Limited Sensor Research Articles

Feature-Enhanced Beamforming for Underwater 3-D Acoustic Imaging

A planar array of sensors and beamforming algorithms are required for underwater 3-D acoustic imaging system. However, due to limited aperture and sensor numbers, conventional beamforming usually results in wide mainlobes and high sidelobes, which severely degrade the image quality. To date, using unconventional beamforming algorithms to improve the imaging quality of underwater 3-D acoustic systems have been rarely investigated. This article proposes a feature-enhanced beamforming (FEBF) algorithm, with the goal of improving the imaging quality by utilizing more appropriate prior knowledge that objects in the angular domain in underwater 3-D imaging scenes are generally sparse but locally dense. The proposed FEBF employs the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{1}$</tex-math></inline-formula> -total variation (L1-TV) mixed norm regularization. The penalized beamforming optimization is reformed into convex programming with linear constraints and solved by alternating direction method of multipliers. The results of simulation and experiment demonstrate that the proposed FEBF has the ability to effectively improve the image quality when compared with the other existing beamforming methods. This study also proposes a 2-D fast Fourier transform based acceleration strategy, which effectively reduces the computational time of the FEBF algorithm by about 4.3 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> times for forming 200 × 200 beams. The accelerated version of the proposed FEBF method is considered to have potential for real-time imaging systems.

Sparse sensor reconstruction of vortex-impinged airfoil wake with machine learning

Reconstruction of unsteady vortical flow fields from limited sensor measurements is challenging. We develop machine learning methods to reconstruct flow features from sparse sensor measurements during transient vortex–airfoil wake interaction using only a limited amount of training data. The present machine learning models accurately reconstruct the aerodynamic force coefficients, pressure distributions over airfoil surface, and two-dimensional vorticity field for a variety of untrained cases. Multi-layer perceptron is used for estimating aerodynamic forces and pressure profiles over the surface, establishing a nonlinear model between the pressure sensor measurements and the output variables. A combination of multi-layer perceptron with convolutional neural network is utilized to reconstruct the vortical wake. Furthermore, the use of transfer learning and long short-term memory algorithm combined in the training models greatly improves the reconstruction of transient wakes by embedding the dynamics. The present machine-learning methods are able to estimate the transient flow features while exhibiting robustness against noisy sensor measurements. Finally, appropriate sensor locations over different time periods are assessed for accurately estimating the wakes. The present study offers insights into the dynamics of vortex–airfoil interaction and the development of data-driven flow estimation.Graphic abstract

Reconstruction of structural long-term acceleration response based on BiLSTM networks

Reconstructing lost dynamic responses is significant for structural condition assessment in structural health monitoring (SHM). Current advanced methods usually employ deep learning models based on convolutional neural networks (CNN) to reconstruct the acceleration response of structures. However, such methods cannot fully mine the temporal correlations among the monitoring data, leading to a relatively low accuracy. Therefore, this study proposes a structural acceleration response reconstruction method based on bidirectional long short-term memory (BiLSTM) networks, which can better mine complex spatial–temporal correlations among the sensor data. A numerical study of a long-span cable-stayed bridge and a practical monitoring data study on the Z24 bridge were conducted to verify the accuracy and efficacy of the proposed reconstruction method. The results show that structural long-term successive acceleration data can be reconstructed with high accuracy using the proposed method despite limited sensors due to the fact that the spatial–temporal correlations of acceleration data can be well extracted. The proposed method outperforms current advanced machine learning-based methods and exhibits exciting application prospects in practical structural acceleration data recovery and damage identification.

Path Planning and Motion Control of Indoor Mobile Robot under Exploration-Based SLAM (e-SLAM)

Indoor mobile robot (IMR) motion control for e-SLAM techniques with limited sensors, i.e., only LiDAR, is proposed in this research. The path was initially generated from simple floor plans constructed by the IMR exploration. The path planning starts from the vertices which can be traveled through, proceeds to the velocity planning on both cornering and linear motion, and reaches the interpolated discrete points joining the vertices. The IMR recognizes its location and environment gradually from the LiDAR data. The study imposes the upper rings of the LiDAR image to perform localization while the lower rings are for obstacle detection. The IMR must travel through a series of featured vertices and perform the path planning further generating an integrated LiDAR image. A considerable challenge is that the LiDAR data are the only source to be compared with the path planned according to the floor map. Certain changes still need to be adapted into, for example, the distance precision with relevance to the floor map and the IMR deviation in order to avoid obstacles on the path. The LiDAR setting and IMR speed regulation account for a critical issue. The study contributed to integrating a step-by-step procedure of implementing path planning and motion control using solely the LiDAR data along with the integration of various pieces of software. The control strategy is thus improved while experimenting with various proportional control gains for position, orientation, and velocity of the LiDAR in the IMR.

