## 2 Related Works  
[PERSON] et al. [12] present a new routing system for balancing the energy consumption amongst every node and extend the WSN lifespan that establishes a score value allocated for every node beside a path as an integration of evaluation metrics. Particularly, the scoring method assumes the data of node densities at a particular region and the node energy level for representing the significance of individual nodes from the routes. [PERSON] and [PERSON] [13] examine PEGASIS (power-efficient gathering in sensor information systems), an adjacent optimum chain-based protocol that is a development on LEACH. In PEGASIS, all the nodes transmit only with nearby neighbours and turn broadcasting to BS, therefore decreasing the count of energy spent per round. [PERSON] et al. [14] introduce a directional transmission-based energy aware routing protocol termed as PDORP. The presented protocol PDORP has the features of either a power effective collecting sensor data system or DSR routing protocol. Additionally, the hybridization of GA and a bacterial foraging optimizer (BFO) was executed for the present routing protocol for identifying the energy-efficient optimum path. In [15], the authors have explained the principal systems of Route Discovery and Route Maintenance utilized by DSR, and they are demonstrated to enable the wireless mobile node for automatically creating an entirely self-organizing and self-configuring network between them. [PERSON] et al. [16] presented an energy aware clustering and multi-hop routing protocol with a multiple sink (EACMRP-MS) method for IoT-aided WSN. The study aim was to proficiently minimize the power consumption of IoT-SNs, thus raising the network efficacy of the IoT network. The recommended approach originally depends on the Tunicate Swarm Algorithm (TSA) for cluster assembly and cluster head (CH) selection, along with that for the TSA. Additionally, the type-II fuzzy logic (T2 FL) method was employed for optimally selecting the multi-hop routes, with many input variables utilized for achieving this. [PERSON] et al. [17] devised a biologically inspired cross-layer routing (BiHCLR) method for attaining effectual and energy conserving routing in the WSN-supported IoT. Primarily, the installed SNs will be organized in a grid form according to the grid-related routing method. After that, to allow energy conservation in BiHCLR, the fuzzy logic method was performed for selecting the CH for all cells of the grid. After that, a hybrid bio-inspired technique was utilized for selecting the routing path. The hybrid technique will combine Salp Swarm and moth search optimized methods. [PERSON] and [PERSON] [18] proposed an intelligent fault-tolerant method in which distinct faults within the WSN-assisted IoT such as a link fault and node fault will be promptly identified and tolerated. It suggestively enhances network reliability. In [19], the authors modelled a robust reinforcement learning-related mobile sink method for dynamic routing and data collection. Moreover, the Q-Learning technique was applied for inducing automatic learning by the shortest route. Integrating such techniques will preserve stability of the network and efficiently enhances the routing performance along with the reward. [PERSON] [20] presented an energy-efficient cluster-related routing protocol with secure IDS (EECRP-SID) for WSN. This aforementioned approach includes three main stages; they are intrusion detection, cluster construction, and optimal path selection. Primarily, the type-II FL-oriented clustering (T2 FC) method with three input variables can be implemented for selecting CH. Along with the CH selection, the Salp Swarm optimization (SSO) method was leveraged for selecting optimal routes for inter cluster data communication, which leads to energy-effective heterogeneous WSN. [PERSON] and [PERSON] [21] introduced the multipath routing protocol, utilizing the devised optimized technique, called the tunicate swarm grey wolf optimizer (TSGWO) methodology in the IoT-supported WSN.