## 4 Fuzzy Logic-Based Routing Algorithms
One big challenge in a wireless sensor node is that it has very little storage space, low computational capability, and limited battery power. Due to the aforementioned issues, a trade-off should be handled between power optimization and processing accuracy in WSNs. In a study by [PERSON] et al. [47], the authors proposed a novel energy-aware clustering method via algorithm (LPO) and fuzzy logic in WSNs. Figure 7 depicts a clustering technique, which uses the FLS described in Figure 1, that has been provided based on two parameters named distance from the sink and remaining energy. The proposed clustering technique has a direct effect on the routing process as it takes the nodes' distances from the sink while forming the clusters, which results in a superb performance in terms of end-to-end delay, energy consumption, input packet, and network lifetime when compared with the related literature work. Typically, data are received by sensor nodes within a limited transmission range. These data have been extracted from the packet and processed then forwarded to the sink node. This process leads to high-energy consumption. This issue is considered one of the main challenges in WSNs that have a direct impact on the lifetime as sensors use batteries. Moreover, sensors that are closer to the sink consume higher energy than others because they directly forward the packets to the sink. Therefore, having an adaptive energy transmission based on the nodes' positions compared to the sink node is considered as one of the major issues in WSNs. In [48], [PERSON] and [PERSON] proposed optimization energy consumption with multiple mobile sink nodes using fuzzy logic. In this paper, a technique is suggested that can optimize the energy consumption of sensor nodes through various mobile stations (MSs) in which fuzzy logic is used in WSNs. In this technique, instead of using a fixed sink at a predetermined location, an MS is used to  
Figure 7: Forwarding mode fuzzy monitoring system. collect sensor information so that it moves smartly within the network and collects sensor information. The remainder of the nodes are also grouped on the basis of fuzzy logic in order to avoid the issue of the energy hole. The fuzzy logic concept is centered on the idea of partial set membership, rather than crisp or discreet set membership. It was initially implemented as an alternative strategy to information processing that is defined by a set of \"fuzzy parameters\" that include components in which membership level varies within the set. Fuzzy logic differs from standard control techniques by incorporating simple IF-THEN structure instead of any complicated mathematical model. Consequently, the lifetime of the network is therefore improved. The purpose behind this study, however, is to solve the issue of the energy hole, particularly in large networks, by separating the WSN field into different independent zones and using an MS in each. They also use the fuzzy logic method in this study to deal with challenges in the estimation of cluster head radius. In the purposeful fuzzy strategy in this algorithm, the selection of CH happens by selecting three fuzzy descriptors, namely, residual energy of each node, distance to RN, and node density in order to estimate the radius of competition for tentative CHs. Indeed, the introduced multiple mobile sink nodes had a positive impact on the routing process, as it made the destination node(s) closer to the sensor nodes, thus reducing the needed transmission energy and improving the network lifetime. In this algorithm, multi-sink nodes' MSs are employed, and the positive area defined as the areas that have neighbors or eligible nodes are divided into small areas. Moreover, the distances between sensors are reduced by proposing a clustering method using fuzzy logic. This method helps in reducing energy consumption. First node die (FND) and half of the nodes alive (HNA) metrics have been tested. Simulation results outperformed other algorithms in terms of energy consumption.