## 3 Fuzzy Logic-Based WSN Clustering
On the other hand, if the energy level in the cluster head was high, the distance from the base station to the cluster head and the number of connections to the cluster head was low, and then the chance of this node to be a cluster head became very high. The authors depicted that the proposed algorithm achieved an improvement of about 12% in the network lifetime compared with LEACH [24], and the fuzzy logic cluster formation protocol (FLCFP). [PERSON] et al. [39] introduced a synchronous distributed fuzzy C-means algorithm that was capable of clustering the nodes based on the nodes' measured data, with degrees of membership values varying from 0 to 1. Simulation results showed that the decentralized approach had almost similar results to the centralized one. However, as expected, by de-centralized algorithms, they had higher communication overhead in terms of data exchanged between nodes than centralized algorithms. Recently, the authors in [40] reported a distributed C-means clustering algorithm implementation in an asynchronous distributed fashion via broadcast-only token-passing for wireless sensor networks to address the issues such as managing a huge amount of data that were collected from the field of interest and analyzing the behavior of the nodes on the basis of the collected data. In this work, the authors proposed to partition the networks into homogeneous groups, such that the agents belonging to the same set had similar information, and agents in different groups would have distinct information. The presented work organized data transmission following a broadcast only token-passing approach, thereby reducing the undesirable communication efforts that may happen in such networking scenarios. The simulation results depicted that the distributed C-means algorithm based on asynchronous, event-driven, and broadcast-only communication yielded similar results to the centralized C-means algorithm, but with a reduced amount of data exchanged compared to synchronous distributed fuzzy C-means algorithm [39]. In the study by [41], the authors proposed a distributed opinion dynamics model utilizing fuzzy states. This model was extended and used to cluster wireless sensor nodes in a distributed manner [42], where each sensor held a large set of data. The main goal was to group the nodes into homogeneous clusters by information type. In the literature, most of the proposed algorithms ask the user to specify the desired number of clusters beforehand. In their approach, the number of clusters was calculated on the basis of the distance constraint among cluster centroids, which did not require a central authority and depended only on the exchanged data between nodes, thus making the clustering algorithm rely merely on the sensed data and not on the network topology. Figure 4 depicts a diagram summarizing the different parameters discussed in this paper used in cluster formation utilizing fuzzy logic. Table 1 summarizes the various protocols discussed earlier in terms of some essential characteristics and capabilities possessed. All the protocols discussed in Table 1 are cluster-based protocols implemented with the fuzzy-based approach. According to Table 1, self-management model [37] performs better than the other mechanisms as it is highly reliable because the authors proposed a novel hole detection mechanism along with the fuzzy-based approach. One more vital factor to be noted in the comparison chart is that the self-management model is not round-based, and hence the network operation is never affected due to reconfiguration. In addition, the model proposes the idea of hop nodes, which, in case of emergencies, can deliver critical information faster to the base station. Furthermore, in most of the fuzzy-based clustering algorithms discussed in the literature, the metrics such as first node dies (FND), half of the nodes dies (HND), and last node dies (LND) were used to evaluate the network lifetime [33; 35; 37].