Singular spectrum analysis based structural damage identification in beams with multiple breathing cracks

This paper presents a generalized vibration-based multiple breathing crack localization technique in beams with an unknown number of cracks based on Singular Spectrum Analysis (SSA). Localization of multiple breathing cracks is a highly challenging inverse problem, as all these breathing cracks (more than two) might be in a similar state (either opening or closing state) at any particular time instant or in contrasting states (while some breathing cracks are opening, others in closing state) during vibration. The level of nonlinearity of vibration response is strongly dependent not only on each crack size, and location but also on the application of driving force along the cracked beam. The concept of varied input frequency excitation sources is employed in the present work for multiple breathing crack identification over the tedious traditional approach of varying input force application positions. The major advantage of using singular spectrum analysis in the present work is that it can reliably isolate and extract the very low-amplitude nonlinear sensitive components (super harmonics and intermodulation) being buried in the total response based on the pairwise eigenvalue property of harmonic components. Investigations have been carried out by varying the number, spatial locations, and also intensities of the breathing cracks. Sensitivities associated with measurement noise and also with limited sensors are also investigated. The results of both numerical and experimental investigations carried out in this paper concluded that the proposed SSA can effectively localize closely spaced or sparsely spaced (i.e.,, spatially far apart) multiple cracks in the beam. The proposed SSA approach is data-driven, works with limited sensors, and does not need reference healthy state measurements.

Global Mechanical Response Sensing of Corrugated Compensators Based on Digital Twins

The corrugated compensators are important components in the piping system, absorbing mechanical deformation flexibly. To reduce the risk of the piping system with corrugated compensators and improve the safety and stability of industrial equipment, condition monitoring and fault diagnosis of bellows is necessary. However, the stress monitoring method of corrugated compensators with limited localized sensors lack real-time and full-domain sensing. Therefore, this paper proposes a digital twin construction method for global mechanical response sensing of corrugated compensators, combining Gaussian process regression in machine learning and finite element analysis. The sensing data of three types of displacements are used as the associated information of a finite element model with 19,800 elements and its digital twin. The results show that the values of performance metrics correlation of determination R2 and standardized average leave-one-out cross-validation CVavg of the digital twin satisfy the recommended threshold, which indicates that the digital twin has excellent predictive performance. The single prediction time of the digital twin is 0.76% of the time spent on finite element analysis, and the prediction result has good consistency with the true response under dynamic input, indicating that the digital twin can achieve fast and accurate stress field prediction. The important state information hidden in the multi-source data obtained by limited sensors is effectively mined to achieve the real-time prediction of the stress field. This paper provides a new approach for intelligent sensing and feedback of corrugated compensators in the piping system.

A novel time domain-based multiple breathing crack diagnosis technique using Quadratic-Teager Kaiser Energy (Q-TKE)

This paper presents a generalized time domain-based multiple breathing crack localization technique in beams with an unknown number of cracks based on Quadratic-Teager Kaiser Energy (Q-TKE) using vibration data. Localization of multiple breathing cracks is a highly challenging inverse problem, as all these breathing cracks (more than two) might be in a similar state (either opening or closing state) at any particular time instant or in contrasting states (while some breathing cracks are opening, others are in closing state) during vibration. The level of nonlinearity of vibration response is strongly dependent not only on each crack size and location but also on the application of driving force along the cracked beam. The concept of varied input frequency excitation sources is employed in the present work for multiple breathing crack identification over the tedious traditional approach of varying input force application positions. The major advantage of using Q-TKE in the present work is that it can reliably isolate and extract the very low-amplitude nonlinear sensitive components (superharmonics and intermodulation) being buried in the total response and enhance them in the time domain signal itself. Investigations have been carried out by varying the number, spatial locations, and also intensities of the breathing cracks. Sensitivities associated with measurement noise and also with limited sensors is also investigated. The results of both numerical and experimental investigations carried out in this paper concluded that the proposed Q-TKE can effectively localize closely spaced or sparsely spaced (i.e., spatially far apart) multiple cracks in the beam. The proposed Q-TKE approach is data-driven, works with limited sensors, and does not need reference healthy state measurements.

