Unknown Nodes Research Articles

Effectiveness of Data Augmentation for Localization in WSNs Using Deep Learning for the Internet of Things.

Wireless sensor networks (WSNs) have become widely popular and are extensively used for various sensor communication applications due to their flexibility and cost effectiveness, especially for applications where localization is a main challenge. Furthermore, the Dv-hop algorithm is a range-free localization algorithm commonly used in WSNs. Despite its simplicity and low hardware requirements, it does suffer from limitations in terms of localization accuracy. In this article, we develop an accurate Deep Learning (DL)-based range-free localization for WSN applications in the Internet of things (IoT). To improve the localization performance, we exploit a deep neural network (DNN) to correct the estimated distance between the unknown nodes (i.e., position-unaware) and the anchor nodes (i.e., position-aware) without burdening the IoT cost. DL needs large training data to yield accurate results, and the DNN is no stranger. The efficacy of machine learning, including DNNs, hinges on access to substantial training data for optimal performance. However, to address this challenge, we propose a solution through the implementation of a Data Augmentation Strategy (DAS). This strategy involves the strategic creation of multiple virtual anchors around the existing real anchors. Consequently, this process generates more training data and significantly increases data size. We prove that DAS can provide the DNNs with sufficient training data, and ultimately making it more feasible for WSNs and the IoT to fully benefit from low-cost DNN-aided localization. The simulation results indicate that the accuracy of the proposed (Dv-hop with DNN correction) surpasses that of Dv-hop.

Square grid Path Planning for Mobile Anchor-Based Localization in Wireless Sensor Networks

Localization is to provide all sensor nodes with their geographical positions. A mobile anchor-based localization in WSNs uses a mobile anchor equipped with GPS, which travels along a predetermined path. At each specified beacon point, it broadcasts its current known position to help other sensor nodes with unknown locations estimate their positions. In this paper, we analyze the determination of beacon points based on a square grid. We propose an improved path planning model named Union-curve. Our proposed model incorporates all beacon points of five previously developed paths, namely, SCAN, HILBERT, S-type, Z-curve, and $\Sigma$-Scan on the commonly used square grid decomposition of area. Unknown sensor nodes estimate their positions using two techniques, APT and WCWCL-RSSI. Simulation results show that the proposed model has higher accuracy, with a big difference in error rate compared to the other models. In addition, this model guarantees maximum coverage with less path resolution value.

Modeling of Dung Beetle Optimization-based Sink Node Localization Approach for Wireless Sensor Networks

Wireless sensor network (WSN) performs monitoring of each aspect of the area of interest by detecting the surrounding physical phenomena with sensor nodes and transferring the information to the gateway through the corresponding system. Several researcher workers have introduced localization methods to accomplish high accuracy of localization. An intelligent optimization technique has attracted various researcher workers due to its advantages such as strong optimization capability and few parameters to optimize the localization performance of the DV-Hop method. Sink node localization (NL) using metaheuristics in WSN includes applying optimization techniques inspired by human behavior or natural phenomena to define the geographical coordinates of the sink nodes within the network coverage region. WSNs can accomplish better localization performance, especially in dynamic or complex environments, improving the efficiency and reliability of network management and data transmission by leveraging metaheuristics. In this view, this manuscript develops a Dung Beetle Optimization based Sink Node Localization Approach (DBO-SNLA) for WSN. In the DBO-SNLA technique, the DBO algorithm involved is based on the social behavior of dung beetle populations and is developed with five updated rules to assist in finding high-quality solutions. In addition, the DBO-SNLA technique addresses the issues of defining the sink node location with lowest localization error once the data between the nodes is transferred wirelessly. Finally, the localization errors are calculated and the location of the different unknown nodes is computed. A detailed set of simulation takes place to examine the performance of the DBO-SNLA technique. The empirical analysis stated the betterment of the DBO-SNLA method than other techniques

Optimizing Sensor Localization and Cluster Performance in Wireless Sensor Networks through Internet of Thing (IoT) and Boosted Weight Centroid Algorithm

