## 2 Literature Review
A new model for the clustering phase was developed, considering the energy consumption of both gateways and conventional nodes and routing. The suggested IHSCR outperformed the prior clustering algorithms in balancing the sensor nodes' energy consumption (especially regular nodes), greatly enhancing the WSN energy efficiency and network lifespan [21]. The authors proposed a jellyfish dynamic routing protocol (JDRP) to protect location privacy and avoid minimum latency. A novel routing channel was planned to regularly combine the information from tentacle nodes, demonstrating how data were sent in a possible angle orientation, while using less energy and extending the lifetime. JDRP's simulation results were compared to the QDVGDD, QWRP, and starfish routing protocols. The findings indicated that JDRP outperformed the other network characteristics and efficiently routed data packets to the dynamic sink [22]. The authors suggested the use of a cluster-based dynamic energy-aware routing system for agricultural WSNs. A routing protocol based on the gateway cluster energy efficient centroid (GCEEC) was provided in this work. In this protocol, gateway nodes were chosen from each cluster, and the cluster chiefs were chosen from the centroid locations. Data from cluster head nodes was routed to base stations through gateway nodes. The suggested methodology located and span the CH at the cluster's energy centroid. The simulation results showed that the protocol outperformed the EECRP regarding network longevity, throughput, and energy usage [23]. A protocol for energy-efficient clustering and immune-inspired routing for a WSN-assisted IoT system was proposed based on adaptive fuzzy rules. An FEEC-IIR protocol was proposed in this research as an adaptive fuzzy rule-based, energy-efficient clustering, and immune-inspired routing mechanism for WSN-assisted IoT devices. Good performance results were shown for the quality-of-service measures, such as packet delivery rate (99%), packet loss rate, throughput (0.95 Mbps), net-work lifespan (5500 rounds), and end-to-end latency compared to the current routing protocols (45 mJ) [24]. A WSN-based approach for efficient fuzzy-based cross-layer routing protocol was explained. The study focused on locating sensor nodes and promoting cross-layer effective route finding for data transmission reliability in the network. The objective was to follow the sensor nodes' position and activity. A new technique named the fuzziness-based contiguous node refining algorithm was proposed to analyze its behavior. NS2 simulations demonstrated that it outperformed current approaches of ELOER, EFLOR, SSPRA, DRPC, and TSTP in connectivity ratio, end-to-end latency, throughput, overhead, and energy usage [25; 26]. An effective energy-saving system offered a reduction of energy use and extension of network life. One of the most typical methods for lowering WSN energy usage is clustering technology. The fundamental concept behind this approach is to employ clustering methods to shorten the communication distance between sensor nodes; using the K-medoids approach to identify the ideal intersections of the sensor nodes and to obtain flawless cluster results, and then to decide on the proper cluster heading. By distributing the network load among clusters, it is possible to increase the energy efficiency, while increasing the network's lifespan. According to simulation data, the approach surpassed the other widely-used algorithms regarding power usage and network longevity [27]. The authors suggested a multimodal population-based iterative heuristic global optimization method called the sunflower optimization algorithm (SFO). SFO uses concepts such as root speed and pollination to achieve improved robustness compared to conventional algorithms. The issue of identifying structural degradation in composite laminated sheets in reverse was then addressed using this new methodology.