## 3 Fuzzy Logic-Based WSN Clustering
Once the cluster head was elected, the best route was decided by finding the shortest route between each node and the base station through the cluster heads. The authors inferred that the presented work performed better compared to the existing techniques in the literature in terms of energy utilization, packet delivery ratio, delay, and network lifetime. The limitation of the presented work was that all the nodes were assumed to be trustful nodes, which might not have been the case. [PERSON] et al. [35] proposed a novel clustering scheme for WSN based on fuzzy logic to improve the lifetime of the network. In this work, the authors used parameters such as residual energy, distance from the sink, and the density of the nodes in a locality as the inputs to the fuzzy system. Once the sensor nodes were deployed in the area of interest, the nodes self-configured themselves as a network. In this work, there were two essential phases, namely, cluster head selection and formation. The cluster head was elected based on fuzzy rules. The authors used a fuzzy inference system as the decision-making unit that used IF-THEN rules along with OR/AND connectors for decision making. This decision-making behavior was effectively handled by computing the rank of each node. The parameters such as density, remnant energy, and distance from the base station were applied as inputs to the fuzzy inference system. The overhead of the cluster head was also considered during the cluster head selection process. Further, similar to the work presented in [34], the authors adopted the Mamdani inference system that imitated the human inference system in making a decision. De-fuzzification mapped the fuzzy set to a crisp set for exact decision-making. Once the cluster head was elected, the nearby nodes had to decide to join one of the elected cluster heads. In this process, the fuzzy inference system used three input parameters such as density, closeness to the cluster head, and cluster head residual energy. A node expressed its willingness to join a cluster by sending a 'JOINING' request packet to the cluster head, and the request would then be acknowledged by the cluster head. Once the clusters were formed, the cluster head shared a time division multiple access (TDMA) slot with all the member nodes in order to avoid a collision. The collected data were fused and transferred to the base station for further processing by the end-user. The authors inferred that the performance in terms of stability period and protracted lifetime was better when compared to the traditional schemes such as LEACH and CHEF discussed in the literature. [PERSON] et al. [36] proposed a novel clustering algorithm for wireless sensor networks to extend the lifetime of the network. In this work, the authors attempted to improve the performance of the LEACH protocol by introducing the concept of super cluster head (SCH) election. SCH was elected among the cluster heads by applying appropriate fuzzy descriptors, and only the SCH could communicate with the base station. The authors showed that the proposed technique reduced the number of transmissions and retransmissions performed by the cluster heads. In this work, the authors used parameters such as remaining battery power, mobility, and centrality as the fuzzy descriptors to elect an appropriate SCH that can communicate with the base station. Because the energy level of the cluster head gets reduced each round, remaining battery power was considered as one of the metrics. Another critical metric was centrality, which depicted the reachability of SCH concerning all the cluster heads, and the mobility metric referred to the variation in the distance between the base station and SCH, as the authors assumed that base station was mobile. Here, the proposed algorithm was composed of four essential blocks, these being fuzzification, rule evaluation, fuzzy inference engine, and de-fuzzification.