## 2 Literature Review  
[PERSON], [PERSON] et al. [11] proposed a cluster-based routing with simulated annealing and genetic algorithm-based hybrid (SAGA-H) method. The presented technique was simulated and explained by utilizing MATLAB. Furthermore, the observed results were compared to a contemporary genetic algorithm (GA)-based method regarding average residual energy, network lifespan, and the number of packets transmitted between the BS and sink. [PERSON], [PERSON] et al. [12] introduced a multiple-objective founded gathering and Salfish Optimizer (SFO)-directed protocol to augment energy efficiency in WSNs. The CH is chosen based on fitness criteria that are expressed from multiple objectives. It assists in reducing the number of dead sensors and minimises energy utilization. Following CH selection, SFO is utilised to select an optimum route to a sink node for transmitting information. The presented method was analysed, and results were compared to current techniques. [PERSON], [PERSON] et al. [13] proposed a novel hybrid neural network (NN)-based energy-effective routing method by using routing protocol (RP). Firstly, the sensors are clustered by the mean shift clustering method. Next, the bald eagle search approach chooses the CH to clustered node. Subsequently, RP is chosen rather than visiting each CH. Then, the RP is selected according to the weight assessment among hop distances and the amount of transferred data packets. At last, a hybrid NN using group teaching algorithm was presented for determining the optimal route. [PERSON] et al. [14] presented an IoT-assisted cluster-based routing (CBR) method for information-centric WSNs (ICWSN), named CBR-ICWSN. The proposed method undergoes a black widow optimization (BWO)-founded gathering approach for efficiently choosing an optimal set of CHs. Furthermore, the CBR-ICWSN approach comprises an oppositional ABC (OABC)-based routing method for better election of routes. [PERSON] et al. [15] proposed a robust cluster-based routing protocol (RCBRP) to recognize the routing path by which minimal energy is expended, in order to enhance network lifespan. The presented method was composed of transmission and explored ow methods. Then, is the study presented two approaches: (i) distance and energy consumption assessment method, and (ii) routing and energy-effective bunch technique. [PERSON] et al. [16] presented a stability-aware cluster-based routing (SACR) approach for CRSN. In terms of cluster formation, the study presented range subtleties and energy utilisation in the clustering method. [PERSON] et al. [17] proposed a metaheuristic artificial intelligence approach based on the social behavior of grey wolves, in order to minimise the energy utilization of WSNs from the livestock industry. The energy level, grid size, transmission range, and direction were the major parameters utilised for measuring the algorithm's performance. The following Table 1 describes the existing approach methodologies. [PERSON] et al. [18] presented LPWANs, a multi-hop data directing approach. Since the multi-hop data program encounters a number of obstacles, including augmented data dormancy, increased meddling, and decreased data quantity (i.e., inefficient utilisation of bandwidth), we suggest a reinforcement learning strategy to handle these obstacles. The suggested technique updates the network's Q-matrix at periodic time intervals and picks relay devices to exploit the increasing prize rate between designated device-gateway pairings. Furthermore, we present a novel data routing mechanism that optimises the energy rate of the cables. This strategy results in unchanging energy use and accelerated data transport. On a WSN testbed, simulations and actual node deployments are used to conduct experiments [19]. In order to prevent sensor nodes from behaving selfishly in future operations, a consequence instrument is developed to compel device nodes to embrace cooperative methods.