## 2 Literature Review  
In recent years, many researchers have developed various routing protocols to minimize energy consumption and to improve the network lifetime. Some of the articles related to various routing approaches in WSNs are reviewed below. [PERSON] et al. [16] designed Energy-Efficient Scheduling using the Deep Reinforcement Learning (DRL) (E\({}^{2}\)S-DRL) algorithm in WSNs. E\({}^{2}\)S-DRL contributes three phases to prolong network lifetime and to reduce network delay: clustering, duty-cycling, and routing phases. E\({}^{2}\)S-DRL's clustering phase reduces the energy consumption incurred during data aggregation. This was achieved through a Zone-based Clustering (Zbc) scheme. In the Zbc scheme, hybrid Particle Swarm Optimization (PSO) and Affinity Propagation (AP) algorithms were utilized. [PERSON] et al. [17] designed the LEACH-Fuzzy Clustering (LEACH-FC) protocol and implemented a fuzzy logic-based cluster head selection and cluster formation to maximize the network's lifetime. For the selection of cluster head and cluster formation, a centralized approach was used instead of distributed ones, and fuzzy logic was employed to select the vice cluster head. [PERSON] et al. [18] designed an efficient routing mechanism called power-aware energy-efficient routing (PAEER) to meet the network lifetime. The maximization and energy efficiency in WSNs and the different contributions of the PAEER approach are: (a) the multi-sink node approach which can lead to an increase in the node network lifetime and event detection mechanism that meets the reliability requirement of the WSNs; and (b) using the PAEER mechanism, the data are sent to the sink node by covering multipath routes to aggregate the node data. [PERSON] et al. [19] designed an energy-optimized algorithm. Owing to the heterogeneity of the sensor nodes in the WSNbased Internet of Things(IoT) for smart cities, one approach for scheduling the sensing activity is to cluster the sensors intoKmutually different subsets in such a way that every subset of sensors alone can cover all targets of the network. In this case, finding the maximum number of sensor subsets, or equivalently, the sensor covers the problem by converting it to the SET K-COVER problem. To solve the SET K-COVER problem, the proposed Grouping Memetic Algorithm (GMA) is proposed. [PERSON] et al. [20], aiming to reduce communication costs and resilience against different WSNs security attacks, used a Paillier Cryptosystem and Compressive Sensing-based Routing (PC\({}^{2}\)SR) to design three mechanisms: a pallier cryptosystem-based vital for distribution and management, intra-cluster data gathering, and secure data transmission. The PC\({}^{2}\)SR provided a pallier security key to each device for data authentication. [PERSON] et al. [21], aimed to develop a protocol architecture that can extend network lifetimes, balance, and reduce the energy consumption of networks, reduce redundancy, and increase information validity and integrity. [PERSON] et al. [22] discussed minimizing the overall energy consumption and maximizing the network lifetime. In this study, the Butterfly Optimization Algorithm (BOA) is employed to choose an optimal cluster head from a group of nodes. The performance measures of the proposed methodology are analyzed in terms of alive nodes, dead nodes, energy consumption, and data packets received by the BS. The disadvantage is the latency. [PERSON] et al. [23] attempted to develop a new clustering model with optimal cluster head selection by considering four major criteria: energy, delay, distance, and security. Furthermore, to select the optimal CHs, this paper proposes a new hybrid algorithm that hybridizes the concept of dragon fly and firefly algorithms, termed fire fly re-placed position update in dragonflies. [PERSON] et al. [24] proposed an improved energy-efficient clustering protocol (IEECP) to prolong the lifetime of a WSN-based IoT. [PERSON] et al. [25] proposed a clustering protocol named Energy Efficient Cluster-head Selection Scheme (ECSS).