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With the rapid development, extensive knowledge, and diverse application scenarios of target perception and positioning technology in sensor networks, a passive target perception and localization platform based on Wireless Sensor Networks (WSN) has been designed. The platform is mainly applied for the teaching of electronic information, communication, and Internet of Things (IoT) engineering. The platform follows the teaching concept of "developing students' ability to solve complex engineering problems" in the emerging engineering discipline and combines experimental simulation with real-world testing, as well as integrating scientific research and teaching. It encompasses technical elements such as perception and localization theory, sparse representation classification modeling, solving underdetermined equations, data analysis, and sparse coding, as well as nontechnical aspects such as team collaboration and cost budgeting. The platform boasts high fidelity and scalability, providing students with the opportunity to develop comprehensive practical and innovative skills in solving complex engineering problems. [ABSTRACT FROM AUTHOR]

Over the last decade, concepts such as industry 4.0 and the Internet of Things (IoT) have contributed to the increase in the availability and affordability of sensing technology. In this context, structural health monitoring (SHM) arises as an especially interesting field to integrate and develop these new sensing capabilities, given the criticality of structural application for human life and the elevated costs of manual monitoring. Due to the scale of structural systems, one of the main challenges when designing a modern monitoring system is the optimal sensor placement (OSP) problem. The OSP problem is combinatorial in nature, making its exact solution infeasible in most practical cases, usually requiring the use of metaheuristic optimization techniques. While approaches such as genetic algorithms (GAs) have been able to produce significant results in many practical case studies, their ability to scale up to more complex structures is still an area of open research. This study proposes a novel quantum-based combinatorial optimization approach to solve the OSP problem approximately, within the context of SHM. For this purpose, a quadratic unconstrained binary optimization (QUBO) model formulation is developed, taking as a starting point of the modal strain energy (MSE) of the structure. The framework is tested using numerical simulations of Warren truss bridges of varying scales. The results obtained using the proposed framework are compared against exhaustive search approaches to verify their performance. More importantly, a detailed discussion of the current limitations of the technology and the future paths of research in the area is presented to the reader. [ABSTRACT FROM AUTHOR]

Received signal strength- (RSS-) based localization in wireless sensor networks (WSNs) has attracted significant attention due to its advantages of low cost and simple implementation. In practice, RSS measurements may suffer from sensor biases, which deteriorates the localization accuracy. However, most of the existing localization methods are designed for bias-free measurements. In this paper, we propose a convex combination method for RSS localization in the presence of sensor biases. The parameter vector composed of unknown location and sensor biases is estimated simultaneously by using a convex combination of some virtual points. These virtual points form a convex hull, into which the parameter vector falls with large probability. By this, the original nonconvex estimation problem is converted to be convex. Numerical examples demonstrate the superiority of the proposed method in terms of localization accuracy, compared to the existing semidefinite programming (SDP) methods. [ABSTRACT FROM AUTHOR]

MEMS-IMUs are widely used in research, industry, and commerce. A proper calibration technique must reduce their innate errors. In this study, a turntable-based IMU calibration approach was presented. Parameters such as the bias, lever arm, and scale factor, in addition to misalignment, are included in the general nonlinear model of the IMU output. Accelerometer error parameters were estimated using the transformed unscented Kalman filter (TUKF) with triangulation algorithm is suggested for calibrating inertial measurement unit (MPU6050) three-axes accelerometer. In contrast to the present methods, the suggested method uses the gravitational signal as a constant reference and necessitates no external equipment. The technique requires that the sensor be positioned in a rough orientation and that basic rotations be adopted. This technology also offers a quicker and easier calibration. Comparing the experimental findings with other works, Allan deviation shows significant improvements for the bias instability, where a bias instability of (0.116 μg) is achieved at temperatures between (−15°C) and (80°C). [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) hierarchical (clustering) routing involves the gathering of sensor nodes to form clusters in which sensor node communicates to cluster head (CH). The CH is responsible for data aggregation and forwarding collected data toward base station (BS). Energy expenditure in usual protocols becomes unequal because of clusters having diverse lengths. In this paper, we propose another new enhanced clustering protocol called adaptive threshold residual energy-based efficient sensor network protocol (ATREEN) for adaptive selection of CH in WSNs. In ATREEN, CHs are chosen by comparing threshold of node value with respect to distance between cluster member and CH, residual energy of sensor nodes, and position of sensor node with respect to BS. Simulation results show that ATREEN protocol extends network lifetime and stability with decline in energy dissipation compared to clustering protocols such as low-energy adaptive clustering hierarchy (LEACH), power-efficient gathering in sensor information systems (PEGASIS), and distributed energy-efficient clustering (DEEC) scheme for heterogeneous WSNs. [ABSTRACT FROM AUTHOR]

