## 2 Related Works
The multipath was modelled by the multipath source node to numerous destinations by the multipath routing protocol. In the beginning, the nodes in IoT-supported WSN can be experimented with together and execute the CH selection through the Fractional Gravitational Search Algorithm (FGSA). After that, the multipath routing procedure can be performed based on the devised TSGWO, whereby the routing path was chosen through the consideration of the fitness variables, for example, QoS trust factors and variables. In [22], collision-aware routing utilizing the multi-objective seagull optimization algorithm (CAR-MOSOA) was modelled to address the efficacy of scalable WSN. The presented model uses the clustering procedure for selecting CHs for transferring the data from the source to destination, therefore constituting a scalable network, and enhancing the CAR-MOSOA protocol's performance. Though several models are available in the literature, there is still a need to enhance the energy efficiency and lifespan of the network. Most of the existing models do not take multiple input parameters for the objective function into account. Therefore, in this study, we develop a new HMSFO-EARS technique with multiple parameters in the objective function for an enhanced network performance. Table 1 provides an overall summary of the reviewed works. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Objective** & **Method** & **Metrics** & **Advantages** & **Limitations** \\ \hline [PERSON] & To develop an energy aware clustering with routing for IoT-assisted WSN & EACMRP-MS technique, TSA-based CH selection, T2 FL routing & Energy efficiency & Adjust routes via mobile sink trajectories & Requires extensive experimentation \\ \hline [PERSON] et al. & To introduce a routing technique for IoT-WSN & BiHCLR technique & Packet loss, error bit rate, transmission delay, lifetime of network, buffer occupancy, throughput & Alternate solution for energy aware routing & Low execution speed \\ \hline [PERSON] and [PERSON] [18] & To design an intelligent fault-tolerant data routing method for IoT-assisted WSN & Fault-tolerant scheme & Average packet delivery, energy consumption, throughput, network lifetime, delay & Node fault and link fault are detected and tolerated in a timely manner & \\ \hline [PERSON] and [PERSON] [19] & To introduce a reinforcement learning-related mobile sink method for dynamic & Q-Learning & Energy efficiency & Preserve network stability and improved routing & Requires extensive experimentation \\ \hline [PERSON] [20] & To present a cluster-related routing with intrusion detection & EECRP-SID technique, T2 FC, SSO based clustering & Energy efficiency, lifetime, intrusion detection & Intrusion detection & Need to improve performance \\ \hline [PERSON] and [PERSON] [21] & To design a routing technique for IoT-WSN & TSGWO technique, FGSA-based CH selection & Energy, lifetime, throughput, PDR & Involves route maintenance process & Low execution speed \\ \hline [PERSON] et al. [22] & To present a cluster-based routing in IoT-assisted WSN & CAR-MOSOA technique & Energy, lifetime, throughput, PDR, delay & Secure data transmission with minimal energy utilization & Requires extensive experimentation \\ \hline [PERSON] et al. [23] & To present a clustering-based protocol with arbitrary CH rotation & LEACH & Energy, lifetime & Reduces communication energy & Single-hop communication, arbitrary CH rotation \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of existing routing protocols in WSN.