Localization is an extremely important component of applications that make use of wireless sensor networks. It has a substantial impact on academics as well as real-time sensor deployment applications in the aim of lowering the amount of energy that is used while simultaneously locating unknown nodes. The process of obtaining the coordinates along an axis that represent the locations of the sensor nodes is referred to as localization. The accuracy of locating the positions of the nodes varies depending on the environmental conditions, the type of nodes, the type of application, and the type of localization methods used. A standard localization method known as distance vector hop (DV-hop) localization will be able to determine the positions of unknown nodes with typical accuracy with the assistance of beacon nodes based on Internet of things. The DV-hop and improved weighted centroid localization algorithms, in addition to the suggested boosted weight centroid-based localization approach, are both addressed in this article. The suggested boosted weight centroid localization technique is utilized to find nodes in the remote area of the WSN while conserving energy. This is accomplished with the assistance of measurements involving both the nodes and the centroid. The modified weight metric is utilized in the process of carrying out the task of localisation of an unknown node. The performance of BWCLA is evaluated based on a number of different metrics, including accuracy in localization, average localization error, total packets utilized, and energy usage.

CALCULATION OF SHELLS BASED ON THE MIXED FEM VARIANT WITH TENSOR-VECTOR APPROXIMATION OF THE DESIRED VALUES

An algorithm for calculating thin shells based on the mixed finite element method (FEM) in a two-dimensional formulation is proposed. To correctly take into account possible displacements of the shell as a rigid body, the algorithm implements the developed tensor-vector form of the interpolation procedure of the desired values, which were chosen as the tensors of deformations and curvature at the point of the middle surface and the displacement vectors of this point. The discretization element was a quadrangular fragment of the middle surface with unknown nodes in the form of location and their first derivatives, as well as components of strain and curvature tensors among the inner surface at the nodes of the finite element. After minimizing the modified mixed functional, a 36?36 finite element stiffness matrix is formed. In order to verify the developed algorithm, a number of test problems were solved for calculating a fragment of an elliptical cylinder with an analytical solution, as well as for calculating a shell with spring supports that allow the ellipsoidal shell to move as an absolutely rigid body. Analysis of the obtained results showed that when calculating a fragment of an elliptical cylinder, the calculation scheme of which makes it possible to obtain an analytical solution, the numerical values of bending moments and normal stresses completely coincide with the values calculated from the condition of static equilibrium. An analysis of the calculation results for an ellipsoidal shell with spring supports led to the conclusion that the developed tensor-vector interpolation procedure in the mixed FEM version allows one to correctly take into account the displacements of shells as solids and obtain an adequate assessment of their stress-strain state.

Parallel Finite Element Analysis of 1D and 2D Problems of Heat Distribution in Mine Ventilation Galleries

The analyses of large-scale numerical problems of mining domain such as heat dissipation from rock strata to the ventilation air in large network of underground mine galleries are usually time taking process and require huge computational resources. To overcome this issue, High Performance Computing system (HPC) can be utilized to expedite the numerical analysis process for solving the problems within a reasonable time. In order to utilize HPC, this paper developed parallel finite element codes for solving heat conduction problems of mine galleries. The developed C++ codes of parallel FE are applied in solving 1D and 2D heat conduction problems. For the comparative analysis of 1D and 2D problems, the number of unknown nodes is fixed. It is observed that the computational time reduced when the number of processors increased. On the other hand, the computational time took longer time in 2D problem in comparison to that in 1D. Graphics User Interface (GUI) is also developed in terms of number of nodes of mesh, the boundary temperatures and length of the element. To sum up, the work of this paper reveals that the parallel computing is highly efficient in solving the large scale numerical geo-mining problems.

A Multi-Point Geostatistical Modeling Method Based on 2D Training Image Partition Simulation

In this paper, a multi-point geostatistical (MPS) method based on variational function partition simulation is proposed to solve the key problem of MPS 3D modeling using 2D training images. The new method uses the FILTERSIM algorithm framework, and the variational function is used to construct simulation partitions and training image sequences, and only a small number of training images close to the unknown nodes are used in the partition simulation to participate in the MPS simulation. To enhance the reliability, a new covariance filter is also designed to capture the diverse features of the training patterns and allow the filter to downsize the training patterns from any direction; in addition, an information entropy method is used to reconstruct the whole 3D space by selecting the global optimal solution from several locally similar training patterns. The stability and applicability of the new method in complex geological modeling are demonstrated by analyzing the parameter sensitivity and algorithm performance. A geological model of a uranium deposit is simulated to test the pumping of five reserved drill holes, and the results show that the accuracy of the simulation results of the new method is improved by 11.36% compared with the traditional MPS method.

