## 3 Literature Review  
Numerous strategies are used in the literature to solve the deployment problem of WSNs for optimizing coverage and connectivity issues. One of them is optimization algorithms, which can be single- or multiple-objective; the single-objective optimization algorithm is not suitable for real applications, since optimizing one performance metric may inversely affect another metric; for example, in sensor node deployment, maximizing coverage needs large spread number of sensor nodes, and this will increase the consumed energy, hence reducing the network lifetime. For this reason, the solution goes towards using the multi-objective optimization algorithms in WSN deployment, which aims to satisfy multiple goals at the same time, with a set of constraints, which will be a real challenge. Meta-heuristic search algorithms have been widely used in this area, since they can provide multiple elements in the Pareto front in a single evaluation, as they have a population-based nature. These algorithms have the advantages of preventing the local optimum traps and reaching the global optimum points [33]. Within this line of research, [PERSON] et al. (2020) proposed an improved multi-objective genetic algorithm NSGA-II (non-dominated sorting genetic algorithm II) to implement a new suggested mathematical formula for 3D WSNs deployment problem with directional sensing ability, in order to maximize the coverage and minimize the deployment cost. Through extensive simulation, they proved the performance of the proposed formula, while using the proposed Bresenham line-of-sight coverage model and assuming real sensors and 3D environment models [34]. [PERSON], [PERSON] et al. (2020) [35] suggested a conceptual framework to maximize the sensing coverage, and lifetime and minimize the total deployment cost of WSN in the smart building by using the building information modeling (BIM) database, which includes all the required information about the building and uses the sensor parameters as input data to the proposed system; then, an evolutionary algorithm (genetic algorithm) will be used to solve the optimization problem. After that, this optimized solution will be visualized using the BIM plugin tool in an off-line and real-time 3D building model, considering heterogeneous sensors and different obstacles. The decision variable vector of the optimization problem will be the sensor node location in the smart building, while the constraint is to find at least one path between the sink node and each sensor node, in order to construct a connected graph. Due to the emergence of 3D data regarding urban terrain, [PERSON] et al. (2019) [36] used this data to implement a heterogeneous wireless directional sensor network deployment in smart cities by optimizing three objectives, i.e., coverage, connectivity quality, and lifetime, while simultaneously considering connectivity and reliability as a constraint. They used a graph-based 3D SPM signal propagation model, which evolved by employing a line-of-sight (LOS) model for simulating wireless signals and calculating the path loss and intensity of the signal at a given point. To solve the optimization problem, they proposed a distributed parallel multi-objective evolutionary algorithm (MOEAs), with a message passing interface (MPI) called a distributed parallel cooperative coevolutionary multi-objective large-scale evolutionary algorithm with multiple populations (DPCCMOLESEA-MP). Two types of terrain data were used, i.e., even and rough. This proposed algorithm was compared with other evolutionary algorithms, in terms of performance and operation time. Another optimization algorithm called the flower pollination algorithm was modified by [PERSON], [PERSON] et al. (2019) [37] to propose two new versions of the algorithm. The first is called the improved flower pollination algorithm (IFPL), which is a single-objective optimization algorithm used to maximize the coverage area of WSN deployment in an urban area, assuming for solar batteries and heterogeneous sensors with obstacles.