## 4 Fuzzy Logic-Based Routing Algorithms
FLGR is composed of three phases--it starts by determining the next forwarding nodes on the basis of the fuzzy location region of the receiver node. Next, the best forwarding node is selected on the basis of the following three steps: fuzzification, fuzzy reasoning, and de-fuzzification of the node's parameter. Lastly, in the routing process, avoid avoidance scheme was designed and developed to avoid the void nodes in trap areas. Simulation results demonstrated that FLGR can avoid the trap area efficiently. Moreover, FLGR performed a high delivery ratio and reduced routing overhead compared with existing protocols. [PERSON] [45] conducted a novel research study focusing on prolonging the network lifetime named \"a new ant colony optimization routing approach based on fuzzy clustering\". This approach is composed of two phases: clustering and routing. In clustering, a local self-selection process by an ordinary node is applied to be a cluster head, and then the decision among neighbors in the network is advertised. This process is split into two phases: setup and steady. In the first phase, an ordinary node uses a fuzzy system to calculate its chance of helping in deciding to be a cluster head on the basis of residual energy, number of neighbors, received signals, and centrality. The node with a higher chance becomes a cluster head and determines its members. On the other hand, clusters are fixed until the end of the steady phase. This approach is repeated continuously until the end of the network. In the routing phase, an inter-cluster routing was proposed on the basis of ant colony optimization as per the following steps. First, finding the best relay node for cluster head on the basis of neighbor information data collection by cluster head. Second, finding a path towards the base station using ants in the cluster head, helping in determining the next hop. In the third step, ants that passed by the base station collect path information. Moreover, each cluster head can find the best cluster head candidate to forward its packets. Lastly, the transmission process starts forwarding the data packet. The proposed approach was compared with other studies in the literature, showing that the proposed work outperformed in terms of routing efficiency and network lifetime. In order to enhance the data aggregation efficiency in a multi-hop wireless sensor network, [PERSON] et al. [46] introduced a two-tier distributed fuzzy logic-based protocol (TTDFP). In tier I, TTDFP selects the optimum cluster head through an energy-based competition of provisional leaders using a probabilistic model. In all phases, the inclusion of a central decision-making point does not require in TTDFP. In the fuzzy clustering phase, TTDFP handles specific events in the clustering phenomena more efficiently compared with its crisp and other fuzzy counterparts. Moreover, TTDFP utilizes the optimization framework to tune the two parameters in this tier, which are the threshold and the maximum competition radius, rather than the use of a trial-and-error approach to find the right blend of these parameters. Figure 6: The fuzzy logic system used for routing process. On the other hand, in the second tier (tier II), fuzziness enhances routing performance in comparison with its crisp counterpart. In mission-critical sensitive scenarios, TTDFP is an efficient approach to be used, where utilization context may be updated concerning the application field. In the clustering phase, TTDFP uses three main inputs: the remaining node energy, distance, and relative node connectivity. In the routing phase, TTDFP increases the operation architecture of multi-hop routing methodology with the use of fuzzy logic. TTDFP employs two fuzzy parameters, namely, distance to the efficient routing path and residual energy. In order to evaluate the performance of the proposed protocol, several experiments were conducted, proving that TTDFP is an efficient protocol compared with other related work proposed in the literature.