Improved DV-Hop model based on the application of variable scenarios

This work proposes an improved DV-Hop model based on function analysis and simulation parameter determination (named FuncDV-Hop) to address the problems of low positioning accuracy and strong scene dependence of the DV-Hop localization model. The undetermined coefficient optimization, step function segmentation experiment, weight function strategy with equivalent points, and maximum likelihood estimation correction are introduced to reduce the positioning error by analyzing the error reasons in the average hop distance, distance estimation, and least square calculation of the DV-Hop model. The experiment is designed using the control variable method. The total number of nodes, the ratio of beacon nodes, the communication radius, the number of beacon nodes, and the number of unknown nodes are designed as variables to control the experiment. Finally, the experiments of two stages of simulation parameter determination and integration optimization are carried out. The simulation positioning error of all scenarios is reduced by 0.0630–0.2472, and the positioning accuracy is increased by 23.81%–75.85%. Experimental results show that the FuncDV-Hop model has the highest optimization rate of more than 50% in all experimental scenarios compared with the other models, the localization error is reduced by more than 0.1, and the optimization rate is increased by more than 10% in the record parameters of the existing wireless sensor network system.

Joint tensor completion anchor selection and coordinate transformation for high precision cooperative positioning in device-to-device systems

The synergistic amalgamation of device-to-device (D2D) communication between mobile terminals (MT) and ultra-dense networks in 5G systems enables cooperative positioning. In this paper, we propose a joint tensor completion, anchor selection, and coordinate transformation (JTCASCT) for high precision cooperative positioning in D2D systems. The positioning is achieved by mitigating the challenging issue of recovering the missing values from the corrupted distance between nodes and determining the global positions of unknown stationary nodes. Initially, a hankelized distance tensor structure called Euclidean distance tensor (EDT) is constructed from incomplete EDM. The missing values are recovered as tensor completion by exploiting its latent low rank structure. The optimization problem is solved using simple low rank tensor completion (SiLRTC) and high accuracy low rank tensor completion (HaLRTC) algorithms. The relative position of nodes is determined after applying multidimensionality scaling (MDS). To reduce the data dimension for coordinate transformation and to select the optimal combination of anchor nodes, we propose a low complex Kruskal–Wallis (KW) test. Finally, the global position of unknown nodes is computed using Helmert transformation (HT). Numerical evaluations demonstrate that the cooperative positioning accuracy is significantly improved with the proposed JTCASCT approach compared to the state-of-the-art techniques.

An enhanced node segmentation and distance estimation scheme with a reduced search space boundary and improved PSO for obstacle-aware wireless sensor network localization

This paper proposes an enhanced method for localizing sensor nodes in wireless sensor networks with obstacles. Such environment settings lead to lower localization accuracy because locations are estimated based on detour distances circumventing the obstacles; we, therefore, improve the segmentation technique to address the issue as they divide the whole area into multiple smaller ones, each containing fewer or no obstacles. Nevertheless, when radio transmissions between sensor nodes are obstructed (as simulated by the radio irregularity model), the signal-strength variation tends to be high, reducing localization accuracy; thus, we provide a method for accurately approximating the distances between pairs of an anchor node (whose location is known) and an unknown node by incorporating the related error into the approximation process. Additionally, when the nodes with unknown locations are outside the polygon formed by the anchor nodes, the search area for localization is relatively large, resulting in lower accuracy and a longer search time; we then propose a method for reducing the size of approximation areas by forming boundaries based on the two intersection points between the ranges of two anchor nodes used to localize an unknown node. However, these reduced search areas could still be large; we further increase the accuracy of the PSO location estimation method by adaptively adjusting the number of particles. In addition, with PSO, the accuracy of unknown node location estimation depends on having a properly selected fitness function; therefore, we incorporate appropriate variables to reflect the distance approximation accuracy between each anchor-unknown node pair. In experiments, we measure performance in sensor node deployment areas of three different shapes: C-shaped, with 1 hole, and with 2 rectangular holes. The results show that our method provides higher localization accuracy than others in small-, medium-, and large-scaled WSNs. Specifically, our proposed method is 27.46%, 49.28%, 50.33%, and 74.62% more accurate on average than IDE-NSL, PSO–C, min-max PSO, and niching PSO, respectively.

