# Fuzzy-Logic Based Distributed Energy-Efficient Clustering Algorithm for Wireless Sensor Networks
## 2 Related Works
CHEF (Cluster Head Election mechanism using Fuzzy logic in wireless sensor networks) is a kind of clustering algorithm which introduces fuzzy logic into wireless sensor networks to optimize the energy consumption of the system [18]. CHEF chooses nodes residual energy and distance between nodes and base station as the input values to compute the probability of each node to be CH. Although the algorithm offers a good decision scheme for wireless sensor networks in distributed CH election, it ignores the influence of nodes degree on the energy utilization efficiency for clustered networks. DFLC (Distributed Fuzzy Logic-based Clustering algorithm) is also a distributed clustering algorithm using fuzzy approach for WSNs [19]. It decreases the number of unnecessary data packages which the relay nodes need to receive and forward during the messages transferring from the leaf nodes to the root node. Moreover, it eliminates the message of the nodes which have a lower probability to be a new root. In other words, it adds a filter mechanism before CHs election to improve the quality of the candidate nodes. In this algorithm, it chooses node residual energy, distance to the base station and nodes density as consideration factors for the input of fuzzy approach. The output parameter \"probability\" is a fuzzy linguistic variable with five stages of fuzzy division. The output is converted to crisp value by Center of Area (COA) method. However, it ignores the hotspots problem in multi-hop communication, and it may cause load imbalance and lead to an earlier death of relay nodes, which affects the overall performance of the whole network systems. Moreover, considering the distance between nodes and BS is not quite suitable in dynamic network because the location of nodes is uncertain in dynamic networks, it will cause a lot of energy consumption if all the nodes have to communicate with the BS to get the current distances in real time in each round. LAUCF (Low-energy Adaptive Unequal Clustering protocol using Fuzzy c-means) is a kind of low energy adaptive unequal clustering algorithm using fuzzy c-means to select suitable CH nodes to uniform energy consumption [20]. In [21], the authors also proposed a fuzzy c-means clustering algorithm to save energy consumption. Cluster head is selected based on sensor's location within each cluster, its location with respect to fusion center (FC), its signal-to-noise ratio (SNR) and its residual energy. In [22], the authors made use of fuzzy c-means to cluster network to reduce the shortest path error between far nodes and improve the final localization accuracy for irregular cognitive radio networks localization. Although making use of c-means can get more reasonable fuzzy analysis results, nodes require much more extra energy consumption during every CH election in this way. DUCF (Distributed Unequal Clustering using Fuzzy logic) is a distributed clustering algorithm based on fuzzy logic for unequal clustering networks, which chooses node residual energy, node degree, and the distance between nodes and base station as the input, and chooses the probability to be elected as the CH and the size of the cluster as the outputs [23]. This scheme not only takes the node's own factors, but also the cluster size into consideration, and enhances the overall performance of the networks. Similar to the shortages in DFLC, getting the distance between nodes and BS in real time also leads to a lot of energy consumption in dynamic network, which has a negative influence on the lifetime of sensor nodes. Considering the inadequacy of the algorithms mentioned above, we propose an Energy Efficient Distributed Clustering algorithm using Fuzzy approach for wireless sensor networks with non-uniform distribution called EEDCF. The algorithm is a fully distributed clustering algorithm that each node uses TSK fuzzy inference system to analyze whether it fits to be the CH compared with its neighbor nodes. Nodes only need to communicate with the neighbor nodes to maintain the local topology.