Application of nitrogen-doped carbon particles modified electrode for electrochemical determination of tetrazepam as muscle relaxant drug

The objective of this work was to create a modified carbon paste electrode (N-CNP/CPE) with nitrogen-doped carbon nanoparticles for electrochemically detecting tetrazepam (TZ) as a muscle relaxant in samples of human blood serum. N-CNP was created using a microwave-assisted process, and N-CNP was applied to the CPE surface using an electrodeposition technique. N atoms were effectively doped within the carbon framework of CNP/CPE, according to the results of morphology and structure characterization using FE-SEM, XRD, and EDS analyses. Additionally, abundant and evenly distributed pores and a spherical structure of N-CNP were electrodeposited on the CPE surface. The electrochemical analyses' findings show that the N-CNP/CPE TZ sensor is sensitive and selective, with a broad linear range of 0–650 µg/mL and a sensitivity value of 0.29478 µA/µg.mL−1. The proposed TZ sensor's limit of detection was discovered to be a low value of 5 ng/mL, which was a relatively low value of LOD and a broad linear range compared to other TZ sensors. Results indicated acceptable recovery values (more than 98.53%) and appropriate precision (RSD less than 5.08%), as well as good agreement between the DPV measurements and ELISA technique, for the determination of TZ in prepared samples of blood serum from bodybuilding athletes. These results show that the TZ content of blood samples from athletes may be monitored using the N-CNP/CPE as a trustworthy TZ sensor.

Optimized Energy-Efficient Routing Protocol for Wireless Sensor Network Integrated with IoT: An Approach Based on Deep Convolutional Neural Network and Metaheuristic Algorithms

Wireless sensor networks (WSNs) have emerged as a significant architecture for data collection in various applications. However, the integration of WSNs with IoT poses energy-related challenges due to limited sensor node energy, increased energy consumption for wireless data sharing, and the necessity of energy-efficient routing protocols for reliable transmission and reduced energy consumption. This paper proposes an optimized energy-efficient routing protocol for wireless sensor networks integrated with the Internet of Things. The protocol aims to improve network lifetime and secure data transmission by identifying the optimal Cluster Heads (CHs) in the network, selected using a Tree Hierarchical Deep Convolutional Neural Network. To achieve this, the paper introduces a fitness function that takes into account cluster density, traffic rate, energy, collision, delay throughput, and distance from the capacity node. Additionally, the paper considers three factors, including trust, connectivity, and QoS, to determine the best course of action. The paper also presents a novel optimization approach, using the hybrid Marine Predators Algorithm (MPA) and Woodpecker Mating Algorithm (WMA), to optimize trust, connectivity, and QoS parameters for optimal path selection with minimal delay. The simulation process is implemented in MATLAB, and the developed method’s efficiency is evaluated using several performance metrics. The results of the simulation demonstrate the effectiveness of the proposed method, which achieved significantly lower delay (99.67%, 98.38%, 89.34%, and 97.45%), higher delivery ratio (89.34%, 89.34%, 83.12%, and 88.96%), and lower packet drop (93.15%, 91.25%, 79.90%, and 92.88%) in comparison to existing methods. These outcomes indicate the potential of the optimized energy-efficient routing protocol to improve network lifetime and ensure secure data transmission in WSNs integrated with IoT.

Adaptive Fault-Tolerant Tracking Control for Uncertain Nonlinear Systems With Unknown Control Directions and Limited Resolution

This article addresses the adaptive fault-tolerant tracking control (FTTC) problem for a family of strict-feedback uncertain nonlinear systems subject to limited sensor resolution and unknown control directions. Both partial loss-of-effectiveness (LOE) and lock-in-place (LIP) faults of actuators are investigated. An adaptive control strategy based on a Nussbaum-type function and neural networks is presented by introducing a backstepping approach to make the system output track a desired reference output signal with bounded tracking error in the case of faulty actuators. The effect of the limited resolution is decoupled from the nonlinear system and is approximated using a neural network. The impact of disturbances is effectively compensated by utilizing adaptive parameter estimation terms in the backstepping procedure. It is proven that the proposed FTTC strategy can ensure the boundedness of all signals and guarantee that the output tracking error can converge into a small neighborhood of the origin. Two simulation examples are given to illustrate the effectiveness of the proposed FTTC strategy.

