## 2 Related Work
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Year** & **Advantages** & **Shortcomings** \\ \hline \multirow{4}{*}{RIM} & \multirow{4}{*}{2008} & The inversion movement of neighbour nodes is used to recover a failed node, restoring both connectivity and coverage. & RIM is hampered by a large number of nodes. As a result of \\  & & The inversion movement of neighbour nodes is used to & its propensity to cause a large \\  & & & recover a failed node, restoring \\  & & & both connectivity and \\  & & & moved. This increases the \\  & & & network’s total overhead \\  & & & distance. \\ \hline \multirow{4}{*}{PADRA} & \multirow{4}{*}{2008} & \multirow{4}{*}{2008} & As a result of the movement of \\  & & By locating the cut-vertex & the cascade. During the \\ \cline{1-1}  & & ahead of time, less network & connection restoration phase, \\ \cline{1-1}  & & disjointedness can be avoided. & all participating nodes expend \\  & & & more energy. \\ \hline \multirow{4}{*}{C3R} & \multirow{4}{*}{2010} & Node failure will not affect \\  & & connectivity or coverage any \\  & & further because the nearby \\ \cline{1-1}  & & nodes of the failed node will \\ \cline{1-1}  & & perform back-and-forth moves \\ \cline{1-1}  & & from their original positions to \\ \cline{1-1}  & & those of failed node. & \\ \hline \multirow{4}{*}{VCR} & \multirow{4}{*}{2010} & VCR solves the connection \\  & & problem by choosing nearby \\ \cline{1-1}  & & nodes that are closer to the \\ \cline{1-1}  & & cut-vertex node. & reduced in a dynamic context. \\ \hline \multirow{4}{*}{Movement  assisted  sensor  deployment} & \multirow{4}{*}{2012} & The cascade motions of all \\ \cline{1-1}  & & surrounding nodes of the \\ \cline{1-1}  & & failing nodes provide a \\ \cline{1-1}  & & mechanism for restoring \\ \cline{1-1}  & & connectivity in this technique. & \\ \hline \multirow{4}{*}{CRAFT} & \multirow{4}{*}{2015} & In this approach, a BP \\  & & (Backbone Polygon) is built \\ \cline{1-1}  & & around the disjoint network’s \\ \cline{1-1}  & & core. There are no partitions \\ \cline{1-1}  & & within that network. & \\ \hline \multirow{4}{*}{GSR} & \multirow{4}{*}{2016} & A geometrical skeletal \\  & & backbone is constructed, \\ \cline{1-1}  & & consisting of a group of nodes \\ \cline{1-1}  & & with the greatest number of \\ \cline{1-1}  & & connections to other nodes. \\ \hline \multirow{4}{*}{HRSRT} & \multirow{4}{*}{2017} & The main goal of this algorithm \\  & & is to reduce the amount of \\ \cline{1-1}  & & energy and money spent on data \\ \cline{1-1}  & & compilation and acquisition by \\ \cline{1-1}  & & changing (c) and (d) (P). \\ \hline \multirow{4}{*}{SACR} & \multirow{4}{*}{2017} & The data load levels of disjoint \\ \cline{1-1}  & & segments are taken into \\ \cline{1-1}  & & account in this technique. The \\ \cline{1-1}  & & connection of the partitioned \\ \cline{1-1}  & & segments is examined in this \\ \cline{1-1}  & & technique. \\ \hline \multirow{4}{*}{CDS} & \multirow{4}{*}{2019} & Two algorithms are used in this \\ \cline{1-1}  & & strategy. The first algorithm is \\ \cline{1-1}  & & used to determine which nodes \\ \cline{1-1}  & & are critical and which are not. \\ \hline \end{tabular}
\end{table} 
Table 1: Algorithms for related work.