###### Abstract  
As sensors are distributed among wireless sensor networks (WSNs), ensuring that the batteries and processing power last for a long time, to improve energy consumption and extend the lifetime of the WSN, is a significant challenge in the design of network clustering techniques. The sensor nodes are divided in these techniques into clusters with different cluster heads (CHs). Recently, certain considerations such as less energy consumption and high reliability have become necessary for selecting the optimal CH nodes in clustering-based metaheuristic techniques. This paper introduces a novel enhancement algorithm using Aquila Optimizer (AO), which enhances the energy balancing in clusters across sensor nodes during network communications to extend the network lifetime and reduce power consumption. Lifetime and energy-efficiency clustering algorithms, namely the low-energy adaptive clustering hierarchy (LEACH) protocol as a traditional protocol, genetic algorithm (GA), Coyote Optimization Algorithm (COY), Aquila Optimizer (AO), and Harris Hawks Optimization (HHO), are evaluated in a wireless sensor network. The paper concludes that the proposed AO algorithm outperforms other algorithms in terms of alive nodes analysis and energy consumption. Keywords:wireless sensor networks; optimization algorithms; aquila optimizer; alive nodes; energy consumption +
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among others, to minimize human effort and increase the quality of experiences (QoE). Such is the set of areas in which applications have been established to enhance life quality in our countries. Figure 1 represents a whole classification of the IoT in the major fields of application. IoT theory may be summarised as creating everyday data from one entity and sending it to another. As a result of facilitating communication between things, IoT applications are broad, diverse, and unconstrained [3]. In an IoT network, data transmission is allowed by different protocols. The applied protocol's job is to manage a device's behaviour during data transfer. Designing an IoT network with several sensors is extremely difficult; these nodes are highly dependent on a steady energy supply, channel throughput capacity, and storage characteristics, necessitating smart resource management. It is critical to introduce a data sink to the network based on WSNs. The procedure begins by saving the data obtained in the sink before moving on to the other nodes and repeating the process. As the sensor and sink placement may enhance the IoT network capacity, the suitable choice of data transmission mechanism has a significant impact. The WSN is made up of sensor nodes that are linked together via wireless connections. After collecting data in the environment, the data is transferred to gateway devices, which then transport the data to the cloud over the Internet. Communication between nodes in a WSN can be either direct or multi-hop via its associated network and designated cluster head. Sensor nodes are limited in power and computing resources, but gateway nodes have enough resources. Many academics have been striving to validate the applicability of IoT devices for energy savings. When compared to the energy consumption of the devices they control, most IoT sensors need extremely little power [5]. Figure 1: Taxonomy of IoT Applications [4]. An energy-efficient cooperative acoustic communication protocol has been presented in [6], to improve network lifetime and reduce energy consumption. The algorithm selects the destination nodes based on the signal-to-noise ratio (SNR) and distance among the communicating nodes. A new scheme \"A Content-based Adaptive and Dynamic Scheduling (CADS)\", with a two-way communication model in WSNs, is proposed in [7], to increase lifetime and offer an energy-efficient WSN. This scheme changes a node's states dynamically via data aggregation. Each node adapts a new state based on the transmitted packets.