Diagnostics-oriented Model for Automotive SCR-ASC

This paper presents a diagnostics-oriented aging model for combined Selective Catalytic Reduction (SCR) and Ammonia Slip Catalyst (ASC) system, along with a model-based on-board diagnostic (OBD) method applied to both test-cell data and on-road data from commercial trucks. The key challenge with model development was unavailability of NOx and NH3 measurements between SCR and ASC. Since it would have been very difficult to calibrate both SCR and ASC dynamics without any measurements between SCR and ASC, therefore ASC was modeled using static look-up tables to determine ASC’s NH3 conversion efficiency and its selectivity to NOx and N2O as a function of temperature and flow rate. The traditional three-state single-cell ordinary differential equation (ODE) model was used for SCR. Hot Federal Test Procedure (hFTP) was used to calibrate the model. Cold FTP (cFTP) and Ramped Mode Cycle (RMC) were used for validation. Results show that the SCR-ASC model can capture the aging signatures in tailpipe NOx, NH3, and N2O reasonably well for cFTP, hFTP, and RMC cycles in the testcell data. After slight re-calibration and combining with a simple model for commercial NOx sensor’s cross-sensitivity to NH3, the model works reasonably well for on-road data from commercial trucks. A model-based on-board diagnostic (OBD) method has been presented with enable conditions designed to detect operating conditions suitable for detecting aging signatures, while minimizing false positives and false negatives. The OBD method is applied to both test-cell and real-world truck data with commercial NOx sensors. Results on test-cell data demonstrate the challenges of robust SCR monitoring even on the limited data set used in this work. The model-based enable conditions are shown to be robust but extremely restrictive as the OBD gets enabled at very few points in the test-cell data. Application on truck data showed that the proposed OBD method can be implemented on commercial trucks with limited sensors. In the truck data, the enable conditions were satisfied on many more points than the test-cell data. Results on truck data show encouraging trends between relative degradation level and the number of miles on four trucks. In future work, these trends will be validated using more data from commercial trucks with known aging levels.

Automated damage diagnosis of concrete jack arch beam using optimized deep stacked autoencoders and multi-sensor fusion

A novel hybrid framework of optimized deep learning models combined with multi-sensor fusion is developed for condition diagnosis of concrete arch beam. The vibration responses of structure are first processed by principal component analysis for dimensionality reduction and noise elimination. Then, the deep network based on stacked autoencoders (SAE) is established at each sensor for initial condition diagnosis, where extracted principal components and corresponding condition categories are inputs and output, respectively. To enhance diagnostic accuracy of proposed deep SAE, an enhanced whale optimization algorithm is proposed to optimize network meta-parameters. Eventually, Dempster-Shafer fusion algorithm is employed to combine initial diagnosis results from each sensor to make a final diagnosis. A miniature structural component of Sydney Harbour Bridge with artificial multiple progressive damages is tested in laboratory. The results demonstrate that the proposed method can detect structural damage accurately, even under the condition of limited sensors and high levels of uncertainties.

Sensor dynamic compensation method based on GAN and its application in shockwave measurement

Limited by the dynamic characteristics of the sensor, the high-frequency signal will be distorted by the dynamic error after passing through the sensor, which will affect the accuracy of the real value. To reduce the dynamic error, it is necessary to obtain a high-precision dynamic compensation model. This paper provides a solution of compensation model based on the deep learning method. First, the problem of limited sensor dynamic data is solved by data augmentation through Deep Convolutional Generative Adversarial Network. After that, the sensor compensation model is obtained by Speech Enhancement Generative Adversarial Network and applied to step signals and shockwave signals. This compensation method can compensate a variety of sensors used in the dynamic measurement. It is verified by the pressure sensor as an example in this paper, the results are better than that of traditional ones, the overshoot can be reduced from 119.2% to 2.5%, and the rising time is 5.5μs. The innovation of this paper is that we find a way to use deep learning methods to compensate sensor dynamic error based on a small dataset. At the same time, it is proved that this method has strong versatility, which is not available in traditional sensor compensation methods. It also provides a feasible scheme for the application of deep learning in dynamic compensation model calculation.

