## 2 Literature Survey  
This chapter briefly overviews various works related to WSN, including WSN-based clustering, various optimization techniques, and hybrid optimization in WSN. Local optima, NP-hard problems, and so on can be solved by optimizing CHs using various meta-heuristic optimization algorithms. The need for hybrid optimization arises from improving performance, evolving quality algorithm solutions, and integrating algorithms as part of larger systems. [PERSON] et al. (2021) proposed a hybrid moth-flame-cuttlefish-optimization (MFO-CFO) technique for routing. Initially the clusters are formed by ant lion optimization (ALO) based on fuzzy-clustering means (FCM). A particle-swarm-optimization (PSO) algorithm based on an adaptive neuro-fuzzy inference system (ANFIS) is proposed for optimal CH selection. The results are compared to conventional methods such as the fuzzy, PSO and genetic algorithm (GA) [9]. [PERSON] et al. (2021) presented a CH-selection algorithm that chooses CHs based on the remaining energy, location, and centrality of the node. The simulation outcomes show that the suggested method outperforms existing methods such as LEACH, multi-hop LEACH networks based on reliability, network lifetime and energy consumption [10]. [PERSON] et al. (2020) revealed the possibility of combining the fuzzy-clustering approach and the elephant-herding-optimization (EHO)-Greedy algorithm for WSN. This method considers individual sink nodes for fixed and mobile sinks to reduce power consumption. Finally, the EHO-Greedy hybrid-algorithm routing protocol has been proposed for data transmission. The simulation outcomes demonstrate that the suggested technique outperforms other existing energy utilization methods and system lifetimes [11]. [PERSON] et al. (2020) revealed a routing method based on multi-objective energy-efficiency-criterion optimization and clustering. A multitier-deterministic routing protocol is employed to determine the optimal path for data transmission to the base station, thereby improving the WSN network's performance [12]. [PERSON] et al. (2021) proposed a multi-objective particle swarm optimization with the Levy-distribution (MOPSO-L)-based dynamic-clustering algorithm. The experimental outcomes show that the suggested model outperforms its competitors regarding network lifetime and energy efficiency [13]. [PERSON] et al. (2020) proposed a clustering method with self-diagnosis data fault detection and prediction for WSNs. The proposed approach nearly doubles the lifetime improvement compared with LEACH [14]. [PERSON] et al. (2021) proposed a bio-inspired cross-layer routing (BiHCLR) algorithm for efficient routing. A fuzzy logic method is used to choose a CH for each node. The routing paths are then chosen using a hybrid bionic algorithm. The hybrid algorithm combines salp-swarm-optimization and moth-search techniques. The proposed BiHCLR's result is assessed using a quality-of-service (QoS) analysis [15]. [PERSON] et al. (2021) propose an efficient CORP protocol for grouping and routing in WSNs. In this paper, the k-medoid algorithm with HHO was used for the election of CH, and the CORP algorithm was used for routing. The suggested work's results were compared to the existing FRLDG and HEED algorithms [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38]. Various communication and IoT routing protocols are also discussed in [39; 40; 41; 42; 43; 44; 45]. [PERSON] et al. (2015) revealed distributed fault-tolerant clustering and routing (DFCR). The algorithm is fault-tolerant and uses little energy. When CH fails unexpectedly, DFCR employs SN's distributed runtime recovery. SNS that do not have CH within their communication range are responsible for this. Various network scenarios are considered, and extensive experiments are carried out. The algorithm's robustness has been demonstrated in terms of various performance metrics after comparing empirical results with those of existing algorithms [17]. [PERSON] et al.