## 1 Introduction  
A wireless sensor network (WSN) is one of the widely adopted network systems in industry health care and in many intrusion detection areas. It comprises a network of devices defined as nodes. The nodes have the ability to sense the environment from the area under monitoring, gather information, and communicate the collected data by means of wireless links. The final destination node is called the destination node or sink node. The data from the initial node are forwarded through multi-hop routing to the destination node and the collected data are utilized by other networks through the gateway. The structure of the nodes can be moving or stationary, and they must be aware of their identity location. Since a WSN does not require cables or wires, it is flexible and may add new nodes or devices to the network at any time. The recent developments in WSN extended its application to sophisticated systems. The challenges in WSNs persist even with the modern developments [1]. A WSN is interconnected with various nodes and forms a network within a range. The expansion of coverage is determined by the type of application as well as the number of nodes and devices. The energy used by these nodes depends on the establishment and the remote areas that require an improved, energy efficient system [2]. The data collected by the sensor nodes must be transmitted consistently over a long period of time. The modern approaches and recent studies have shown that the data transfer happens through WSN and makes them consume more energy and reduces the network lifespan. The power resource constraint is critical, considered a lifetime factor for WSNs which cannot be replaced if they face certain technical issues in their application. For optimal routing mechanisms, the pattern of clustering and the decision of head selection for clusters are critical. The selection is based on the nodes having maximum energy and minimum traversing distance between the neighboring nodes with medium speed quality, followed by the patterns of clustering for the determination of routing paths. Algorithms in artificial intelligence should be employed for determining the shortest. It is the responsibility of the protocol to administer and update the routing table for effective routing and to minimize the energy consumption. Protocols used for routing mechanisms should focus mainly on the energy needed for the network operation. Many researchers have developed new techniques to overcome the energy efficiency problem in order to improve the WSN lifespan. However, these techniques are not sufficient to balance everything. Researchers have adopted hybrid approaches to take advantage of different techniques to achieve a balance in WSN. This is because an overperforming WSN results in premature node failures or dead nodes, indicating that nodes die with burden. If the nodes are used for a long time, they face premature failure [3]. To overcome this issue, recent studies have attempted to optimize routing to reduce the energy consumed and improve the network lifespan. The optimization of path in WSNs is important for solving the issue of redundant data forwarded into the network and for reducing energy consumption. Cluster formation helps in the process of minimizing the energy level, where data aggregation is an important criterion that needs to be addressed both at the level of the cluster head and at the base station. The process of data aggregation is performed at each router while forwarding the data packets. The packet transmission follows the working principle of routing protocols that involve many algorithms for the determination of optimal network data transfer and the pathway for communication between the sensor nodes. Various optimization techniques have been used [4] by researchers and developers in WSNs, such as Particle Swarm Optimization (PSO) [5, 6], Grey Wolf Optimization (GWO) [7], Crow Search Optimization (CSO) [8, 9], and Dragonfly Optimization (DFO) [10].