## 2 Literature Review
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Title of Paper** & **Method Adopted** & **Conclusions** \\ \hline Wireless Sensor Network Localization Based on Cuckoo Search Algorithm [13] & Cuckoo Search Algorithm & Providing greater accuracy in locating unknown sensor nodes than Particle Swarm Optimization (PSO) and Biogeography-Based Optimization (BBO) variants in a distributed and iterative manner. \\ \hline Modified Bat Algorithm for Localization of Wireless Sensor Network [14] & Modified Bat Algorithm & Better convergence rate (less computing time) and high success rate (more number of localized nodes) as compared to original bat algorithm. \\ \hline Node Localization in Wireless Sensor Networks Using Butterfly Optimization Algorithm [15] & Butterfly Optimization Algorithm & Improved WSN performance with respect to localization errors and decreased computations. \\ \hline Localization in wireless sensor networks using particle swarm optimization [16] & Particle Swarm Optimization & This technique copes with the problem of the solution being caught in the local minima that is inherent in gradient search non-linear optimization problems. \\ \hline Bio-Inspired Node Localization in Wireless Sensor Networks [17] & Distributed Iterative Localization & PSO and bacterial forging algorithm (BFA) are discussed, concluding that trade-off issue is there. PSO finds the node position more quickly while the BFA finds it with greater precision. \\ \hline Bio-inspired algorithm for optimizing the localization of wireless sensor Networks [18] & Hybrid approach (PSO and BFA) & Having faster convergence speed and higher rate of accuracy. \\ \hline A genetic algorithm for the minimum cost localization problem in wireless sensor networks [19] & Genetic Algorithm & Investigated minimum cost localization problem using genetic algorithm and the results reached solution more than 50\% better. \\ \hline Flower pollination algorithm based localization of wireless sensor network [20] & Flower Pollination Algorithm & Success rate of localized nodes and accuracy of node localization are improved. \\ \hline Optimized range-free 3D node localization in wireless sensor networks using firefly algorithm & Range-free localization using firefly algorithm & Optimized edge weights between the anchor nodes and the target node are used to find the location of the target node. Fuzzy Logic System (FLS) is used to overcome the problem of nonlinearity between the Received Signal Strength (RSS) and distance. \\ \hline Multistage localization in wireless sensor networks using artificial bee colony algorithm [22] & Artificial Bee Colony algorithm & Enhanced localization accuracy without increasing computational time. \\ \hline Bio Inspired Distributed WSN Localization Based on Chicken Swarm Optimization [23] & Chicken Swarm optimization & Having more precise accuracy with a ratio of 55\% over PSO and Binary Particle Swarm Optimization (BPSO) and 10\% over Penguin Search Optimization Algorithm (PeSOA). For computation time, proposed algorithm requires a computation time that is shorter by 30\% than PeSOA as well as 50 and 40\% than PSO and BPSO, respectively. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of meta-heuristic algorithms in WSN Localization. In this research, a new approach for constructing a virtual anchor node in a 2D situation is used to establish the exact location of movable target nodes in WSNs to increase accuracy and lessen localization mistakes utilizing a mLebTLBO method.