Received signal strength- (RSS-) based localization has attracted considerable attention for its low cost and easy implementation. In plenty of existing work, sensor positions, which play an important role in source localization, are usually assumed perfectly known. Unfortunately, they are often subject to uncertainties, which directly leads to effect on localization result. To tackle this problem, we study the RSS-based source localization with sensor position uncertainty. Sensor position uncertainty will be modeled as two types: Gaussian random variable and unknown nonrandom variable. For either of the models, two semidefinite programming (SDP) methods are proposed, i.e., SDP-1 and SDP-2. The SDP-1 method proceeds from the nonconvex problem with respect to the maximum likelihood (ML) estimation and then obtains an SDP problem using proper approximation and relaxation. The SDP-2 method first transfers the sensor position uncertainties to the source position and then obtains an SDP formulation following a similar idea as in SDP-1 method. Numerical examples demonstrate the performance superiority of the proposed methods, compared to some existing methods assuming perfect sensor position information. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network (WSN) is a powerful tool to help humans monitor a specific area, and the deployment strategy of sensors profoundly determines the performance of WSN. How to find the best deployment method has become the research topic for many scholars. The deployment strategy aims to expand the deployment scope, reduce energy consumption, and reduce duplicate coverage areas. Many multiobjective heuristic algorithms have been proposed to solve this problem. This paper proposes a multiobjective adaptive fish migration optimization (MAFMO) algorithm, which adds an adaptive-based repository and crowding degree-selection strategy for multiobjective optimization. The simulation results reveal that the MAFMO algorithm has more advantages in malleability and distribution than other famous algorithms. Finally, the algorithm is applied to the WSN deployment problem, and the simulation results are compared with other algorithms. The results show that a better solution can be found using MAFMO. [ABSTRACT FROM AUTHOR]

Scanning the bridge's frequencies from the passing vehicle's vibration data has been frequently investigated recently. However, in previous studies, vehicles were typically simplified to quarter- or half-car models, and apparent disparity could be observed between the models and real vehicles. To make the vehicle model more practical, in this study, a 3D vehicle model is built to extract the bridge's frequencies from vehicle vibrations. For the first time, equations for calculating the contact-point (CP) response of the 3D vehicle model are derived with tire damping. Furthermore, residual CP responses between front and rear wheels are utilized to eliminate the inverse effects of road roughness, making the bridge frequencies outstanding in the frequency domain. The robustness of the proposed method is tested under different influence factors, and two possible measurement errors are as follows: the sensor position and axle distance when applying the proposed method in engineering. Results show that the proposed method performs stably under the influence of different road roughness classes and tire damping. Bridge frequencies can be identified when the vehicle is travelling at a highway speed (108 km/h in this study). Environmental noises can submerge the bridge's high-order frequencies but have little influence on the low-frequency range. High bridge damping will restrain the transmission of bridge vibration to the vehicle, making high-order bridge frequencies less visible. In addition, the errors introduced by a vehicle body sensor position can be eliminated when calculating the CP responses for tires, thus will not influence bridge frequency identification. To avoid possible errors induced by manual measurement of the axle distance, a novel cross-correlation function-based method is employed, which is verified effective and practical for calculating residual CP responses. [ABSTRACT FROM AUTHOR]

