Energy-Efficient Fuzzy-Logic-Based Clustering Technique for Hierarchical Routing Protocols in Wireless Sensor Networks  
[PERSON]  
11 Department of Computer Engineering, Jordan University of Science and Technology, P.O. Box 3030, Irbid 222110, Jordan; [EMAIL_ADDRESS], jo (A.H.  
distributed approaches, local information is used to generate clusters. The Low Energy Adaptive Clustering Hierarchy (LEACH) protocol [12] is the first technique based on a probabilistic approach in CH election. Each node in the network generates a random number, and if this random number is less than a threshold value, the node assigns itself as the cluster head and broadcasts an advertisement message to all other nodes. Each of the receiving nodes (non-CHs) determines the CH to associate with based on the signal strength of the advertisement message.  
Developing an energy-efficient communication protocol is a critical goal in WSN. Several energy-efficient routing protocols were proposed to address this issue. They all adopt the idea of clustering or chaining sensor nodes so the transmission to the BS occurs in multi-hops.  
Clustering allows multi-hop transmission, data aggregation, data compression, and redundant data elimination. The benefits from clustering depend on the perfection of the clustering algorithm and the fitness of the exploited parameters. Unlike distributed clustering algorithms, which are performed by individual sensor nodes using their local information, the centralized clustering algorithms performed by the BS allow optimal clustering solutions, because the overall view of the WSN is available.  
Many factors affect the clustering algorithms in WSN, for example the remaining energy in the sensor nodes and the distances from their BS. However, if the problem is carefully analyzed, other factors can be considered. Obtaining an optimal clustering solution requires scaling each parameter by a weight corresponding to its influence on the dissipated energy and network lifetime. Therefore, if the clustering algorithm exploits more energy-affecting factors, the clustering will be more efficient. The Fuzzy Inference System (FIS) is an efficient modeling tool to combine parameters for better parameter integration results.  
We introduce a fuzzy-based centralized clustering technique for energy-efficient routing protocols in WSN. The proposed clustering technique uses fuzzy logic to elect CHs and enforces a separation distance between them for even CH distribution through the covered area. Separation distance is calculated adaptively based on the number of remaining live nodes, the dimensions of the area covered by these nodes, and the percentage of the desired CHs.  
The proposed fuzzy model uses five parameters to prioritize opportunities of sensor nodes' CH election. These factors are: the remaining energy of the sensor node, distance to the BS, density of surrounding sensor nodes (which are probable cluster members for the current sensor node if elected as a CH), compacting of surrounding sensor nodes, and finally, the location suitability calculated via the average of the local consumed energy for the surrounding nodes. As a secondary contribution to this research, we suggest adopting the Gini index [17] for energy balance evaluation among nodes in the WSN clustering algorithm.  
The rest of this paper is organized as follows: In Section 2, we review the basics of WSNs and fuzzy logic. Section 3 provides the methodology road-map for the proposed approach. Section 4 presents the performance evaluation, which begins with the simulation settings, followed by the performance metrics, and ends with the results and discussion. Finally, Section 5 draws the conclusions of the entire study and discusses further potentials of follow-up research.