## 2 Literature Review
### Energy Consumption  
Energy consumption plays a vital role in enhancing the performance and energy efficiency of the network. To reach this goal, many researchers have offered papers in terms of energy consumption in WSNs. In [16], the author introduces a novel routing algorithm in WSN for bacterial foraging and a mobile sink, named the ultra-innovative algorithm, which leads to energy efficiency. In the provided method, the number of sensor nodes is determined by two criteria including the energy amount on the battery surface and the distance from the sink ahead, which establish regular clusters, and assuming a multi-step routing scheme to link with the sink and consume uniform energy. The simulation results show better performance of the given method for energy consumption, throughput rate, end-to-end delay, signal-to-noise ratio (SNR), and delivery of data at a higher rate successfully to the sink. The authors in [17] describe an energy-efficient coverage optimization strategy based on the Voronoi glowworm swarm (VGS) optimization K-means algorithm. In this technique, GS Optimization, K-means method, and Voronoi cell optimize coverage area with the fewest number of living nodes considering the optimum sensing radius calculation for well-organized sensor deployment. Furthermore, the suggested solution extends the lifespan of the established network by lowering the energy required by the sensor nodes via multi-hop send and the sleep-and-wake technique. The simulation results show that the suggested strategy achieves more area coverage with the optimum number of living sensor nodes. In [18], the authors present an improvement strategy for reducing energy consumption and increasing network lifespan by expanding energy balancing in clusters across all sensor nodes to reduce energy dissipation during network communications. A CH selection mechanism underpins the improved method. Finally, the technique compares LEACH and other protocols in terms of network lifespan, number of CHs, energy usage, and the number of packets sent to BS. The simulation results demonstrate that the suggested technique is successful and that the energy consumption of WSN has been lowered, extending the network lifespan more than LEACH. To improve current systems for extending lifespan, authors in [19] offer a scheme that organizes sensor nodes for charging. To begin, an inspection algorithm is created to visit and inspect sensor nodes to identify the sensor nodes that require charging. Second, using a greedy charge method, calculate the shortest distance in which the WCV must go. The energy for nodes algorithm was presented to decide the ending point and when the WCF must return to BS. Simulation tests are also carried out to control the performance, and it was discovered that our suggested method outperformed existing systems in the literature using metrics. In [20], the authors investigate the influence of energy-aware routing on network performance. In software defined networking (SDN) with multiple controllers, a new energy-aware technique is proposed to decrease the number of active connections while accounting for in-band control traffic. Two heuristic approaches are developed to reduce overall power consumption: static network architecture and dynamic energy-aware routing. In simulations using real topologies and demand data, significant values of switched-off connections are obtained. Furthermore, the performance-agnostic energy-aware model influences critical network metrics such as control traffic delay, data channel latency, link usage, and ternary content addressable memory (TCAM) occupancy. Paper [21] provides a fuzzy logic (FL) and (LEACH) approach-based (PSO) technique for choosing the primary cluster head (PCH) and secondary cluster head (SCH) (FL).