## 2 Related Work
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & \
\begin{tabular}{c} **Year/** \\ **Author** \\ **Name** \\ \end{tabular} & **Objective** &
\begin{tabular}{c} **Software** \\ **Used** \\ \end{tabular} & **Parameter** & **Future Scope** \\ \hline
[1] & 2019/ & \begin{tabular}{c} By integrating the \\ SecTrust system into the \\ RPL protocol, a simulated \\ exercise was conducted to \\ end. \\ \end{tabular} & \begin{tabular}{c} PhD \\ Research \\ Lack of \\ demonstrate the SecTrust \\ system’s effectiveness at \\ fending off Rank and \\ Sybil assaults. \\ \end{tabular} & \
\begin{tabular}{c} To increase the \\ network’s \\ integration of \\ trustworthy \\ packet drop rate. \\ \end{tabular} &
\begin{tabular}{c} Integrability \\ trustworthy \\ nodes that have \\ repeal their \\ battery life by \\ extending the \\ SecTrust-RPL. \\ \end{tabular} \\ \hline
[2] & 2019/ & \begin{tabular}{c} Utilizing the gain ratio to \\ assess how effectively the \\ clustering methods can \\ balance the energy \\ distribution among WSN \\ sensor nodes. A fuzzy \\ logic-based CH election \\ method, a k-means-based \\ clustering method, and \\ LEACH are contrasted \\ with the suggested \\ technique FL-EEC/D. \\ \end{tabular} & \
\begin{tabular}{c} Utilize the gain ratio to \\ assess how effectively the \\ system’s residual \\ power, the \\ distance to the \\ BS, the density \\ of the SN, the \\ compacting of \\ the SN, and \\ location \\ appropriateness. \\ \end{tabular} &
\begin{tabular}{c} The Gini index is \\ a reasonable \\ assessment tool \\ for assessing the \\ routing protocols’ \\ energy \\ effectiveness in \\ WSNs for the \\ metric of energy \\ distribution \\ balance. \\ \end{tabular} \\ \hline
[6] & 2020/ & \begin{tabular}{c} The method provided \\ reliable and insightful \\ learning through the use \\ of heuristic evaluation, \\ which was taken from AI. \\ \end{tabular} & MATLAB & \
\begin{tabular}{c} Throughput, the \\ ratio of packet \\ drops, \\ significant delay, \\ consumed \\ energy, \\ erroneous routes, \\ overhead on \\ networks, and \\ computational \\ cost. \\ \end{tabular} &
\begin{tabular}{c} To make the \\ system smarter \\ and fault-tolerant \\ by employing \\ certain \\ lightweight \\ machine \\ learning-based \\ approaches to \\ enhance the \\ SEHR technique. \\ \end{tabular} \\ \hline
[8] & 2019/ & \begin{tabular}{c} Developed an accessible \\ virtual structure that \\ serves as an intermediary \\ architecture between the \\ sink and the nodes while \\ sharing metadata and \\ query messages in order \\ to lessen the mobile sink’s \\ frequently current \\ location to all nodes. \\ \end{tabular} & \
\begin{tabular}{c} Average rate of \\ delivery, average \\ energy \\ \end{tabular} &
\begin{tabular}{c} The suggested \\ approach can be \\ further explained \\ to understand \\ network, and \\ absolute delay. \\ \end{tabular} \\ \hline
[31] & 2019/ & \begin{tabular}{c} To reduce the energy \\ consumption of WSNs, \\ hierarchical techniques \\ that utilize clustering \\ hierarchy are proposed. \\ [PERSON] \\ et al. \\ \end{tabular} & \
\begin{tabular}{c} TABLE \\ latency change of \\ transmission to a base \\ station could be carried \\ out using the nodes with \\ the highest residual \\ energy. \\ \end{tabular} &
\begin{tabular}{c} TABLE \\ timeframe, \\ HNA, the \\ lifespan of a \\ network, and \\ and will analyze \\ and throughput. \\ optimization. \\ \end{tabular} \\ \hline
[32] & 2019/ & \begin{tabular}{c} The proposed \\ experimental \\ \end{tabular} & \
\begin{tabular}{c} The proposed \\ technique can be \\ expanded to \\ manage a system \\ of mobile sinks \\ and will analyze \\ network traffic, \\ and throughput. \\ \end{tabular} &
\begin{tabular}{c} TABLE \\ timeframe, \\ expanded to \\ manage a system \\ of mobile sinks \\ and will analyze \\ the network \\ and throughput. \\ optimization. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative analysis of existing literature.