# Sensor Topology Optimization in Dense IoT Environments by Applying Neural Network Configuration
## 5 Conclusions  
In the published literature, there are techniques in WSNs for joint coverage and energy optimization after the initial sensor deployment [33; 34; 35; 36; 37]. Having these techniques in mind, in this paper, we proposed a near-optimal solution in polynomial time applying NN configurations for the problem of joint sensor network energy and coverage constraint lifetime optimization. Therefore, we considered and compared several WSN topologies, in conjunction with several NN parameter configurations, and tested them in terms of the total network lifetime extension rate. This means that our solver tried to relocate a percentage of sensor nodes in order to minimize an objective function under certain coverage and connectivity constraints.  
Our simulations focused on three distinct and representative use cases, related to small-, medium-, and large-size network deployments, for precision agriculture applications. The simulation results show that our proposed solver achieved a network lifetime extension in all use cases while maintaining the constraints. The network lifetime extension factor was around 5% in all cases, ensuring a coverage percentage of 90% in all topologies. Moreover, in all network cases, the connectivity among the communicating node pairs was ensured. Our simulations show a trade-off between lifetime extension and coverage in terms of optimization constraints. This trade-off is a system parameter, which the network designer needs to fine-tune. A greater extension was achieved in the cases where the solver applied more hidden layers. In future work, we will consider experimenting with more NN configurations and parameter settings and enhance our communication model with power control optimization in some of the cases. A limitation of our proposed solution is that we tested it in a simulation environment. However, the solver was responsive to large WSN deployments. In essence, our work proposes a tool for a potential network designer to predefine and, afterwards, deploy the final topology.  
In future work, we plan to test our solution in real field conditions and discuss the potential limitations associated with the utilization of moving sensors for the final placement, under hybrid topologies with static nodes and moving ones. Moreover, we plan to test the solver's robustness, as far as several neural network configurations are concerned, and highlight which is the best for each network scenario.  
Conceptualization, G.P., A.X., C.C., D.K., P.C. and N.S.S.; Methodology, A.X., C.C., D.K., P.C. and N.S.S.; Software, G.P.; Validation, G.P. and N.S.S.; Formal analysis, G.P., D.K. and P.C.; Investigation, G.P., A.X., D.K., P.C. and N.S.S.; Resources, G.P.; Data curation, G.P.; Writing--original draft, G.P. and A.X.; Writing--review & editing, A.X., C.C., D.K. and P.C.; Supervision, A.X., C.C., D.K., P.C. and N.S.S. All authors have read and agreed to the published version of the manuscript.  
Figure 6: (**a**) Network lifetime extension rate for different network types and initial topologies (i.e., deterministic vs. random), for (2) and (3) hidden solver layers; (**b**) Network lifetime in rounds for deterministic and random topologies.  
Funding: This research received no external funding. Conflicts of Interest: The authors declare no conflict of interest.