# Sensor Topology Optimization in Dense IoT Environments by Applying Neural Network Configuration
## 2 Related Works and Motivation  
A WSN designer places sensor nodes inside a field of interest (FoI) according to the application coverage requirements. These nodes, due to their transceivers, have the ability to cover either small or medium areas by sending data to a gateway node. The energy required for data communication is usually greater than the circuitry energy consumption [11]. In the majority of WSN and IoT applications, nodes are battery-equipped [6]. However, in cases where there is great difficulty in replacing batteries (i.e., harsh terrain, a large volume of nodes, etc. ), researchers' interest focuses on mechanisms to extend the network's lifetime under communication and coverage constraints. Topology control plays a dominant role in achieving this target. Thus, optimal sensor node placement within the FoI is needed for cost-effective deployment. The authors in [12] propose a sensor placement algorithm, which utilizes a biologically inspired optimization technique to imitate the behavior of territorial predators in marking their territories with their odors, known as the territorial predator scent marking algorithm (TPSMA). The TPSMA technique is based on maximizing a coverage objective optimization function. The problem of determining the location of sensor nodes, such that the terrain target points are covered and the network lifetime is maximized, is called the sensor deployment problem (SDP). Various heuristics and approximation algorithms have been proposed to solve the SDP in WSNs. Two improved versions of the particle swarm optimization (PSO) algorithm are presented in [13]. The first one is a cooperative PSO and the second is its improved version, applying fuzzy logic. These approaches do not deal with maximizing the coverage area or prolonging the network lifetime. A properly designed and applied sensor deployment strategy improves WSN performance and resource management [9]. The coverage ratio is directly influenced by the deployment strategy. In principle, there is no positive correlation between energy consumption minimization and coverage maximization. Thus, to maximize the coverage area, sensor nodes should be placed far away from the sink node (SN) or the gateway node, which takes higher transmission power to reach the SN and raises the total energy consumption. This problem is partially solved if an energy-efficient and multihop routing algorithm is applied [7] instead of a one-hop communication pattern. However, in several precision agriculture applications [3], the terrain's structure may block multihop communication patterns (i.e., tall trees) and, thus, the one-hop pattern along with an energy- efficient topology design may solve the energy problem. In essence, in this work, we follow this design pattern. Following on from this, nature-inspired optimization algorithms are adopted by many researchers in WSN applications [14]. While genetics, ants, and particle swarm algorithms are the dominant examples, many others emerge regularly such as the flower pollination algorithm (FPA), which is a novel global optimization algorithm inspired by the pollination process of flowers [14]. Based on the multiobjective version of the FPA (i.e., MOFPA) for WSNs, a new approach is proposed in [15]. This approach aims to find the optimal topology deployment, taking into consideration conflicting objectives, such as total energy minimization and total coverage maximization, while maintaining connectivity constraints. The network's lifetime primarily depends on the total energy consumption, which is mainly related to the nodes' radio electronics energy consumption. An energy-efficient coverage optimization technique, the Voronoi-glowworm smarm optimization-K-means algorithm, is presented in [16]. In this approach, the Voronoi cell structure enhances the area coverage, applying the minimum required number of active nodes. The Voronoi diagram is one of the most famous computational geometrical structures applied in sensor topology design problems to ensure coverage extension [17].