# An Unequal Clustering and Multi-Hop Routing Protocol Based on Fuzzy Logic and Q-Learning in WSNs
## 2 Related Works  
Recently, several protocols employing unequal clustering have been developed to improve energy efficiency in WSNs. The EEUC [20] protocol uses a probabilistic threshold, \(T\), to determine tentative CHs. The final CH is selected by choosing the node with the highest residual energy within the competition radius. During the multi-hop data transmission phase, relay nodes are chosen based on their distance from the BS and remaining energy. The UCR [22] protocol designates CHs based on the residual energy of the nodes, thereby creating smaller clusters in proximity to the BS and larger clusters at greater distances. The EADUC [23] protocol calculates CH candidacy weights by considering both the remaining energy and the node's degree. Recent research has indicated that clustering in WSNs is influenced by various interconnected factors. Fuzzy logic effectively manages uncertainties and combines different parameters to obtain the desired results, making it highly effective for clustering in WSNs [24]. The CHEF [21] protocol probabilistically selects tentative CHs and employs a FIS to assess the likelihood of a node being elected as the final CH. This assessment is based on various inputs, including local distance and residual energy. The EADCF [25] protocol uses an FIS to calculate the competition radius, considering a node's remaining energy and its distance to the BS. It also probabilistically identifies preliminary CHs. Ultimately, the final CH is selected as the tentative CH with the highest remaining energy within this radius. To identify CHs, the EEFUC [26] protocol utilizes an FIS that evaluates factors such as node energy, distance to the BS, and the number of active nodes. CHs are responsible for data aggregation and multi-hop transmission to the BS. In EFUCA [27], each node can become a CH by making an educated decision using fuzzy logic. In the clustering stage, the fuzzy logic system uses the remaining power, proximity to the base station, and average distance to communicating nodes as inputs to determine the rank and competition radius. Another fuzzy logic system, whose inputs are the next-hop rank, proximity to the next hop, and distance reduced to the BS, is used to finish the next-hop selection for effective data forwarding in order to further extend the network lifetime. The FMCB-ER [28] protocol was introduced as a hierarchical routing approach designed to improve network longevity. It combines Bio-inspired Energy-efficient Routing with Fuzzy Multi-Criteria Clustering techniques. The protocol employs a grid-based clustering strategy to form robust clusters, leveraging adaptive fuzzy multi-criteria decision-making, which integrates TOPSIS and fuzzy AHP for optimal CH selection, along with the Emperor Penguin Optimization (EPO) algorithm to determine the most effective routing path for data transmission from CHs to the sink. [PERSON] [29] used the fuzzy grey wolf optimization (F-GWO) algorithm for cluster head selection as part of an energy-efficient clustering and routing protocol for a WSN. The opportunistic routing method, which minimizes power consumption and balances energy consumption among nodes in WSNs, was used, and the fuzzy parameters were evaluated with an algorithm that produced an enhanced route. However, the implemented algorithm did not take into account the fitness function of energy and distance that caused packet drop in the network. The Fuzzy Multi-Criteria Clustering and Bio-inspired Energy-efficient Routing (FMCB-ER) [30] protocol was presented as a hierarchical routing technique to increase the operational lifetime of networks. It uses a grid-based clustering strategy to create strong clusters and adaptive fuzzy multi-criteria decision-making, which combines TOPSIS and fuzzy AHP for the best CH selection and the Emperor Penguin Optimization (EPO) algorithm to determine the most effective routing path for data transfer from CHs to the sink.