## 6 Conclusions and Future Work  
In this study, a robust path planning scheme called NHExCurves is proposed for MANAL. The proposed model guarantees that it will receive messages from at least three collinear signals to find all UNNs in the network. Therefore, the proposed path provides full coverage and high localization accuracy. The localization performance of the proposed model is compared with the performance of five known and strong static path planning models in the literature in the obstacle-presence scenario. Except for the Z-curve [9], none of these path planning models have been tested in the obstacle-presence scenario. In this study, the obstacle-presence scenario in [9] is adopted to the other four path planning models. In this study, the localization performances of all models were compared for the first time in a regular-shaped obstacle scenario according to different evaluation criteria in the same environment. With the obstacle-handling trajectories used for the models, the negative effect of the obstacle to localization was tried to be reduced. Performance evaluation criteria examined in this study for path planning models can be briefly listed as localization error depending on different parameters, localization ratio by resolution, and scalability. In this context, the simulation results made in this study can be summarized as follows:  
1. Generally, for the WCL method, NHExcurves, and Hcurve models stand out compared to other models. For this localization method, the HILBERT model generally shows the worst performance. 2. In general, for the APT method, the H-curve model is the best performing model, but the proposed NHExCurves model is superior to other models, especially at low resolutions. For this localization method, the Z-curve model has generally been the worst performing model. 3. For the APT method, the localization performance of the LMAT model was obtained poorly at low resolutions. However, the localization performance of the same model at low resolutions in WCL was achieved very well. This shows that a path planning model that performs well in one localization method may not perform well in another method. 4. It can be said that the model proposed for both localization methods and all performance evaluation criteria stands out compared to other models at low resolutions. The condition of the proposed model in other conditions is also very good. Therefore, NHExCurves is an effective static path planning model that can be used in WSNs in environments with regular-shaped obstacles. Our future research topics in MANAL will likely be as follows:  
1. There is no investigation regarding the energy consumption or efficiency of the path planning model proposed in this study. One of our future works will be to calculate the energy consumption of the proposed model and plan a transmission power adjustment scheme to improve energy efficiency. 2. In this study, the proposed model and other models are tested in the area with regular-shaped obstacles. In future work, it is planned to observe the performance of these models in areas with irregular obstacles. 3. Using only one anchor, MANAL algorithms can take a long time to find all UNNs in an ROI, especially for large-scale WSNs. Therefore, the collaborative MANAL algorithm using several MAs can be designed to reduce localization time and improve localization accuracy. 4. In this study, it was assumed that MA did not go through UNNs. In future studies, the problem of collision of nodes will be examined, and in the presence of this problem, localization performances of path planning models will be compared. Improvement of localization performances in the case of collision will be tried with the existing solutions proposed in the literature to solve this problem. 5. Bluetooth 5.1 is a new technology aiming to improve indoor localization with techniques such as AoD and AoA [39]. Likewise, UWB technology is also very suitablefor indoor applications [40].