A cluster analysis approach to sampling domestic properties for sensor deployment

Sensors are an increasingly widespread tool for monitoring utility usage (e.g., electricity) and environmental data (e.g., temperature). In large-scale projects, it is often impractical and sometimes impossible to place sensors at all sites of interest, for example due to limited sensor numbers or access. We test whether cluster analysis can be used to address this problem. We create clusters of potential sensor sites using factors that may influence sensor measurements. The clusters provide groups of sites that are similar to each other, and that differ between groups. Sampling a few sites from each group provides a subset that captures the diversity of sites. We test the approach with two types of sensors: utility usage (gas and water) and outdoor environment. Using a separate analysis for each sensor type, we create clusters using characteristics from up to 298 potential sites. We sample across these clusters to provide representative coverage for sensor installations. We verify the approach using data from the sensors installed as a result of the sampling, as well as using other sensor measures from all available sites over one year. Results show that sensor data vary across clusters, and vary with the factors used to create the clusters, thereby providing evidence that this cluster-based approach captures differences across sensor sites. This novel methodology provides representative sampling across potential sensor sites. It is generalisable to other sensor types and to any situation in which influencing factors at potential sites are known. We also discuss recommendations for future sensor-based large-scale projects.

A Review of Thermal Spectral Imaging Methods for Monitoring High-Temperature Molten Material Streams.

Real-time closed-loop control of metallurgical processes is still in its infancy, mostly based on simple models and limited sensor data and challenged by extreme temperature and harsh process conditions. Contact-free thermal imaging-based measurement approaches thus appear to be particularly suitable for process monitoring. With the potential to generate vast amounts of accurate data in real time and combined with artificial intelligence methods to enable real-time analysis and integration of expert knowledge, thermal spectral imaging is identified as a promising method offering more robust and accurate identification of key parameters, such as surface temperature, morphology, composition, and flow rate.

Linear Quadratic Optimal Control with the Finite State for Suspension System

The control algorithm could greatly help the suspension system improve the comprehensive performance of the vehicle. Existing control methods need to obtain the intermediate states, which are difficult to obtain directly or accurately when estimated by filters or observers. Thus, this paper proposed a new practical finite state LQR control method to deal with this problem. By combining with the output state of the finite sensor of the vehicle suspension system and weakening the unknown state as the goal, an optimization model is established with the design variables as the LQR weight coefficients. Then, the direct relationship between the current control input and the finite sensor output is obtained, and the finite state LQR control is realized. Taking the quarter-car suspension model as an example, the corresponding noise is added considering sensor accuracy, and the control performance of the four control methods is studied considering the uncertainties of suspension system parameters. In addition, the acceleration of sprung mass and the dynamic travel coefficient of suspension have been separately calculated by methods of finite state LQR control, LQR control, and PID control. The results show that there is not much difference between them under shock excitation or random excitation. However, the finite state LQR control method has the best comprehensive control performance in that its dynamic tire load coefficient is better than other methods; it could take into account the suspension work stroke coefficient, dynamic tire load coefficient, and sprung mass’ acceleration of the vehicle suspension system at the same time. In order to realize the optimal control effect with limited sensor arrangement, the finite state LQR control method only needs to obtain the current sensor output and the current control input, without estimating the unknown intermediate state. By this means, the proposed control method greatly simplifies the design of the control system and has great advantages on practical value.

Excitation of Mechanical Resonances in the Stationary Ring of a Mechanical Seal by a Continuously Operated Electromagnetic Acoustic Transducer.

Acoustic/ultrasonic testing is now a common method in the field of nondestructive testing for detecting material defects or monitoring ongoing mechanical changes in a structure during operation. In many applications, piezoelectric transducers are used to generate mechanical waves inside the specimen. Their actual operating frequency is highly dependent on the dimensions of the transducer. Larger dimensions of the piezoelectric transducer allow for a lower operating frequency. However, these dimensions limit the use of piezoelectric transducers in certain applications where the size of the transducer is restricted due to limited installation space and when low-frequency excitation is required. One application that places these requirements on the transducer is the monitoring of mechanical seals. Here, the transducer must be mounted on the stationary ring of the seal. In this paper, a continuously operated electromagnetic acoustic transducer (EMAT) is presented as an alternative to piezoelectric transducers as a transmitter. The advantage of a EMAT is that it meets the requirements of limited sensor size (sensor area < 10 × 6 mm) and can excite mechanical waves with frequencies below 10 kHz. A structural analysis of the stationary ring shows that the first two mechanical resonances occur around 4 and 5.5 kHz. An experimental study meterologically demonstrates the ability of the EMAT to excite these first two mechanical resonances of the ring. A comparative simulation agrees well with the measurement.

