## 4 Fuzzy Logic-Based Routing Algorithms
As a result, the problem of the energy hole in WSNs was optimized. WSN suffers from computation power, limited energy, bandwidth, and resource memory. The use of clustering techniques in the routing protocol is one promising solution that has a direct impact on energy efficiency in WSNs. In [49], [PERSON] et al. proposed a hop-by-hop protocol named the development of a fuzzy-based energy-efficient cluster routing protocol to increase the WSN lifetime. The algorithm has several phases. In the setup phase, clustering techniques in routing protocols are used to identify and select the CH. Next, the data are forwarded from sender CH to another CH towards the sink node. In the second phase, the proposed protocol uses fuzzy logic with three parameters that were weight, energy, and distance, to forward the data from the source node towards the sink. The use of the fuzzy logic helps in identifying the node that has the highest trust factor and closer to the sink, thus improving the routing process. Then, CHs are selected on the basis of these factors. Simulation results showed that the proposed protocol improved the network lifetime and reduced the network overhead; therefore, the network lifetime and overhead were improved. To sum up, comparison tables for the surveyed papers that highlight the most common routing protocols/techniques in WSNs that use fuzzy logic algorithms are presented in Table 3. In this table, the comparison between various fuzzy logic routing protocols in WSNs is summarized. As shown in this table, energy consumption is considered to be the main problem in routing algorithms. Each sensor node is equipped with batteries with limited energy. The energy of each sensor node should be balanced in order to improve the network lifetime. Most algorithms in the literature focus on energy-efficiency with different modeling parameters. Residual energy has been used as main input for the fuzzy system. Other parameters have been utilized such as link quality, distance, and number of hops, and some algorithms have utilized cluster head selection in order to balance the energy and avoid the void region. Different fuzzy logic strategies have been utilized by the survey algorithms. FEARM [43] utilized different parameters such as link quality, energy consumption, physical distance, and available buffer, whereas FLGR [44] did not employ physical distance parameter. On the other hand, new and colony optimization routing [45], TTDFP [46], and LPO [47] focused on identifying cluster head based on different parameters. The main forwarding strategies in most algorithms have been designed to select the efficient sensor node that is closer to the sink node with minimum route cost
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Protocol** & **Objectives** & **Modeling Parameters** & **Fuzzy Logic Strategy** & **Forward Selection Strategy** \\ \hline
[43] & Reliability, delay aware, and energy-efficiency & Residual energy, link quality, distance information & Fuzzy logic system with input parameters (link quality, residual energy, physical distance, and available buffer) & The maximum value of the chance of becoming the next node \\  & & Number of hops, packet delivery ratio, parameters of assessments, candidate node & Fuzzy logic location region using numbers of assessments, and without location information of destination node & 1. Avoiding the void nodes. \\  & & & & 2. Fuzzification, fuzzy reasoning, and \\  & & & & de-trazzification. \\  & & & & de-trazzification. \\  & & & & \\