IoT has evolved into a central domain of research, transforming numerous other essential research areas by facilitating seamless interaction and the intelligent exchange of data among physical things (Alsuwat and Alsuwat, 2025; Sennan et al., 2021a; Sankar et al., 2020; Madireddy and Yarrabothu, 2025). IoT plays an indispensable role in the development of many fields, like smart homes, healthcare, agriculture, transportation, and smart cities, to broaden operational efficiency and user experience (Mohapatra, 2025). Underwater Wireless Sensor Networks (UWSNs), as a large-scale wireless network, consist of numerous distributed and autonomous small nodes that monitor pollution and environmental characteristics at the sink or base station for further processing and storage (Alshammri, 2025; Sennan et al., 2021b).

UWSNs are becoming popular in various fields. These include military, marine environments, disaster prevention, and offshore resource management (Gola and Gupta, 2020; Mamta et al., 2022). Sensor nodes can be deployed in 2D or 3D based on how deep the water is. Using the aquatic environment to deploy sensors in WSNS has always been a challenge (Sathish et al., 2022). Data transmission in UWSN is done via acoustic communication mode among nodes due to the fact that radio communication is limited in underwater environments. UWSNs are composed of sensors that monitor the physical environment and communicate their data to a central server for further processing (Srinivasan et al., 2019; Rajput and Kumaravelu, 2021). Usually, the data gathered from the environment is first converted into analog signals for pre-processing and then sent wirelessly using specialized transceiver modules which are a crucial part of a UWSN (Wang et al., 2019; Sheikh et al., 2012a; Sheikh et al., 2015). With the recent advancements in microelectronics, small-sized sensors with low power requirements have been developed, and the opportunities for real-time applications have become even more significant. UWSNs have been extensively used in battlefield surveillance, industrial automation, and ocean health monitoring (Asghari et al., 2019; Tran et al., 2019). In particular, in some cases multiple sensor nodes are necessary to fulfill the real-time process requirements. Furthermore, it is in the nature of UWSNs to be dynamic and therefore can change the network configuration in response to external factors such as node mobility, changes in the environmental context and the addition or removal of nodes, thus always ensuring the efficiency of the network.

When it comes to UWSN-based IoT, routing is the key component without which, communications of data among nodes is impossible. A lot of applications send the same data through the routing protocol, which results in a resource-constrained network. To fix this, a clustering method is used and it enhances the lifetime. Three principal steps are involved in this process. They are the cluster formation, selecting CH nodes, and selecting the best route for data transmission. Overall network performance will be massively improved by carrying out the steps mentioned above. Energy in the sensor nodes can be efficiently controlled, and network lifetime can be increased by simply forming clusters. Sensor nodes are resource-limited devices with very few resources (Storage, battery power, computation, and acoustic communication) (Hassija et al., 2019). It is extremely challenging to maintain a sustainable network when it relies entirely on sensor nodes in UWSN (Li et al., 2017). Various proposals have been made for energy-efficient technology in the network (Poluru and Naseera, 2017; Kharrufa et al., 2019). Sensor nodes are spread throughout the network region and formed into different clusters. A CH node is assigned to each cluster, which communicates with the sink node (Thangaramya et al., 2019).

Clustering is one of the conventional methods that have been extensively used in UWSNs to save energy by significantly reducing the volume of data transmission among sensor nodes (Sheikh et al., 2012b; Kotary and Nanda, 2020; Hase et al., 2017). Within this model, the group nodes are organized into different clusters. Each cluster is directed by a Cluster Head (CH), which aggregates data from all Cluster Members (CMs) and passes it to the sink node. Several studies have proposed various cluster head selection methods utilizing optimization algorithms, where an optimal solution is selected from the numerous possibilities. Furthermore, multiple cluster head selection algorithms have been proposed using a variety of optimization techniques in the recent past, such as Crested Porcupine Optimizer (CPO), GA, DE, Gannet Optimization Algorithm (GOA), ACO (Sumida et al., 1990; Storn and Price, 1997; Abdel-Basset et al., 2024; Pan et al., 2022; Landge et al., 2025), or Atomic Orbital Search (AOS) (Azizi, 2021). However, the standard optimization algorithms based on CH selection suffer from slow convergence, resulting in inefficient cluster formation and maximized communication overhead. Consequently, the overall energy balance in the network is affected. Hence, the Walrus Optimization Algorithm (WOA) is selected to determine the appropriate CHs within clusters, thereby extending the overall lifetime of the network. The WOA optimization algorithm seems generic; it can be employed in a network model by using an objective function formulation to observe energy consumption behavior and communication among nodes in a deployment scenario. The main contribution of this research is:

Initially, the sensor nodes are grouped into clusters based on their spatial proximity in a structured clustering framework, after which the energy-aware CH Node in the cluster is determined, which is responsible for RER and communication distance.