Study of thermo-fluid flow has its significance in energy analysis of a building. Various thermo-fluid analyses are performed in heating ventilation and air conditioning (HVAC) systems for energy efficient buildings by scientists and engineers to save energy consumption during the life of a building. In order to achieve energy conservation for different HVAC systems, determination of air flow pattern and flow physics are vital parameters. In case of fluid flow postprocessing techniques such as streamlines, vector plots and contours are often employed. These techniques help in understanding the nature of flow and its properties. Each of these postprocessing techniques mentioned are based on Eulerian methods and have certain inherent deficiencies pertaining to the amount of information they can convey about certain aspects of fluid flow. Lagrangian coherent structures (LCS) on the other hand use Lagrangian data for analysis purposes. LCS are generated using finite time Lyapunov exponent fields which in turn depict the rate of expansion or contraction of the trajectories around a certain point. LCS act as the transport barriers across which there is approximately zero mass flux. This property means that LCS can be applied to problems related to separation and reattachment in fluid flow and find virtual boundaries inside flows. In the current study, we focused on the application of LCS for efficient placement of sensors for HVAC systems. We computed LCS using velocity data extracted from the CFD simulations of a 2D room model. Thus, LCS can be used to identify the virtual boundaries in fluid flow. This helps in indication of regions where mixing of particles occurs and also where particles are stagnated. Inlet angles of 0, 15, 22, and 30 degrees are used and analysis shows that manifolds with 30 degree angles provide better ventilation. The outcome of this study can be used to improve the energy efficiency as well as predict the accurate location of HVAC sensor and control units. [ABSTRACT FROM AUTHOR]

Wireless capsule endoscopy (WCE) is used to diagnose lesions in the gastrointestinal (GI) tract. The physicians require to know the exact position of the lesions which can be performed by localizing the WCE in the GI tract. In this paper, we propose ultra-wideband (UWB) path loss-based WCE localization and compute the Cramer–Rao lower bound (CRLB) to evaluate the accuracy bounds of localization in the small intestine. First, we propose the estimation of smoothed path loss by minimizing the path loss deviations caused by shadow fading effects of body tissues. Then, the estimated path loss is used to estimate the degree of path loss and to compute the weight of the sensor's positions. Finally, we propose the smoothed path loss degree-based weighted centroid localization (SPLD-WCL) algorithm to estimate the location of the WCE. We simulate the proposed SPLD-WCL algorithm and verify the accuracy by comparing it to the computed CRLB. The proposed SPLD-WCL localization algorithm shows a significantly high accuracy of localization with a 6.83 m m root mean square error R M S E without any advanced knowledge of unknown parameters and bounds. [ABSTRACT FROM AUTHOR]

In a wireless sensor network, information processing, and information acquisition, localization technology is the key to making it practically possible application. Approximate Point-in-Triangulation (APIT) is the most widely used localization estimation which has high accuracy in localizing the nodes and ease of deployment of nodes in the real-time environment. Though it has numerous key advantages, some of the drawbacks which make it a little setback in preference are the unevenness in the distribution of nodes. Tracking is more appropriate for mobile sensor nodes than tracking is for static sensor nodes. The two main types of localization algorithms are range-based and range-free techniques. In an indoor setting, the projected range (distance) between an anchor and an unknown node is very inaccurate. By utilizing a large number of already existing access points (APs) in the range-free localization approach, this issue can be overcome to a great extent. The utilization of multisensor data, such as magnetic, inertial, compass, gyroscope, ultrasonic, infrared, visual, and/or odometer, is stressed in recent research to further increase localization accuracy. The tracking system also makes location predictions for the future based on historical location data. To overcome this issue, the proposed localization algorithm of APIT with Bat-SA proves its efficiency. Due to its low localization error, the traditional Bat method is more accurate than APIT. The proposed Bat using the SA algorithm is found to perform better than the traditional APIT algorithm in terms of convergence of computing rate and success rate. In order to mimic the suggested APIT method, it is paired with the Bat-SA localization technique. Simulation evaluation proves the performance efficiency of the proposed algorithm. The performance metrics parameters are latency, node distribution map, positioning error map, and neighbor relationship diagram which are used to evaluate the proposed method. [ABSTRACT FROM AUTHOR]