Chaotic Mapping Lion Optimization Algorithm-Based Node Localization Approach for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) contain several small, autonomous sensor nodes (SNs) able to process, transfer, and wirelessly sense data. These networks find applications in various domains like environmental monitoring, industrial automation, healthcare, and surveillance. Node Localization (NL) is a major problem in WSNs, aiming to define the geographical positions of sensors correctly. Accurate localization is essential for distinct WSN applications comprising target tracking, environmental monitoring, and data routing. Therefore, this paper develops a Chaotic Mapping Lion Optimization Algorithm-based Node Localization Approach (CMLOA-NLA) for WSNs. The purpose of the CMLOA-NLA algorithm is to define the localization of unknown nodes based on the anchor nodes (ANs) as a reference point. In addition, the CMLOA is mainly derived from the combination of the tent chaotic mapping concept into the standard LOA, which tends to improve the convergence speed and precision of NL. With extensive simulations and comparison results with recent localization approaches, the effectual performance of the CMLOA-NLA technique is illustrated. The experimental outcomes demonstrate considerable improvement in terms of accuracy as well as efficiency. Furthermore, the CMLOA-NLA technique was demonstrated to be highly robust against localization error and transmission range with a minimum average localization error of 2.09%.

Deterministic treasure hunt and rendezvous in arbitrary connected graphs

Treasure hunt and rendezvous are fundamental tasks performed by mobile agents in graphs. In treasure hunt, an agent has to find an inert target (called treasure) situated at an unknown node of the graph. In rendezvous, two agents, initially located at distinct nodes of the graph, traverse its edges in synchronous rounds and have to meet at some node. We assume that the graph is connected (otherwise none of these tasks is feasible) and consider deterministic treasure hunt and rendezvous algorithms. The time of a treasure hunt algorithm is the worst-case number of edge traversals performed by the agent until the treasure is found. The time of a rendezvous algorithm is the worst-case number of rounds since the wakeup of the earlier agent until the meeting.To the best of our knowledge, all known treasure hunt and rendezvous algorithms rely on the assumption that degrees of all nodes are finite, even when the graph itself may be infinite. In the present paper we remove this assumption for the first time, and consider both above tasks in arbitrary connected graphs whose nodes can have either finite or countably infinite degrees. Our main result is the first universal treasure hunt algorithm working for arbitrary connected graphs. We prove that the time of this algorithm has optimal order of magnitude among all possible treasure hunt algorithms working for arbitrary connected graphs. As a consequence of this result we obtain the first universal rendezvous algorithm working for arbitrary connected graphs. The time of this algorithm is polynomial in a lower bound holding in many graphs, in particular in the tree all of whose degrees are infinite.

MOANS DV-Hop: An anchor node subset based localization algorithm for wireless sensor networks

Several applications have exploited wireless sensor networks (WSNs), and localization is a key WSN technology. The range-free localization technique is substantially less expensive than conventional range-based localization strategies since it does not require distance or angle measurements between the anchor and unknown nodes. The Distance Vector-Hop (DV-Hop) technique is a widespread localization solution for WSNs because of its straightforward theory and minimal cost. The coordinate estimating precision of the classic DV-Hop algorithm needs further improvement due to its large localization error. In this paper, a DV-Hop-based method utilizing modified optimum anchor node subset (MOANS DV-Hop) is proposed to improve the localization performance of the DV-Hop algorithm. A strategy for anchor node deployment is proposed. An objective function is formulated to minimize the error in estimating coordinates of unknown nodes. With the MOANS DV-Hop algorithm, each anchor node first uses other anchor nodes to locate itself, then uses the Scaled General Learning Equilibrium Optimizer (SGLEO) algorithm to create an optimal subset made up of anchor nodes other than itself. The anchor node then updates its average hop size using the anchor node subset and broadcasts both the updated hop size and the anchor node subset to the nearby unknown nodes. An unknown node then determines its location using the Equilibrium Optimizer (EO) algorithm, the anchor node subset, and the updated hop size received from the closest anchor node. Simulation findings show that MOANS DV-Hop algorithm has a greater level of localization accuracy in coordinate estimation than both the classical and other improved DV-Hop methods.

A Hybrid Localization Algorithm for an Adaptive Strategy-Based Distance Vector-Hop and Improved Sparrow Search for Wireless Sensor Networks.

