# Sensor Topology Optimization in Dense IoT Environments by Applying Neural Network Configuration
## 2 Related Works and Motivation
Following on from this, a sensor node deployment technique is proposed in [18] as a constrained multiobjective optimization (MOO) problem. The proposed algorithm is a multiobjective evolutionary algorithm (MOEA), known as MOEA/D-DE, that uses a decomposition approach and employs differential evolution (DE). The aim is to find a sensor node deployment to maximize the coverage rate, minimize the network energy consumption, maximize the network lifetime, and minimize the number of deployed sensor nodes, while ensuring connectivity between each sensor node and the sink node for proper data transmission. A tree structure between the deployed nodes and the sink node for data transmission is assumed in this approach. Heuristic approaches are appropriate for near-optimal solutions, especially in NP-complete problems. To this end, a metaheuristic algorithm is proposed in [19], which determines the sensors' positions for area coverage maximization. As a part of the solution, the authors utilized the immune plasma algorithm (IPA), a unique metaheuristic algorithm that is based on the immune plasma therapy concept and the transfer of an antibody-rich fraction of blood from previously recovered patients to others who are considered critically ill. This optimization algorithm is used to determine the optimal solution to the maximum coverage problem, but does not deal with the minimum number of nodes, network lifetime extension, or connectivity assurance. The authors in [20] propose an approach which relies on exploiting the building information modeling (BIM) database to obtain real-time and valid information about a target area. The majority of the proposed schemes present possible solutions without taking into account the terrain structure and potential obstacles during the WSN topology design process. The proposed solution can be integrated as a plugin within BIM tools in order to optimize sensor deployment in real time, taking into account both nodes and obstacles, respectively. In order to optimize WSN deployment, after collecting useful data from sensor nodes and the BIM database, this approach relies on an evolutionary algorithm to solve the multiobjective problem for coverage, cost, and lifetime. The output is each sensor's optimal location in the smart building application. One of the major challenges in sensor deployment is to find the trade-off between conflicting network optimization objectives under certain connectivity constraints. As proposed in [21], an approach to deal with this is a constrained Pareto-based multiobjective evolutionary approach (CPMEA). It aims to find Pareto-optimal layouts which maximize the coverage and minimize sensors' energy consumption to prolong the network lifetime, while maintaining full connectivity between each sensor node and the gateway. To cover any type of FoI with a predefined number of sensors, a genetic algorithm is proposed in [22] with the purpose of finding the best sensor placement while ensuring maximum network coverage under sensor connectivity constraints. The authors propose the genetic algorithm for area coverage maximization (GAFACM), which covers all shapes of areas for a given number of sensors and finds the best positions to maximize coverage within the FoI, while ensuring connectivity between the sensor nodes. An additional WSN deployment approach, based on the gradient method and the simulated annealing (SA) heuristic algorithm, is proposed in [23], using the minimum number of sensor nodes. However, this work does not deal with maximizing the network lifetime, whereas in [24], the SA heuristic algorithm is applied along with an energy-efficient algorithm to arrange the placement of sensors in order to extend the network lifetime. The main function of WSNs is to gather the required information, process it, and send it to remote gateways. A large number of sensor nodes need to be deployed within a field of interest; therefore, finding the best node placement according to several constraints is a hard problem to solve because it escalates.