head election. Appropriate cluster-head election can drastically reduce the energy consumption and enhance the lifetime of the network. The fuzzy models are used frequently for cluster head election. The fuzzy models can be built either expert's knowledge or numerical data. The authors propose fuzzy model using adaptive Takagi-Sugeno for wireless sensor network protocol (FATSN). The FATSN protocol is implemented by modified merging algorithm of fuzzy clustering with expected value (MCFEV). The FATSN protocol compares with the famous cluster routing protocol LEACH, EEUC, CHEF, and FCM protocols. The results show that FATSN protocol is efficiency

Publisher

IGI Global

Reference36 articles.

1. Abdullah, M., Eldin, Al-Moshadak, Alshaik, & Al-Anesi. (2015). Density Grid-Based Clustering for WirelessSensors Networks. International Conference on Communication, Management and Information Technology, Procedia Computer Scienc.

2. Afifi, W., & Hefny, H. (2014). Adaptive Takagi–Sugeno Fuzzy Model using Weighted Fuzzy Expected Value in Wireless Sensor Network. In Proceeding of The IEEE- 14th International Conference on Hybrid Intelligent Systems (pp. 225-231).

3. Afifi, W., & Hefny, H. (2015). An Enhanced fuzzy model for cluster head election in wireless sensor network. (Unpublished Master thesis). Institute of Statistical and Operation Researches, University of Cairo, Egypt.

4. A survey on sensor networks

5. Fuzzy Modeling for Control

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