Wireless sensor networks (WSNs) are applied in many fields, among which node localization is one of the most important parts. The Distance Vector-Hop (DV-Hop) algorithm is the most widely used range-free localization algorithm, but its localization accuracy is not high enough. In this paper, to solve this problem, a hybrid localization algorithm for an adaptive strategy-based distance vector-hop and improved sparrow search is proposed (HADSS). First, an adaptive hop count strategy is designed to refine the hop count between all sensor nodes, using a hop count correction factor for secondary correction. Compared with the simple method of using multiple communication radii, this mechanism can refine the hop counts between nodes and reduce the error, as well as the communication overhead. Second, the average hop distance of the anchor nodes is calculated using the mean square error criterion. Then, the average hop distance obtained from the unknown nodes is corrected according to a combination of the anchor node trust degree and the weighting method. Compared with the single weighting method, both the global information about the network and the local information about each anchor node are taken into account, which reduces the average hop distance errors. Simulation experiments are conducted to verify the localization performance of the proposed HADSS algorithm by considering the normalized localization error. The simulation results show that the accuracy of the proposed HADSS algorithm is much higher than that of five existing methods.

Secure localization techniques in wireless sensor networks against routing attacks based on hybrid machine learning models

The identification and localization of malicious nodes in wireless sensor networks (WSNs) is a hot area of research that can considerably extend the network's lifetime and make it more valuable. We use sensors whose positions are known, or anchor nodes, to make educated guesses about the positions of the unknown nodes. Several localization methods have been developed for precise estimation of the unknowable nodes. So, during the network setup process, finding suitable network parameters for node localization with the requisite accuracy in a short amount of time remains a tough task. Due to the fact that they manipulate network resources and routing protocols, routing assaults like wormhole attacks, Sybil attacks, blackhole attacks, and replay attacks are just a few examples of the types of attacks that have the potential to hinder the accuracy of localization and the quality of service provided by WSNs. This work proposes safe localization and detection of routing threats in wireless sensor networks by utilizing hybrid optimized machine learning approaches for optimal distance, position, and data communication. These approaches aim to find the optimal distance between sensors and the optimal position of sensors. Calculating the average localization accuracy and finding malicious nodes both need the use of the benchmark datasets CICIDS2017 and UNSW NB15. The machine learning algorithms that have been provided can be utilized with these datasets. The cluster labelling K-means clustering technique is applied to binary classification in the system that has been proposed. As a consequence, the system achieves an average detection accuracy of 100%. The findings of the simulation indicate that the proposed hybrid strategy is capable of achieving a higher level of localization accuracy of the unknown nodes, with an average localization error of 0.191.

New Online DV-Hop Algorithm via Mobile Anchor for Wireless Sensor Network Localization

In many applications of Wireless Sensor Networks (WSNs), event detection is the main purpose of users. Moreover, determining where and when that event occurs is crucial; thus, the positions of nodes must be identified. Subsequently, in a range-free case, the Distance Vector-Hop (DV-Hop) heuristic is the commonly used localization algorithm because of its simplicity and low cost. The DV-Hop algorithm consists of a set of reference nodes, namely, anchors, to periodically broadcast their current positions and assist nearby unknown nodes during localization. Another potential solution includes the use of only one mobile anchor instead of these sets of anchors. This solution presents a new challenge in the localization of rang-free WSNs because of its favorable results and reduced cost. In this paper, we propose an analytical probabilistic model for multi-hop distance estimation between mobile anchor nodes and unknown nodes. We derive a non-linear analytic function that provides the relation between the hop counts and distance estimation. Moreover, based on the recursive least square algorithm, we present a new formulation of the original DV-Hop localization algorithm, namely, online DV-Hop localization, in WSNs. Finally, different scenarios of path planning and simulation results are conducted.

