## 3 Literature Review
This modification includes the use of tent chaotic mapping to generate the mapping sequence, as well as the use of a nonlinear convergence factor, so that the convergence ability of the algorithm can be improved. Additionally, greedy cross-over strategy was used to enhance the accuracy of the solution. The second proposed version of the algorithm, called the non-dominated sorting multi-objective flower pollination algorithm (NSMOFPA), is a multi-objective optimizationalgorithm used to maximize the coverage rate, thus minimizing the energy consumption and node radiation overflow rates, assuming for heterogeneous sensor nodes that are deployed in forest environment having non-rechargeable batteries with obstacles. The global pollination problem is solved by using the external archive and leader strategies, and the diversity of the population can be maintained by using the proposed crowding degree method and elite strategy. These two proposed algorithms are applied to the WSN deployment problem, through extensive simulation experiments using the MATLAB 2014b program. After comparing with other optimization algorithms, they found that IFPA can provide enhanced in-network coverage and deployment cost. Additionally, the NSMOFPA has a better optimization solution for WSN deployment. Smart parking is one of the smart city applications. [PERSON] et al. (2021) [38] developed a new optimization algorithm for WSN system deployment for fire detection in smart car parks called multi-objective binary integer linear programing (MOBILP). The nodes in these networks are divided into two groups, i.e., sensor nodes for target monitoring and relay nodes that receive alert messages from the sensor nodes. This proposed algorithm aims to simultaneously minimize the number of sensor and relay nodes, as well as decrease the maximum distance between sensor and sink nodes, while ensuring coverage and connectivity. They evaluate this method through different tests and compare the results with existing work, such as the mono-objective function. Energy consumption and high cost are the disadvantages that present when deploying homogenous or heterogeneous WSNs to solve coverage problems. So, [PERSON] et al. (2020) developed a deployment model that depends on the probability sensing model (PSM) and harmony search algorithm (HAS) to attain the balance between the coverage performance and cost of heterogeneous WSN. PSM is used to solve the overlapping problem between nodes. The proposed model is evaluated through multiple simulation scenarios, using MATLAB, by analyzing the coverage ratio and cost and comparing the obtained results with those from the scenarios that used a homogenous model and meta-heuristic algorithm, such as the genetic algorithm [39]. WSN lifetimes depends on energy consumption and the covered area. To have efficient coverage and energy usage, there must be an optimum network deployment because it affects all network performance. [PERSON] et al. (2021) proposed an optimization deployment algorithm that used glowworm swarm optimization, K-means algorithm, and Voronoi cell structure for optimizing coverage and energy consumption with a minimum number of nodes, multi-hop transmission, and sleep-wake mechanism [40]. To increase network lifetimes, network clustering is used, which means dividing the network into virtual groups. For each group there is a cluster head, with powerful capabilities, that is responsible for gathering data and sending it to the base station, either directly or through multi-hope routing. Cluster performance is a very important issue, since it affects network lifetimes. [PERSON], [PERSON] et al. (2021) proposed a modified genetic algorithm-based load-balanced clustering algorithm for WSN (MGALBC) that depends on residual energy. Then, compare it with the (GALBC) algorithm. The new suggested algorithm shows an enhancement in network lifetime, energy consumption, and the number of active sensor nodes [41]. [PERSON] et al.