The remaining sections are detailed as follows: Section “Related work” discusses benchmark state-of-the-art works on cluster head selection in IoT. Section “System Preliminaries” presented an overview of the network model and an energy model. Section “Proposed Walrus Optimization Algorithm protocol for CH Selection” discusses the CH selection process. Section “Performance Evaluation and Analysis” presents the performance of the proposed approach, WOA, and comparison with existing works (ZFO-SHO, TIOCHR, and M-PSO). Finally, the summary of the proposed approach and future directions is discussed.

Energy remains a major restriction in UWSNs, although many ways for CH selection have been introduced. Lack of energy and CH selection challenges can be fixed if the right methods are used in UWSNs. Logically, a cluster is a set of nodes that are physically close to each other and hence the members of the set. One of the members of the group, the special node, which is the CH, will be in charge of collecting the data at the local level and sending it to the base station. Energy is saved and the communication load is decreased in this way.

Roberts et al. (2024) introduced a hybrid technique inspired by ZFO-SHO in the WSNs. In this class of network, essential services such as cluster head (CH) selection and energy conservation become quite challenging; however, a hybrid approach is then used to address these aspects. In this approach, the main interests are concentrated on dynamic cluster formation and path selection. The best node of CH selection is achieved with the improved ZO Algorithm (ZFO), which relies on the fitness function. Then, the energy consumption was reduced by utilizing an adaptive technique, namely SHO (Sea Horse Optimization). The distinction of the two-phase approach having a trade-off solution in both phases versus the previous methods is that a number of QoS parameters (RER, lifetime, PDR, and throughput) were utilized to check the ZFO-SHO effectiveness. OMNET++ 6.0, linked with MATLAB R2022b, was used to evaluate the ZFO-SHO using a simulation environment. Dynamic cluster formation is simulated and managed through the ZFO algorithm, whereas energy optimization is performed by the SHO in the MATLAB simulation framework. A node-based implementation of both algorithms (ZFO-SHO) was developed using OMNET++ to validate their operations in a dynamic environment. The evaluation of ZFO-SHO was computed by comparing it with the benchmark techniques NCOGA, FFARF, GAEO, MMABC, and MRP-GTCO. The effectiveness of ZFO-SHO is improved in terms of critical parameters, PDR by 1.8%–6.9%, throughput by 6.7%–24%, network lifetime by 1.86%–7.40%, and RER by 9.65%–37.95%. However, it takes more simulation time for selecting CH.

Babu and Geethanjali (2024) suggested a Trust Index Optimization driven CH selection method to extend the network lifetime in WSNs (TIOCHR). TIOCHR successfully identifies the best node in the network to communicate data to the sink node via the best transmitting path. Trust Index is used not only in the selection of a CH but also in finding the best path. The trust index is evaluated using three metrics: available energy, PDR, and packet consistency. Randomly, the CH node is selected in predetermined iterations. Afterward, the best path for sending packets in the network is chosen. Different performance metrics of TIOCHR are calculated, such as packet forwarding ratio (PFR), energy consumption (EC), routing overhead (RO), latency (L), and network longevity (NL). The TIOCHR method was executed in the NS3 simulation environment. TIOCHR’s performance is analysed against the selected techniques, energy-efficient algorithm, ANT algorithm, FCEEC algorithm, and TIOCHR algorithm through a comparative study. The energy consumption and network longevity of TIOCHR have been raised by 10%, 15%, and 20% when compared to the benchmark energy-efficient algorithm, the ANT algorithm, and the FCEEC algorithm, respectively. Nevertheless, it is necessary to have a larger number of iterations for selecting an optimal CH, which causes an increase in communication overhead.

Prakash et al. (2024) devised energy-efficient and CH selection in WSN through M-PSO and GA (DMPRP). In DMPRP, the CH node is chosen with M-PSO, and cluster members are picked through the GA algorithm. The DMPRP technique proposed in the paper first calculates the probability of a node in selecting the most optimal cluster head. The experiments for the DMPRP method were conducted using the NS2 simulation tool, and the obtained results were compared with the results of the state-of-the-art methods. Overall, the experimental results demonstrated that the performance of DMPRP was superior to the state of the art by 12%.

