## 3 Review of Energy Efficiency in WSNs
### Lifetime Metric and Energy Efficiency in WSN  
Among the popular in WSNs are balancing and minimizing power usage [56]. An algorithm has been proposed to determine the path of following other nodes. The algorithm dynamically allocates dispersed energy to high-energy nodes to act as source routing nodes. PEGASIS provides an energy proficient mechanism for sensor information systems [57; 58]. It connects sensor nodes in a chain so that each node sends and receives information from its neighbor. One of the chain's links is then chosen to transfer data to the base station (sink). The technique reduces the overhead necessary to construct several CHs. This consumption increased lifetime from 8% to 14% while also achieving the goal of saving resources [59]. The adaptive duty cycle and an encoding method are used to synchronize the conversion and encoding. Because the sink node contains the majority of the bottleneck, this method is used there. The results showed that when the suggested adaptive duty-cycled network coding was used, sensor network lifespan increased by 2.5% to 9.5 %. Wireless Multimedia Sensor Networks (WMSNs) have been demonstrated to be an underutilized and inadequate energy-efficient routing mechanism. The routing protocols for WMSNs are plagued by design issues, which are exacerbated by the problems inherent in existing techniques designed primarily for managing non-multimedia data transmissions. Few studies have focused on various energy-efficiency issues in WSNs. Although it is mostly generated from efficient energy-saving technologies, it is also reliant on QoS Assurance for WMSNs [60]. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Methodology** & \multicolumn{2}{c}{**Features**} & \multicolumn{2}{c}{**Limitations**} \\ \hline
[50] & Fuzzy-based balanced cost CH selection & \(\bullet\) & Consider the node’s residue energy and distances between nodes balance energy consumption & \(\bullet\) & Ignore the possibility of CH failure High energy consumption \\ \hline
[51] & Distributed CH scheduling & \(\bullet\) & Use real data Dynamic CH selection based on the intensity of receiving signals & \(\bullet\) & small size of the system under study Constant status of sensors \\ \hline
[52] & Adelson-Velskii and [PERSON] tree rotation clustering model & \(\bullet\) & Minimize network energy consumption by producing two different models using multi models & \(\bullet\) & Disruption by losing one or more nodes high computational calculations for using multi models \\ \hline
[31] & Improved ABC algorithm with an enhanced solution & \(\bullet\) & Improve convergence with modified search space Balancing the energy of nodes in different clusters & \(\bullet\) & Did not consider the dynamic state in changing the structure of clusters Improper performance in presence of noise \\ \hline
[53] & Pareto optimization-based approach & \(\bullet\) & Improving the quality of communication Using real data Using real data & \(\bullet\) & Creating multiple interfaces to establish communication between nodes Need to search for optimal power to transfer data \\ \hline
[54] & Clustering algorithm basis of k-mean & \(\bullet\) & Balancing to increase the lifetime of nodes & \(\bullet\) & Complexity in estimating the k-value Significant time delay due to high computational efforts \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of different issues of clustering algorithms. The Energy Efficient Credit Based technique allows the selection of cluster leaders based on the relative importance of relay nodes [61]. This protocol maximizes the packet loss is greater and minimizes the data delivery rate. The protocol use number of nodes as candidates for data transfer. Directed Diffusion (DD) is adopted for flat routing and cluster architecture for hierarchical routing. DD adds the nodes that are not in the route of sink nodes to minimize energy waste.