Improved Performance of Wireless Sensor Network Based on Fuzzy Logic for Clustering Scheme

The wireless sensor network (WSN) consists of a large number of sensor nodes collaborative to collect and transmit data to the end user. Since the network’s long life is an utmost requirement of WSN. Clustering is one of the most effective ways of prolonging the lifetime of the network. In clustering, a node takes charge of the cluster to coordinate and receive information from the member nodes and transfer it to the sink. With the imbalance of energy dissipation by the sensor node, it may lead to premature failure of the network. Therefore, a robust balanced clustering algorithm can solve this issue in which a worthy candidate will play the cluster head role in each round. This paper proposes an improvement of WSN based on fuzzy logic for clustering. Residual energy, distance from the sink, and density of the nodes in its locality are taken account as the input to feed into fuzzy inference system. Compared results with the other approaches in the literature show the proposed scheme provides the better performance in terms of stability period and protracted lifetime.

[1]  Carlos F. García-Hernández,et al.  Wireless Sensor Networks and Applications: a Survey , 2007 .

[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]  Cesare Alippi,et al.  An Adaptive System for Optimal Solar Energy Harvesting in Wireless Sensor Network Nodes , 2008, IEEE Transactions on Circuits and Systems I: Regular Papers.

[4]  Padmalaya Nayak,et al.  A Fuzzy Logic-Based Clustering Algorithm for WSN to Extend the Network Lifetime , 2016, IEEE Sensors Journal.

[5]  Samayveer Singh,et al.  Energy Efficient Clustering Protocol Using Fuzzy Logic for Heterogeneous WSNs , 2016, Wirel. Pers. Commun..

[6]  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).

[7]  Biswanath Mukherjee,et al.  Wireless sensor network survey , 2008, Comput. Networks.

[8]  Shiyao Jin,et al.  Coverage Problem in Wireless Sensor Network: A Survey , 2010, J. Networks.

[9]  Trong-The Nguyen,et al.  Clustering Formation in Wireless Sensor Networks: A Survey , 2017, J. Netw. Intell..

[10]  Ossama Younis,et al.  HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks , 2004, IEEE Transactions on Mobile Computing.

[11]  Wendi Heinzelman,et al.  Energy-efficient communication protocol for wireless microsensor networks , 2000, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences.

[12]  Mohammed Abo-Zahhad,et al.  A Comprehensive Survey on Hierarchical-Based Routing Protocols for Mobile Wireless Sensor Networks: Review, Taxonomy, and Future Directions , 2017, Wirel. Commun. Mob. Comput..

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

[14]  M. El-Sayed Wahed,et al.  An Enhancement Approach for Reducing the Energy Consumption in Wireless Sensor Networks , 2017, J. King Saud Univ. Comput. Inf. Sci..

[15]  Dirk Timmermann,et al.  Low energy adaptive clustering hierarchy with deterministic cluster-head selection , 2002, 4th International Workshop on Mobile and Wireless Communications Network.