## 4 Open Issues and Challenges
### Trajectory Planning of the UAV
#### 4.3.1 Static Path Planning
More algorithms and their characteristics are summarized and compared in the Table 7. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**Algorithms** & \(N_{\text{mrp}}\)1  & **Advantages** & **Disadvantages** \\ \hline  & & & & **Static Path Planning** \\ \hline Lines [70] & 1 & Low & High & The simplest path planning. & Shorter communication range. \\ SCAN, Double SCAN, Hilbert [71] & 1 & Medium & High & These algorithms are object to maximize the coverage. & They waste much of coll-linearity. \\ Circle, S-curve [72] & 1 & Medium & Medium & The collaboration so col-linearity & These algorithms leave the corner of the interest area. \\ MACL [73] & 1 & Medium & Low & In MACL, the vehicle moves along a spiral path and broadcasts ‘beacon’ (including the current location information) periodically. \\ PI [74] & 1 & Medium & High & Perpendicular Intersection (PI) is used to localize nodes. Vehicle moves in zigzag line with an angle within (0, \(\frac{\pi}{3}\)). & Energy consumption for turns are not considered. \\ LMAT [75] & 1 & Medium & Medium & In this algorithm, the vehicle moves along an equilateral triangle line to send ‘beacon’, through which, the sensors positions are well estimated even if the “beacon” are col-linear. & The authors fail to consider the energy consumption for turns. \\ S-type [76] & 1 & High & High & This algorithm achieves the shortest path through making the vehicle moves in ‘S’. The interesting area is divided into small square (with each has a size of \(\frac{R}{\sqrt{2}}\)), based on which the col-linearity problem is solved. & Much energy consumed for turns and low vehicle utilization. \\ WCL [77] & 4 & High & High & In WCL algorithm, four mobile vehicles were used. They form a three-dimensional space and move in 3D-space, thereby, a 3D space network could be covered. & Due to 3D localization, this algorithm consume much energy. Beacon col-linear problem are fail to sloved. \\ \hline  & & & & **Dynamic Path Planning** \\ \hline MBAL [78] & 1 & Medium & Low & MBAL works better when the sensors are irregularly deployed. & This algorithm fail to consider the obstacle case. \\ BRF, BTG [79] & 1 & Medium & High & In the two algorithms, the network topology was regard as a connected indirectly graph. In addition, they are able to localize all the sensors if they are densely deployed. & This algorithm fail to consider the obstacle case. \\ MBL (ndc) [80] & 1 & High & High & It is a dynamic trajectory planning algorithm. & The collaboration complexity is high when the network size is large. \\ MALS [81] & 1 & Medium & High & This algorithm is proposed based on clustering mechanism. In addition, it is designed for non-uniform and irregular deployment scenarios. & Due to it is based on clustering; thus, high localization delay is brought. \\ DREAMS [82] & 1 & Medium & High & In DREAMS, the sensory field information are not required in advance. & It performs normal when the node failure is presence. \\ Virtual Ruler [83] & 2 & High & High & In virtual ruler, it can be measured in different values from different point of view. It is applicable for obstructed networks. & In this algorithm, all unknown nodes are required to deploy an ultrasound receiver or “beacon”. \\ \hline  & & & & & \\ \end{tabular}
\end{table} 
Table 7: Comparison of existing path planning algorithms based on mobility.