Mabunga and Dela Cruz (2025) proposed an energy-efficient routing protocol for the CH selection using the chronological wild geese optimization (CWGO) in WSNs. The method consists of three stages: CH selection, node energy prediction, and path finding. During the CH selection process, the deep recurrent neural network (DRNN) technique finds the best CH that satisfies the key limitations, such as the LLT, delay, energy, intercluster distance, and intracluster distance. Moreover, the best way from the node to the base station (BS) is obtained by the CWGO for data transmission which takes into account the multiple constraints of trust, delay, distance, and energy. The simulation of CGWO is carried out in a Python environment. The performance of CGWO is evaluated in comparison with conventional models EECHIGWO, DUCISCA, DE_SEP, and E_CERP. The effectiveness of CGWO is improved in terms of critical parameters, specifically distance (19.468 m), trust (0.252 s), energy (0.963 J), and delay (0.700). However, it takes more simulation rounds to choose the optimal CH.

Alshammri (2025) recommended improved CH selection using a squirrel search algorithm for enhancing network lifespan and efficiency (ISSA-C). The basic squirrel search optimization algorithm has been modified for the optimal CH selection. In ISSA-C, the solution quality is improved by adapting a series of adjustments and accelerating the convergence speed of CH selection. The capabilities of SSA, namely exploration and exploitation, are enhanced by implementing the Local search algorithm, dynamic step size control, and Adaptive population initialization. As a result, the optimal solution is achieved with more efficient convergence. The fitness function is evaluated with key parameters such as sink distance, CH balance, intra-cluster distance, and RER. The proposed ISSA-C is computed in different scenarios to assess its effectiveness in the network. In order to simulate the operations of WSNs, the authors implemented the MATLAB 2021a simulation environment. To attest to ISSA-C’s performance improvement, they employed GWO, CDO, SSA, SSO, MOCRAW, EEWC, and MAP-ACO methods as the benchmarks. The packet delivery ratio (PDR), energy consumption, cluster formation time, and CH selection time of ISSA-C are improved by 88%, 220 mJ, 82 s, and 67 s, respectively. Also, the ISSA-C technique is tested without the assistance of the above said methods. Still, it is intended for certain scenarios.

Alsuwat and Alsuwat (2025) proposed a hybrid Q-learning-based artificial bee colony optimization algorithm for CH selection and an optimum data transmission method in WSNs (IQ-ABC). This paper combined the strengths of the original ABC algorithm with a Q-learning mechanism for choosing the best CH in the network. IQ-ABC finds the energy-efficient path for data transmission from the sensor node (SN) to the base station. The selection of CH is enhanced by incorporating trust, energy-efficiency, and latency factors and balancing them via a multi-objective fuzzy logic function. The experiments of the IQ-ABC method were carried out in MATLAB 2021a as the simulation tool. The performance of the IQ-ABC framework was evaluated against the LEACH, HEED, PSO, GWO, ABC, and ACO. This method was exercised in three different scenarios by relocating the base station (BS). When the BS was located centrally, the energy consumption of IQ-ABC achieved 0.253 units over 1,200 rounds compared with other existing techniques, such as 0.38 units by LEACH, 0.361 units by HEED, and 0.6 units by ACO. Similarly, in other scenarios (edge and far), the proposed IQ-ABC approach outperformed with the lowest energy consumption of 0.30 and 0.33 units, respectively. Despite its effectiveness, it does not consider security threats for CH selection.

Amshavalli et al. (2025) outlined a multi-objective-based CH selection protocol using boosted sooty tern optimization Algorithm in WSNs (BSHPFMOCS). This approach aimed to address the formation of quality clusters and CHs. The potential CH selection makes the network stable and achieves low energy consumption in various scenarios. In BSHPFMOCS, the CH selection is achieved through an accurate search process, which helps extend the network lifetime in WSNs. The fitness value is computed to select the optimal CH that enhances cluster aggregation. The proposed BSHPFMOCS model integrated the advanced cluster formation methods by selecting cluster members that had the nearest intra-cluster distance. To address the issue of the hot spot in the network, the sink node mobility is optimized by the Piranhav Foraging Optimization Algorithm (PFOA). The effectiveness of the BSHPFMOCS method was compared to the most recent techniques such as FRHBMEB, MOCRAW, F2SORP, and GEIGOA. The simulation experiments were conducted using MATLAB version 2021a. The BSHPFMOCS performance has been raised by 21.94% energy consumption saving, 20.86% packet delay reduction, 18.42% throughput increase, and 10.76% network lifetime extension as compared to the benchmarked methods. However, it takes more simulation rounds to choose the optimal CH. However, the coverage efficiency of the proposed approach is limited. As a result, the energy is drained quickly.

