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Wireless Sensor Network (WSN) is an immense collection of low-power, intelligent and multifunctional sensor nodes for sensing and monitoring the environmental conditions. The information collected from sensors are transmitted to the sink or Base Station (BS). The sensors in the WSN use the battery energy and the energy consumption of the nodes are considered as an important constraint in the network. In order to overcome the issue related to the energy consumption, a cluster based routing protocol is developed in the WSN. In this paper, an appropriate Cluster Head (CH) selection and route generation are obtained using the Energy Centric Multi Objective Salp Swarm Algorithm (ECMOSSA). The main objective of using ECMOSSA is to improve the network lifetime of the WSN by minimizing the node's energy consumption. The performance of the proposed ECMOSSA method is evaluated by means of alive nodes, total energy consumption, total packets received by the BS, throughput and network lifetime. Moreover, the ECMOSSA method is evaluated with one classical approach namely Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol as well as this ECMOSSA is compared with Grey Wolf Optimizer (GWO)-Dual Hop Routing (DHR) method and Cat-Salp Swarm Algorithm (C-SSA) to evaluate the efficiency of ECMOSSA. The last node dies (i.e., network lifetime) of the ECMOSSA is 1704 that is high when compared to both the LEACH and GWO-DHR method.

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In recent years, Wireless Sensor Networks (WSNs) have received increased international attention due to advancements in the communication, electronics, and information fields. These sensor networks integrate a huge number of sensor nodes to track rapidly changing physical events. The key improvement is that these nodes can be easily positioned in any environment due to their small size. Therefore, maintaining network connectivity is crucial in WSNs. If some nodes become unavailable, the connectivity of the routing path fails, resulting in significant packet loss in the WSN. Therefore, many types of research in WSNs have focused on energy efficiency, where energy consumption is minimized to improve the network lifetime. Here, Energy and Distance-aware Multi-Objective Firebug Swarm Optimization (ED-MOFSO) is projected to achieve an energy efficient process. Furthermore, ED-MOFSO minimizes delays to enhance performance measures. From the overall simulation, it shows that ED-MOFSO achieves improved metrics, including residual energy (14.29 J), delay (11.1 ms), packet delivery ratio (0.994), routing overhead (0.10), and throughput (1.233 Mbps) when compared to conventional Elephant Herding Optimization (EHO) greedy and Ant Colony Optimization Integrated Glow-worm Swarm Optimization (ACI-GSO).

In recent years, WSNs have attracted significant attention due to the improvements in various sectors such as communication, electronics, and information technologies. When the clustering algorithm incorporates both Euclidean distance and energy, it automatically decreases the energy consumption. So, the major goal of this research is to reduce energy consumption for prolong the lifetime of the network. In order to achieve an energy-efficient process, Energy and Distance Aware Multi-Objective Golden Eagle Optimization (ED-MOGEO) is proposed in this research. In addition, this proposed solution reduces retransmissions and delays to improve the performance metrics. And so, this research carried out two major fitness functions (Euclidean distance and energy) for creating an energy-efficient WSN. Furthermore, energy consideration is used to reduce the nodes unavailability which results in packet loss during the transmission. For generating the routing path between the source and the Base Station (BS), the ED-MOGEO algorithm is used. From the simulation results, it shows that Proposed ED-MOGEO achieves better performances in terms of residual energy (14.36 J), end-to-end delay (12.9 ms), packet delivery ratio (0.994), normalized routing overhead (0.11), and throughput (1.131 Mbps) when compared to existing Cluster-Based Data Aggregation (CBDA) and Elephant Herding Optimization (EHO)-Greedy methods.