## 2 Related Work
In the proposed framework, the relay nodes are deployed optimally and therefore the proposed framework achieves significant energy efficiency in ST-WSNs by reducing the average length of the route. Next, [PERSON] et al. [34] have proposed a multi-objective model in ST-WSNs. The proposed multiobjective model focused on optimizing the RNs as well as energy dissipation simultaneously. Further, they proposed a hybrid evolutionary framework consisting of two local searches for providing a solution to the constrained RN placement problem. Further, [PERSON] et al. [35] have proposed excellent dual frameworks for RN placement. The proposed frameworks provide k-connectivity of the SNs. In the proposed dual frameworks, the Genetic Algorithm (GA) is utilized in the first framework while the greedy approach is used in the second proposed framework. Furthermore, [PERSON] et al. [36] have proposed an optimal framework utilizing a modified genetic algorithm for the placement of RNs in ST-WSNs. In this constrained RN placement approach, the proposed framework aimed at deploying the least possible RNswith effective connectivity. Further, the proposed framework assures fault tolerance in ST-WSNs. Besides, [PERSON] et al. [37] have conducted extensive studies for optimal deployment of RNs in ST-WSNs. They focused on optimizing the two important metrics, namely, the average consumption of energy as well as average reliability of the network simultaneously. They utilized three metaheuristics, such as Archive-Based hYbrid Scatter Search, Particle Swarm, and Non-dominated Sorting Genetic Algorithm, for addressing this multiobjective optimization problem. The metaheuristics that are utilized employ varied evolutional policies. Moreover, [PERSON] et al. [38] have proposed an efficient RN placement framework. The proposed framework supports network reconfiguration. The proposed framework is based on Differential Evolution (DE)-based algorithms. Further, the proposed framework assures a deterministic approximation ratio along with tight time complexity. [PERSON] et al. [39] also have proposed an optimal RN placement framework that is based on the multiobjective Firefly Algorithm. The proposed framework aimed at deploying the least possible RNs with consideration of three constraints, namely, energy, connectivity, and coverage. Few articles recently have been published for contributing towards fault-tolerant routing: [PERSON] et al. [40] proposed a fault-tolerance routing framework for improving the QoS in WSNs. In the proposed scheme, faulty nodes are identified, and partial faulty nodes are used to handle the faults in WSN. [PERSON] et al. [41] presented a multipath fault-tolerant routing framework utilizing clustering for WSN. In the proposed framework, an enhanced version of elephant herding optimization is proposed for selecting the multiple routes. Next, [PERSON] et al. [42] proposed a multipath fault-tolerant routing framework for WSN. In the proposed framework, clustering is performed by using a modified teaching-learning optimization scheme. Further, pigeon optimization based on a nonlinear regression is used for computing the backup node for increasing the fault tolerance. Finally, the optimal path was calculated using a deep Kronecker neural network. Furthermore, [PERSON] et al. [43] presented a fault-tolerant routing scheme in WSN. In the proposed scheme, clustering was performed using moth flame optimization. Further, the optimal routes were selected using social spider optimization (SSO) in WSN. Finally, we have selected two existing frameworks, namely, the artificial bee colony-based framework and the genetic algorithm-based framework, to compare the effectiveness of our proposed framework (FRGNWS) against them. In the next paragraph, we briefly provide the features of these two existing frameworks. [PERSON] et al. [44] have proposed the framework for placing the RNs between CHs and the sink. In the proposed framework, two phases exist, namely, first-phase RNs and second-phase RNs.