# Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
## 6 Recent Advancements in Clustering Optimization Algorithms
### Fuzzy Logic
Each node will now compute its connection cost to the new CH in order to become members of the cluster incurring the least cost. The CHs are classified in the virtual backbone formation stage, followed by determination of their levels. Lastly, the data routing stage involves each CH selecting a member from the existing low-level CHs from which it received messages based on the minimum cost function. * DFLBCHSA: A distributed fuzzy clustering algorithm for a WSN with a mobile gateway  
The multi-objective distributed fuzzy clustering (DFLBCHSA) approach was developed to reduce delay in delivering data packets [80]. This approach formulated a CH selection process that considered node location and general state information; this was based on the weighted linear combination approach, which served to decide the values of the node location and its general state. CH selection was based on a fuzzy method using seven fuzzy descriptors. The resulting mobile gateway node position was predicted using the linear prediction method to reduce the problem of control message overhead in the network. The inputs to the fuzzy system were the energy, mean distance, neighbor count, and gateway location relative to the nodes. * MOFCA: multi-objective fuzzy clustering algorithm  
The MOFCA approach [81] was presented for addressing the issue of hot spots and early energy exhaustion in WSNs. This algorithm relies on various fuzzy inputs, as every node selects a number between 0 and 1 per round. When the selected number is less than the TH (i.e., the ideal percentage of the number of CHs), the node may become a temporary CH that will consider the fuzzy inputs in order to determine the competitive radius using fuzzy logic. Information can be disseminated based on the maximum competitive radius and the pre-determined radii of the temporary CHs. The temporary CH that receives the transmitted information from the higher energy level transmitter will cease to compete. However, when the energy levels of the two nodes are equivalent, a comparison of their density parameters will be conducted, and the one with the higher density will become the CH. The three fuzzy input variables in MOFCA are distance from the SNs, residual energy, and node density. Being a distributed method, MOFCA uses local decisions to determine the node competition radius and selection of the potential and final CHs. * FL-EEC/D: Energy-efficient fuzzy logic-based clustering technique for hierarchical routing protocol  
This fuzzy approach to CH nomination was proposed for efficient energy distribution management among the sensors in a WSN [82]. The fuzzy system's role is to create an opportunity for every node in the network to be selected as a CH. The chance of a node being chosen as the CH is determined by the system inputs, such as battery power, distance to BS, location suitability, proximity of neighboring nodes, and node density. All network nodes are compared to determine which node can serve as CH, so the node with the highest chance value can become the CH. With this approach, the proposed fuzzy model can select the optimal set of CHs, but its problem is that it cannot balance the load of the SNs among the CHs. The system analysis showed that the network lifetime was extended compared to LEACH, and the system's performance was efficient in a homogeneous WSN, though it showed poor performance in WSNs overall. * DFLC: A distributed fuzzy logic-based root selection algorithm  
The DFLC was developed as a fuzzy logic-based clustering technique implemented in a distributed manner by the nodes in a WSN [83]. The protocol considers the network a tree where any node can become the BS, CH, member, child, or parent node. For efficient selection of the CH, a fuzzy logic engine is run by each node using five input parameters (residual node energy, node distance to the neighboring nodes, node distance to the BS, node density, and number of hops). Only the necessary nodes with a better chance of being selected as CH are considered during the implementation of the fuzzy logic engine.