Sefp: A New Routing Approach Using Fuzzy Logic For Clustered Heterogeneous Wireless Sensor Networks

Wireless sensor networks (WSNs) are composed of set sensor nodes communicating through wireless channels with limited resources. Therefore, several routing protocols and approaches about energy efficient operation of WSNs have been proposed. Clustering algorithm based routing protocols are well used for efficient management of sensing sensor node energy resources. However, many researches were focused on optimization of well-known hierarchical routing approaches of WSNs using fuzzy logic system or heuristic methods. Most of these routing approaches haven’t considered the impact of heterogeneity of sensor nodes, in terms of their energy which is equipped with additional energy resources. In this paper, we propose stable election using three fuzzy parameters approach (SEFP) using fuzzy logic system for heterogeneous WSNs. The main purpose of this routing approach is to improve the network lifetime and particularly the stability period of the network. In the SEFP approach, the sensor node with the maximum chance value becomes a cluster head (CH) based in three fuzzy parameters such as residual energy of each sensor node, closeness to base station (BS), and sum of distances between particular sensor node and other sensor nodes (area distance). The Simulation results of heterogeneous WSNs shows that our approach using fuzzy logic system always preserves more energy as compared to well-known protocols such as LEACH and SEP. additionally, we found that our approach SEFP outperforms LEACH and SEP protocols in prolonging the lifetime and the stability period for heterogeneous WSNs.

[1]  Anantha P. Chandrakasan,et al.  An application-specific protocol architecture for wireless microsensor networks , 2002, IEEE Trans. Wirel. Commun..

[2]  Seon-Ho Park,et al.  CHEF: Cluster Head Election mechanism using Fuzzy logic in Wireless Sensor Networks , 2008, 2008 10th International Conference on Advanced Communication Technology.

[3]  Marcelo Simoes Introduction to Fuzzy Control , 2003 .

[4]  S. Sivanandam,et al.  Introduction to Fuzzy Logic using MATLAB , 2006 .

[5]  Lotfi A. Zadeh,et al.  Fuzzy Logic Toolbox User''''s Guide , 1995 .

[6]  Kazuo Tanaka,et al.  An approach to fuzzy control of nonlinear systems: stability and design issues , 1996, IEEE Trans. Fuzzy Syst..

[7]  Ian F. Akyildiz,et al.  Sensor Networks , 2002, Encyclopedia of GIS.

[8]  Indranil Gupta,et al.  Cluster-head election using fuzzy logic for wireless sensor networks , 2005, 3rd Annual Communication Networks and Services Research Conference (CNSR'05).

[9]  Alyani Ismail,et al.  A survey on energy awareness mechanisms in routing protocols for wireless sensor networks using optimization methods , 2014, Trans. Emerg. Telecommun. Technol..

[10]  S. Eisenman,et al.  Sensor Networks , 2002 .

[11]  Jin-Shyan Lee,et al.  Fuzzy-Logic-Based Clustering Approach for Wireless Sensor Networks Using Energy Predication , 2012, IEEE Sensors Journal.

[12]  Azer Bestavros,et al.  SEP: A Stable Election Protocol for clustered heterogeneous wireless sensor networks , 2004 .

[13]  Huazhong Zhang,et al.  IMPROVING ON LEACH PROTOCOL OF WIRELESS SENSOR NETWORKS USING FUZZY LOGIC , 2010 .

[14]  Xu Fei,et al.  MOVING TARGET DETECTION BASED ON GLOBAL MOTION ESTIMATION IN DYNAMIC ENVIRONMENT , 2014 .

[15]  Biswajit Panja,et al.  Security in wireless sensor networks for health monitoring helmet with anomaly detection using power analysis and probabilistic model , 2014, 2014 IEEE Conference on Wireless Sensors (ICWiSE).