Das and Dwivedi (2024) devised a scheme in which a CH is energy-aware and which detects a malicious node via a trust optimization algorithm in WSNs (LS-EATO). In LS-EATO the CH is selected by energy awareness through the harmonic search genetic algorithm (HSGA). Based on the trust value, the cluster member (CM) is not considered CH. Subsequently, with the use of the EAIICT (Energy-Aware Intra- and Inter-Cluster Trust) model, the node can be identified. The EAIICT estimation model is based mainly on two modules: direct trust (DT) and indirect trust (IT). The DT value is derived from the communication and data trust of the network. The IT value is derived from feedback trust. The CM is discovered based on trust value evaluation in central and distributed network scenarios. The residual energy (RER) is a key constraint considered when selecting potential CH with high energy in the EAIICT model. This article explores the capability of the newly introduced LS-EATO model besides a few essential parameters such as delay, detection rate of malicious nodes, and communication overhead. The clean LS-EATO method was run on the MATLAB simulation platform. The new approach, LS-EATO, was benchmarked against the existing baseline models, i.e., SQEER, EPO-TRM, FHTMS, and 2STM. Model performance evaluation results demonstrated that our model surpasses the existing ones.

Numerous optimization algorithms have been proposed for CH selection in WSNs. However, existing works demonstrated certain limitations, including communication overhead, poor PDR rate, and suboptimal CH selection. The key limitations of existing CH optimization techniques are listed in Table 1.

There are several metaheuristic algorithms have been proposed to solve CH section problems by focusing on the substitution of algorithms. However, they have shown less attention to the objectives of energy awareness and system constraints.

The proposed walrus optimization algorithm (WOA) focuses on optimal CH selection in wireless sensor network-based IoT using various network parameters. The proposed work seeks to achieve better network lifetime and throughput to manage energy consumption in the network. The CH selection is performed by utilizing a fitness function that is computed based on network factors such as RER and distance. The CH selection algorithm is executed in the sink node due to higher computing power than the sensor node. Then, the cluster is created by using Euclidian distance.

A simulation network space of 500 m × 500 m is considered, with 300 sensor nodes distributed randomly. The fixed Random number generator (RNG 2025) is designed to produce identical results.

Simulation rules:

All nodes maintain the same energy and abilities.

The position of the base station is fixed at (250 m, 250 m).

The Cluster Members (CM) position is fixed in the network area.

The proposed approach, WOA, creates a set of clusters with equal nodes and chooses the CH node. The CH node collects data to send to the BS for further processing.

The average result is calculated after 20 rounds in each simulation.

The experiment simulation is run up to 3,000 rounds, and one round includes data collection, CH selection, and sending data to the BS. The average result is considered over 30 runs to avoid randomness.

The proposed WOA algorithm is compared with baseline optimization algorithms (such as ZFO-SHO, TIOCHR, and M-PSO). All baseline algorithms were implemented under a similar simulation environment. We have considered the same topology, number of iterations, and population size are similar for every approach while running the simulation. The tuning bias is avoided by keeping all simulation parameters constant throughout execution.

The Underwater Wireless Sensor Network (UWSN) consists of low-power devices, thus posing challenges for optimization methods. Extending the lifespan of networks through energy optimization is crucial. Sensor nodes communicate via a clustering protocol. A plethora of optimization algorithms use CH selection to propose optimal solutions to the problem. Nevertheless, many of these optimization approaches are slow in the CH selection process due to an unsteady exploration/exploitation trade-off. To address this, we proposed the Walrus Optimization Algorithm (WOA) to select the best CH in the network. In CH selection, the objective function is determined by the two main factors: maximum Residual Energy (RER) and distance. The WOA CH selection process comprises two stages: cluster formation based on Euclidean distance and CH selection using a WOA-optimized method. The WOA strategy was realized in the MATLAB 2024a simulation environment. The WOA method was benchmarked against popular optimization methods, including ZFO-SHO, TIOCHR, and M-PSO. The WOA is an average improvement of 7%–10% in PDR, and the network lifetime is extended by 10%–15% compared to ZFO-SHO, TIOCHR, and M-PSO. Hence, the WOA has enhanced the total network efficiency.

The proposed WOA method has many advantages, but it is still open to improvements to be made. Future work can be extended to real-time implementation by taking into account factors, fuzzy rules, latency, and RSSI as the main parameters and deployment conditions will be explored further. The traditional algorithms LEACH and HEED will be considered in future extension work. Additionally, CH selection in a hybrid approach can be achieved by integrating other optimization techniques.