## 4 Open Issues and Challenges
### Trajectory Planning of the UAV
#### 4.3.1 Static Path Planning  
In static case, the trajectory for mobile vehicles is determined in advance. Vehicle moves along the pre-determined trajectory strictly. The simplest case is that the vehicles move in Lines [70]. The vehicle moves in a predefined line and broadcasts gradient signals to localize the unknown node. The trajectory is a x-rays. Based on this, the algorithms can be SCAN, DOUBLE SCAN, and HILBERT [71]. In SCAN algorithm, the vehicle moves in one direction while it moves in both directions in DOUBLE SCAN method. In HILBERT SCAN scheme, the vehicle moves in the Hilbert pattern. They all have the same objective that is to maximize the network coverage, even in the corner. It can be noticed that some areas are visited frequently because of the linearity of the trajectory. Thus, curves were introduced. Circle and s-curve [72] are the critical methods to reduce localization collinearity [72] (in statistics, the co-linearity refers to the fact that the explanatory variables in the linear regression model are distorted or difficult to estimate accurately due to the existence of precise correlation or high correlation between the explanatory variables. Thus, in the localization mechanism, the localization could be well estimated if the co-linearity could be reduced.). However, this types of scan leave the corner of the region. Thus, mobile anchor node centroid localization (MACL) algorithm [73] was introduced. In MACL, the \begin{table}
\begin{tabular}{c p{142.3 pt} p{142.3 pt}} \hline \hline
**Objectives** & **Detail** & **Applications** \\ \hline \(\max C(\mathbb{S})\) & Maximization of the coverage of the whole sensing field. In some applications, it is better to make sure that all the nodes within the interesting area to be covered. & In [67], the authors formulate an area coverage reliability issue. Their proposed algorithm quantifies the network likelihood that the network can be in an operating state where the coverage condition is satisfied. & The authors in [21] proposed an effective path length reduction algorithm for UAV path planning which has a low computational complexity. The proposed algorithm not only used in control center but also in UAV controller. \\ \hline \(\min D_{ULAV}\) & Minimization of the UAV’s travel duration. This objective helps the system to save energy consumption. & In [22], the authors address the path planning issue with the objective of the minimization of UAV’s flying duration. Energy, fairness, collision and tracking between UAVs were considered. \\ \hline \(\max A(\mathbb{S})\) & Maximization of the localization accuracy. This objective helps the sensors within the coverage be localized with high accuracy. & In [23], the authors address the path planning accuracy problem on U-WSN. They proposed an novel algorithm which considers the accuracy of positioning and the efficiency of flight. \\ \hline \(\min E_{max}\) & Minimization of energy consumption. This is the main problem in almost all of the applications. & In [68], based on genetic algorithm, the authors proposed an energy efficient mechanism for autonomous UAV. In this algorithm, the authors considered the relief and obstacles. \\ \hline \(\max Q_{data}\) & Maximization of the quality of data communication. The main objective in almost all of the application due to their research based on the collected data. & The authors in [69] proposed two data collection protocols (IDGP and DDGP) based on infrastructure through plan the mobile path. \\ \hline \(\max L_{network}\) & Maximization of the network lifetime. The longer the network lifetime, the more collections of the network. & The authors in [30; 57; 58] proposed trajectory planning algorithms to extend the network lifetime. \\ \hline \(\mathbb{S}\) is the sensor set. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The objectives of path planning in U-WSN. mobile vehicle traverses the interesting area following a spiral trajectory and periodically broadcasting beacon packets which contain its current position.