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Top1. Introduction
WSN has been paid great attention in several areas, like military and natural disaster detection, environmental monitoring, structural health monitoring, because of its flexible communication, low cost, and low power consumption (Khan, et al., 2018). WSN contains several thousands of distributed mobile Sensor Nodes (SN) and every node with similar computing, communication, and sensing capabilities. Thus, such sensor networks have their own characteristics (Cai, et al., 2015; Jia, et al., 2015). WSNs were containing huge SNs that are deployed randomly for sensing and monitoring physical and environmental conditions. In addition, the WSNs become a reality due to the development in the Micro-Electro-Mechanical Systems (MEMS) that results in huge SN size with their communication components. Moreover, the components utilized in the WSN are as follows. a) SNs are employed for accumulating the data from the desired geographical area, b) the interconnection network is utilized for transmitting the SN data to sink or gateway, c) the central data gathering approach, termed sink, and the computing resources at the user end for the further processing, storage, and the analysis (Mishra and Kumar, 2014; Khan, et al., 2018). Furthermore, the WSN having limited power and capacity for processing. In other applications, the energy is refilled by the external source, like solar cells; however, it shows the non-continuous supply to provide smooth functioning. However, the network topology and energy consumption are the key addressing problems in the WSN for the network's better system performance in several applications (Radi, et al., 2014; Mehra, et al., 2020).
The important parameters that are considered for designing WSN are less battery life and the duration, configuration without the infrastructure and topologies of a dynamic network, Quality Of Service (QoS), reliability of the wireless connections, node mobility, variable nodes functions, multi-hop routing scalability, multicast support as well as security threats (Jha and Ghosh, 2018). Hence, a routing protocol is very important in the WSN in order to modify the transmission of the packets effectively on wireless support, when destination and source are the non-adjacent nodes. Therefore, the routing protocol chooses the optimal path among source-destination node pairs based on QoS and power consumption measurements, like average end-to-end delay, packet loss, available bandwidth, and the noise average (Ganesan, et al., 2001; Jha and Ghosh, 2018). In addition, WSN routing may be reactive, hybrid, and proactive. Hence, the proactive protocols create the routing table for transferring the information. In general, reactive protocols are used for inter-cluster routing, whereas proactive protocols are employed for intra-cluster communication (Mehra, et al., 2020) . In addition, the routing algorithms are categorized into single-path and multi-path routing algorithms (Kamarei, et al., 2020). Nowadays, multi-path routing is considered for maintaining the data transmission with high-quality (Fu, et al., 2020).
The swarm-based hierarchical methods were mainly utilized for enhancing the energy efficiency of sensor nodes. The binary particle swarm optimization method was developed along with the adapted connecting set, which used the residual energy to discover the optimal amount of cluster head and clusters [25] (Shankar, et al., 2016). Furthermore, the modified genetic approach was introduced for load balancing, and it decreases the energy consumption in WSN [26] (Shankar, et al., 2016).In (Bahrami, et al., 2018), the multi-path routing model is introduced using the residual energy of the network. This approach uses several hops in every node for identifying the optimal path routing table. This approach uses theAnt Colony Optimization (ACO) for finding the best path in a secure way. In (Sabet and Naji, 2015) the Energy Efficient Path Routing (EEPR)protocol is developed for mitigating the variance of residual energy of nodes and improves the duration of the WSN network. However, this protocol chooses the path using the min-max formulation for identifying residual energy path. The energy based fuzzy clustering protocol is known as fuzzy-based enhanced Cluster Head selection (FBECS) for increasing the network span. In addition, the probabilistic method was employed for selecting the CH for better routing (Fu, et al., 2020) .