A DV-Hop optimization localization algorithm based on topological structure similarity in three-dimensional wireless sensor networks

Localization technology performs an essential part in wireless sensor networks (WSNs). The Distance vector hop (DV-Hop) localization algorithm has been frequently used in the WSNs with its simplicity, feasibility and minimal hardware requirements. However, the low localization accuracy of the DV-Hop algorithm has limited its further application in WSNs. To overcome the accuracy problem of the DV-Hop algorithm, we propose an improved DV-Hop algorithm based on topological structure similarity, called TDSDV-Hop. Different from the traditional DV-Hop algorithm, the topological structure similarity is applied to correct the hop count, reducing the inaccuracy caused by the minimum hop count. The hop counts will be converted from discrete to precise continuous values by the topological structure similarity. In this paper, firstly, the topological structure similarity is defined, followed by the proof of the theory that the distance between two nodes is inversely proportional to the topological structure similarity. Secondly, a hop-correction equation that can correct the hop count between neighbor nodes is set up. Then the TDSDV-Hop algorithm incorporates hop-correction formula and simulates the optimal hop-correction parameters in order to acquire more accurate values of the hop count. And the coordinates of unknown nodes are calculated by the least square method. Finally, the levy adaptive improved bird swarm algorithm (LSABSA) is utilized to optimize coordinates of unknown nodes by constructing a new objective function. The simulation results show that the localization performance of the TDSDV-Hop algorithm is better than that of DV-Hop, GWODV-Hop, CRWDV-Hop and CVLR algorithm in terms of localization accuracy, localization time and communication cost.

Investigation of the heat transfer coefficient for a red clay brick

AbstractThere is a need for the development of materials for thermal isolation. Heat energy is used in different ways, for example, for house warming or production of electricity from solar power plants. However, to use the heat energy efficiently isolating materials are needed. There are different materials available to isolate houses and heat storage containers. However, those materials are expensive and some are over engineered. The best available material for thermal isolation would be air that has the thermal coefficient . Thus, materials that trap a large amount of air are good isolators. On the other hand, some isolating materials are harmful for the environment, for example, glass wool, asbestos. An alternative are red clay bricks because red clay is a natural product that is present everywhere on the planet. But the thermal coefficient of red clay bricks is high . To reduce the thermal coefficient cavities can be added in the brick. The cavities trap air and reduce the thermal coefficient of the brick.We simulate the heat transfer through a hollow brick. The holes in the brick are cavities. In order to determine the heat transfer coefficient we solve the heat flux through the hollow brick. For that a model of the brick is formulated as sequence of a solid and a fluid part. In the solid part only heat conduction is present and in the fluid heat transfer is present in form of conduction, convection and radiation. The flow field in the fluid part is modelled with the velocity‐vorticity formulation of the incompressible Navier‐Stokes. It is assumed that the Rayleigh number is below 106, hence, natural convective laminar fluid flow is present in the cavities. The cavities are also heated by heat radiation of the surrounding walls.The numerical realisation is done with the Boundary‐Domain Integral Method (BDIM). To handle suitable geometries the complexity of the BDIM is reduced from quadratic to logarithmic or almost linear by applying the ‐methodology. M is the number of unknown domain nodes. Numerical studies will be presented.

Enhanced DV-Hop using shuffled shepherd algorithm for localizing sensor nodes in 3-D space

For many wireless sensor networks applications, a powerful localization algorithm is required to determine the exact positions of sensor nodes. In this paper, a new localization algorithm is presented which combines the distance vector hop (DV-Hop) algorithm with the shuffled shepherd optimization algorithm to determine the position for each unknown node within 3-D space. Studying the localization problem in 3-D space is more realistic, but more challenging due to the enlarged search space. Comparison of our proposed algorithm with its alternatives in literature shows that it offers improved accuracy and more stable performance with respect to changes in nodes density and communication range.

Doppler shift with Archimedes Optimization Algorithm for localizing unknown nodes in underwater sensor networks

SummaryThe issue of underwater sensor network (UWSN) localization has led to the aim of techniques presented in recent years. In this paper, we develop Doppler shift with Archimedes Optimization Algorithm for localizing unknown nodes in UWSN. The projected method predicts that sink node plays a major function in managing the computational load contrasted with the remaining nodes in the network of underwater. This node localization is proceeding with frequency shifts of sound waves contrasted toward real, which are present once observer in addition source can be mobile as they do in a marine atmosphere. The proposed technique is utilized to compute the estimated position of an unknown sensor node; here Archimedes' optimization algorithm is utilized to reduce the error during localization of nodes in UWSNs. This proposed technique can be enhancing the accuracy of the localization of nodes in UWSNs. This proposed methodology can be implemented and evaluated with the help of performance metrics. To validate the proposed technique's efficiency, it is contrasted with conventional techniques like Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA).