# Sensor Topology Optimization in Dense IoT Environments by Applying Neural Network Configuration  
[PERSON]  
1 Department of Digital Systems, University of Thessaly, 41500 Larissa, Greece; [EMAIL_ADDRESS] (C.C. ); [EMAIL_ADDRESS] (D.K. ); [EMAIL_ADDRESS] (N.S.S.) 2  
[PERSON]  
1 Department of Digital Systems, University of Thessaly, 41500 Larissa, Greece; [EMAIL_ADDRESS] (C.C. ); [EMAIL_ADDRESS] (D.K. ); [EMAIL_ADDRESS] (N.S.S.) 2  
[PERSON]  
1 Department of Digital Systems, University of Thessaly, 41500 Larissa, Greece; [EMAIL_ADDRESS] (C.C. ); [EMAIL_ADDRESS] (D.K. ); [EMAIL_ADDRESS] (N.S.S.) 2  
[PERSON]  
2  
Footnote 2: email: {c.c. nodes. Therefore, deterministic planning is not always cost-effective. On the other hand, in random deployments, there is a high risk of network outage or partition in cases of total ad hoc deployments [5]. In this work, we apply the Voronoi diagram partitioning theory, as in [4], which is a deterministic approach and is geometrically designed to cover a certain terrain. Following this, we compare it via simulations and propose use case scenarios with random topologies (i.e., via Gaussian coordinate distribution), as in [6], and we give our results in terms of the overall network lifetime, communication, and coverage constraints. Our proposed optimization solver consists of several neural network configurations, with which we experiment so as to reach a near-optimal solution in polynomial time. For our WSN communication model, we apply a realistic power consumption model of a wireless communication subsystem, which has been proposed in many related works [7; 8; 9]. The model introduces the energy consumption for sending and receiving a \(P\)-bit packet over a distance \(d\). The energy required in wireless communication is related to the path loss model and is proportional to a loss factor of \(d^{2}\) for free space and \(d^{n}\) for multipath fading and obstacles [7]. According to the use case scenario, the terrain space, and the node volume, there are two dominant communication patterns: in the first one, all nodes communicate directly with the gateway in an one-hop fashion, and in the second one, a multihop scenario is applied, especially to keep the communication range low and, thus, the energy consumption low. However, in this case, we need many relay nodes and this may lead to a lower total network lifetime [8]. Thus, the network lifetime is closely related to communication energy consumption, as in the majority of WSN deployment cases, nodes have limited energy resources. Following on from this, radio communication to guarantee a connected network data flow graph is mainly affected by the transmission distance and the transmission power. Thus, sensor topology optimization is closely related to the optimization of both the sensor deployment strategy and the applied communication range [9]. The research community shows a strong interest in applying AI tools and neural network (NN) methods to ensure energy efficient WSN deployments [10]. In essence, NNs can play a dominant role in this direction due to their quite simple and parallel-distributed computation design, robustness, and autoclassification methods in the case of sensor data collection. Dimensionality reduction, sensor data classification, and behavior prediction may lead to lower communication costs and overall energy conservation. In this paper, we define a joint sensor network energy and coverage constraint lifetime optimization problem and approximate its near-optimal solution in polynomial time, applying NN configurations as a solver. To this end, we consider and compare several topologies, in conjunction with NN configurations, by relocating certain sensor nodes to new coordinates, thus satisfying the communication and coverage constraints. We evaluate our solution by testing several use case scenarios, highlighting the impact of WSN escalation (i.e., small, medium, and large network deployments) and communication patterns on the overall network lifetime.