## 3 Fuzzy Logic-Based WSN Clustering
In this work, the authors used the [PERSON]'s fuzzy inference technique to elect an appropriate SCH, which used the metrics mentioned above as inputs. Here, the authors chose the linguistic variables on the basis of fuzzy descriptors. Less, medium, and high were used as linguistic variables for remaining battery power; low, moderate, and frequent were used for mobility; close, adequate, and far were used for centrality. The authors inferred that the performance in terms of overall network lifetime was better when compared to the traditional schemes discussed in the literature due to the idea of sink mobility along with the fuzzy logic-based election of SCHs. Most of the research works reported in the wireless sensor networks domain considered random deployment of nodes. In such cases, there was a possibility that the nodes were not deployed uniformly in the area of interest, which may have led to network partition or formation of holes in the network. In such scenarios, there is a possibility that the nodes in the edges of the holes may not capture the event and report it to the cluster head in the correct time, which may lead to a huge disaster. [PERSON] et al. [37] proposed an approach to detect the holes in the network. Here, the authors used the Delaunay triangulation approach to detect the holes in the network and applied a virtual gridding scheme to form grids that represent a cluster. The authors inferred that the employed gridding mechanism saved a considerable amount of energy and time compared to the traditional clustering mechanisms discussed in the literature. In this work, the authors proposed a novel idea of constructing routing paths, namely, common and emergency routing paths. Common routing paths were used by the cluster heads that do not use their full signal strength to transfer messages to the base station, whereas hop nodes that use their full signal strength to transfer critical messages to the base station used the emergency routing paths. Finally, cluster heads and hop nodes were elected by employing fuzzy logic-based approaches to overcome the uncertainties in the environment. In this work, the authors used residual energy and centrality as the fuzzy descriptors for electing the cluster heads, whereas residual energy and radio energy dissipation were used as fuzzy descriptors for electing hop nodes. The authors showed that the proposed mechanism was efficient and applicable to event-based large-scale wireless sensor networks, as the proposed model was not based on rounds and did not stop the network's operation for reconfiguration. In the traditional clustering approaches discussed in the literature, clusters were formed, and cluster heads were elected in more or less the same way. However, practically, the same node cannot continue as a cluster head for a longer duration, which may lead to battery exhaust and, hence, cluster reformation was proposed, wherein the protocol should reform clusters and rotate the responsibility of the cluster heads among the member nodes to improve the network lifetime. Although these protocols were best suited for clustering, it was proven that these protocols do not guarantee suitable cluster head selection. To overcome these limitations, [PERSON] et al. [38] proposed an energy-efficient clustering protocol using fuzzy logic utilizing fuzzy parameters such as the amount of energy in cluster head, distance from cluster head to the base station, and the number of connections in cluster head. In this work, the member nodes were attached to a cluster head having a higher chance concerning the aforementioned three parameters and the fuzzy inference system. For example, if the energy in the cluster head was low, the distance from the base station to the cluster head and the number of connections to the cluster head was high, and then automatically the chance of becoming a cluster head became very low.