# A Review of Machine Learning-Based Routing Protocols for Wireless Sensor Network Lifetime +
## 3 Routing on Wireless Sensor Networks
### Cluster Properties  
Cluster size:On total available nodes and the network transmission range, the lifetime of a WSN can be affected by its cluster size. Increasing the count of nodes in a cluster may lead to a decrease in total available node energy. More iterations may cause node energy depletion if there are a more nodes in the cluster. Cluster density:Energy consumption in CHs is proportional to the total number of cluster members. The cluster's density can change depending on whether it is a static or dynamic cluster. Detailed Procedure of Clustering Routing Protocols  
1. _Finalizing the number of cluster heads:_ after establishing the clustering number K with the scientific model and formula, the clustering probability, P = K/N, can be calculated. 2. _Initial clustering:_ To begin, the network must be initialized and initially clustered with LEACH, with the aim of identifying the temporary clusters and clustering of the head. At the same time, the temporary CH gathers data on the energy and location of each cluster node. The four main components of a fuzzy logic system (FLS) are the fuzzifier, the inferencing engine, the rules-based decision-making, and the fuzzifier. Entering the data and utilizing the existing purposes to recognize the grade of each applicable fuzzy set are the first step. The term \"fuzzy inference\" refers to the application of fuzzy logic to the task of drawing conclusions about an input in terms of an expected output. The depiction then yields recommendations for model identification or decision-making. When a fuzzy inference system generates an imprecise result, this is referred to as degradation. FCH: Fuzzy-logical CH selection (FCH) is performed [7]. It utilizes energy and node cognizance and focuses on the period of calculation to make a CH selection using an integrated procedure, FL. CHEF: In the paper [8], the authors propose a fuzzy-logic-based CH selection mechanism (CHEF). However, CHEF is used to book other nodes for BS and CH choices, as opposed to LEACH's \"hot spare\" approach. LEACH-FL: As stated in [9], LEACH-FL is an improved type of the LEACH algorithm. It employs a device like that of the LCH, albeit with parameters tailored to things like battery life, proximity to the sink, and network density. ICT2 TSK: Type 2 \"Takagi-Sugeno-Kang (TSK) as the FL system\" [10] is used in \"ICT2 TSK\", a sophisticated clustering algorithm. When compared to the TSK FL Type 1 system, it is used to pick CHs and more effectively accomplish the governing of uncertainties. By instituting a uniform zone of competition around each CH, it can gauge the load on the network and make informed decisions about how best to allocate power. SEP-FL: In a heterogeneous WSN, SEP-FL is a FL method that improves CH selectivity [11]. By adjusting the probabilities of each node's residual energy, this refined form of SEP makes use of CH selectivity. EAUCF: In [12], the author suggests using a fuzzy algorithm called Energy-Aware in an Unequal Cluster (EAUCF). DFLCR: To address the problems of network period and energy consumed in WSNs, the writer suggests a dispersed fuzzy-logic-based clustering routing (DFLCR) [13] protocol. In contrast to other protocols, the suggested DFLCR protocol considers the significance of local distance and reserved residual energy. Because the DFLCR protocol can regulate the cluster size, the best node can be chosen as the CH., Fuzzy logic (FL) has been used to address a wide range of issues plaguing WSNs. SIF: this is a swarm-intelligence protocol based on FL (SIF) routing and RE [14], sink distance, and distance to center to choose appropriate CHs. FBUC: An improvement on the EAUCF [15] is the fuzzy-based asymmetric UC (FBUC) method of grouping. According to FNB, FBUC outclasses the other two systems across all stages in terms of energy consumption and network lifetime.