Coverage Optimization for Directional Sensor Networks: A Novel Sensor Redeployment Scheme

The ever-growing Internet of Things (IoT) provides a powerful means for complex and changeable environmental monitoring. Directional sensor networks (DSNs), as a typical architecture of IoT, can efficiently facilitate various digital and intelligent IoT applications. In the DSNs, due to the asymmetry in coverage focus and diversity in detection angle of the directional IoT sensors, how to enhance the coverage performance with the limited sensors becomes a new challenge. To this end, we develop a novel sensor redeployment scheme based on the minimum exposure path (MEP) to optimize the coverage performance of the DSNs. Specifically, we first propose a minimum exposure path searching algorithm based on the particle swarm optimization (MEP-PSO) algorithm with the target of obtaining the MEP in the DSNs. With this algorithm, the traditional MEP problem can be analyzed and simplified by conducting the grid discretization and building the weighted undirected graph. Then, an MEP-based coverage optimization (MEP-CO) algorithm is proposed to determine the optimal deployment locations and the dispatch sensors so that the IoT sensors can be dynamically redeployed to achieve the coverage optimization. After that, we derive the formula for the coverage upper bound (CUB) and develop a CUB algorithm to provide a benchmark for evaluating the effectiveness of different coverage optimization algorithms. Simulation results demonstrate that the proposed coverage optimization scheme can significantly promote the minimum exposure value (MEV) and coverage ratio of the monitoring area compared with the existing algorithms.

Cache controlled cluster networking protocol

When technologies such as Wireless Sensor Network, Internet of Things and Cloud Computing are coupled together, real-world issues can be remarkably resolved. Wireless Sensor Network sends enormous data to a cloud server via the internet. Some applications necessitate a huge number of sensors spread across a large area. Limited battery-powered sensors send lots of data to the Base Station. To save energy and extend sensor lifespan, sensors can be networked instead of talking directly with the Base Station. Our present research work proposes an innovative Cache-Controlled Cluster Networking Protocol (CCCNP). It is a cache-supervised dynamic cluster-based sensor networking technique. In CCCNP, as the complete network is under cache control, the number of caches established in the network and their location play a very crucial role. Therefore, we first formulate the equation for the appropriate number of caches and their location. Then the caches create a network cluster and support data transmission. It stabilizes cluster formation process and decreases sensor node overhead to increase network lifetime. We simulate the CCCNP, Low Energy Adaptive Clustering Hierarchy (LEACH), Improved LEACH (I-LEACH), and LEACH with Vice-cluster Head (LEACH-VH) algorithms for two different types of networks and compare them. CCCNP outperforms other algorithms both the configurations. The dead node rate decreased by three times for each round. Even the lifespan of nodes far from the BS improved by 3.51 and 2.87 times for both network configurations, respectively. The average throughput increased by 350%, and the average lifespan increased up to 289% of the rounds.

Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

Wireless Sensor Network (WSN) consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment. Designing the energy-efficient data collection methods in large-scale wireless sensor networks is considered to be a difficult area in the research. Sensor node clustering is a popular approach for WSN. Moreover, the sensor nodes are grouped to form clusters in a cluster-based WSN environment. The battery performance of the sensor nodes is likewise constrained. As a result, the energy efficiency of WSNs is critical. In specific, the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station (BS). Therefore, energy efficiency and load balancing are very essential in WSN. In the proposed method, a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques (GW-IPSO-TS) was used. The selection of Cluster Heads (CHs) and routing path of every CH from the base station is enhanced by the proposed method. It provides the best routing path and increases the lifetime and energy efficiency of the network. End-to-end delay and packet loss rate have also been improved. The proposed GW-IPSO-TS method enhances the evaluation of alive nodes, dead nodes, network survival index, convergence rate, and standard deviation of sensor nodes. Compared to the existing algorithms, the proposed method outperforms better and improves the lifetime of the network.