## 2 Literature Review  
Energy efficiency is a key task for routing in WSN. The network lifetime and energy efficiency of earlier studies showed less efficiency; hence, optimized routing systems are used for IoT networks in 5G environments [15]. The k-means algorithm used for the clustering phase and the nodes in the network perform multi-hop routing to create a chain so that the data will be transferred to the control center, which ensures the efficient use of energy. Studies have generated a newer set of protocols [16] to overcome the packet routing challenges specifically for the optimized routing. The energy would be used to make predictions and at the time of connection failures. Hence, the energy efficient protocol is used along with fuzzy rules, which prevent link failures. As a result, energy loss during link failures is prevented. Similar to earlier studies, Exponentially Ant Lion Whale Optimization (E-ALWO) was used to optimize the routing [17]. The optimization by this hybrid algorithm is executed through energy, delay, trust, and distance. The integration of the exponentially weighted moving average is used to optimize the above parameters based on the fitness function.  
The hybrid approach in WSN networks has resulted in efficient outcomes, such as the hybridization of firefly and DFO. The static and dynamic properties of the DFO can be explored further and the firefly technique provides the optimal global solution though exploitation [18]. GWO is known for its search precision in IoT networks. Studies have enhanced it to provide an optimum solution [19]. Finding a global solution for the problem using GWO is enhanced but with the improved position update. In this work, it was used for the coverage problem and yielded effective results.  
GWO in node deployment could reduce the computational cost for the WSN. The appropriate position of sensor nodes' deployment has been tested under various coverage scenarios and connectivity configurations [20]. The multi-objective fitness function makes it efficient for optimizing the cost. The coverage optimization by the GWO algorithm with simulated annealing is the coverage optimization of the WSN. By optimizing the coverage problem, energy consumption and e redundancy of the nodes during distribution can be reduced, and the network life can be improved. Annealing enables the GWO properties and improves the global optimization ability and is found to be better than the PSO [21]. Delayed response time, queueing, and increased packet loss reduce the transmission rate. It could be controlled by adjusting the traffic load in the downstream node. This load adjustment could be achieved with multiple classifications [22].  
In summary, the above literature indicates that improving efficiency was achieved through various approaches. The optimization techniques mainly revolve around finding the optimum route in the WSN. The significant issue to alleviate energy consumption is the optimum path selection. Therefore, several authors have adopted different optimization techniques.  
Based on the literature review, the following objectives are framed for this study:  
1. To optimize the data transfer route in the WSN using the optimization techniques.
2. To develop a hybrid approach based on GWO and DFO to optimize the network lifetime and reduce energy consumption.