## 3 Literature Review
(2021) [42] tried to enhance WSN coverage and cost by proposing a multi-objective optimization algorithm (MOO) with variable-length decision space for sensor deployment in a 2D environment. This algorithm used social class multi-objective particle swarm optimization with V-length nature (SC-MOPSO). It expands the concept of social interaction of particle swarm optimization by dividing the solution space into classes according to their dimensions and combines inter- and intra-class operators, in order to confirm the required dynamics of the solution changes to satisfy the Pareto front. This algorithm was evaluated through a different experiment by comparing it with weighted sum variable length particle swarm optimization (WS-VLPSO) and nondominated sorting genetic algorithm (NSGA-II). One of the WSN applications is border surveillance. [PERSON], [PERSON] et al. (2021) conducted a real experiment on the Tunisia-Libya border. The aim was to achieve full coverage and connectivity through the deterministic deployment of sensor nodes. They took the parameters of sensor density, consumed energy, and quality of sensor coverage into account when trying to reach k-coverage and connectivity with a minimum number of sensor nodes. Different types of WSNs were used in this experiment, i.e., were the wireless multimedia sensor and marine wireless sensor networks, as border monitoring systems [36]. [PERSON], [PERSON] et al. (2021) [43] proposed an optimization deployment algorithm for the target wireless sensor network (T-WSN) based on an evolutionary-based non-dominated sorting genetic algorithm (NSGA-II) to solve a multi-objective problem of increasing coverage and connectivity for the target monitoring. Pseudo-codes were written, and multiple scenarios were implemented using the MATLAB simulation tool. The non-dominated sorting keeps the better solutions in multiple objectives simultaneously using dominant relation. The performance evaluation of this multi-objective algorithm was performed in terms of performance indicators, i.e., overall non-dominated vector generation (ONGV) and spacing (SP). This algorithm showed the best performance, after comparison with other algorithms. Considering the mobility, environment properties and using heterogeneous nodes in the WSN increases its deployment problem complexity. [PERSON] et al. (2021) [44] took these problems into account when designing their optimization deployment algorithm based on integer linear programming (ILP). The objective of this algorithm is to maximize coverage, while taking network lifetime, mobility, and heterogeneity as constraints. They observed and discussed the importance of subarea monitoring in this research. For large-scale monitoring areas, they proposed using a swarm intelligence meta-heuristic algorithm for network deployment. In a simulation experiment, they evaluated this algorithm by comparing the coverage ratio and energy consumption with other recent algorithms. While [PERSON] et al. (2021) [45] solved the problem of target and area coverage and connectivity in randomly distributed homogeneous and heterogeneous WSNs, considering both centralized and distributed nodes, using the steepest descent (SD) analytical deployment algorithm with Armojo and Wolf rules, instead of evolutionary algorithms. The proposed method was compared with the genetic algorithm, in the case of moving the sensor nodes towards the target. Through simulations, they found that it outperforms the genetic algorithm; however, in the case of considering both coverage and connectivity, besides managing sensor movement in the required area, a hybrid algorithm was used, which consisted first of the genetic algorithm to define the first positions of the sensor nodes and then using the steepest descent algorithm to move these sensors to the optimal locations. They found that this method can support the sensor's trajectory and better accuracy for network coverage and connectivity. To solve the energy-efficient coverage problem for randomly deployed mobile WSN with obstacles, [PERSON] et al.