In recent times, the deployment of wireless sensor networks becomes important in revolutionary areas such as smart cities, environmental monitoring, smart transportation, and smart industries. The battery power of sensor nodes is limited due to which their efficient utilization is much necessary as the battery is irreplaceable. Efficient energy utilization is addressed as one of the important issues by many researchers recently in WSN. Clustering is one of the fundamental approaches used for efficient energy utilization in WSNs. The clustering method should be effective for the selection of optimal clusters with efficient energy consumption. Extensive modification in the clustering approaches leads to an increase in the lifetime of sensor nodes which is a unique way for network lifetime enhancement. As the technologies were taken to next the level where multiparameters need to be considered in almost every application in clustering, multiple factors affect the clustering and these factors were conflicting in nature too. Due to the conflicting nature of these factors, it becomes difficult to coordinate among them for optimized clustering. In this paper, we have considered multiattributes and made coordination among these attributes for optimal cluster head selection. We have considered Multi-Attribute Decision-Making (MADM) methods for CH's selection from the available alternatives by making suitable coordination among these attributes, and comparative analysis has been taken in LEACH, LEACH-C, EECS, HEED, HEEC, and DEECET algorithms. The experimental results validate that using MADM approaches, the proposed APRO algorithm proves to be one of the better exhibits for choosing the available CHs. [ABSTRACT FROM AUTHOR]

A foot placement of inertial sensors is commonly used for heel-strike (HS) and toe-off (TO) event detection. However, in clinical practice, such sensor placement may be difficult or even impossible due to the deformity of patients' feet. The first contribution of this paper is a new algorithm for HS and TO event detection for cases when the sensors are placed on the lateral malleolus. Such sensor placement allows gait analysis in patients with foot deformities. In addition, the placement of the sensor directly on the wide bone surface of the lateral malleolus ensures secure fixation of the sensor during walking. The proposed algorithm is based on deep neural networks, which can be easily adapted (by retraining the neural networks) for analysis of various pathological gait patterns. It is especially important in clinical practice when the number of possible pathological gait patterns is very large. The algorithm proposed in this paper was implemented in a new wearable system for the clinical gait analysis. The second contribution is a validation of this new wearable system. The performance of both proposed algorithm and gait analysis system was evaluated against a reference treadmill system where a capacitance–based pressure platform was used. A total of 117 healthy volunteers participated in the comparison (62 males and 55 females, age 24–55 years, height 162–183 cm). They were asked to perform 2 min walking trials with different speed. Mean accuracy ± precision was – 0.021 ± 0.091 s for gait cycle, 0.589 ± 1.144 steps/min for cadence, – 0.051 ± 0.544 % for stance phase, – 0.37 ± 0.649 % for single support, 0.296 ± 0.711 % for double support, 0.132 ± 0.561 % for load response, and 0.106 ± 0.661 % for preswing. Limitations of the proposed algorithm and its compassion with state-of-the-art algorithms were discussed. [ABSTRACT FROM AUTHOR]

Pipelines are crucial for transporting liquid and gaseous resources at various industrial sites. As unexpected pipeline failures and improper spill responses often cause detrimental effects on the environment and public safety, real-time leakage monitoring and detection are important. This study proposes a nonintrusive sensor system to measure the pressure and integrity of a pipeline in real time by easily installing nonintrusive sensors on the exterior of the pipelines. Initially, a prototype of the nonintrusive pressure-monitoring sensor was designed and fabricated. This sensor correlated the changes in pressure inside a pipe with those in the contact force exerted between the pipe and the surrounding high-stiffness band; a force-sensing resistor was used to measure the contact force. Furthermore, parametric studies were performed on a six-inch pipe, which indicated that the developed sensor can accurately monitor the inner pressure of the pipe. We also developed a methodology to determine the optimal sensor placement in a pipeline network for detecting damages using the Euclidean distance method. This was combined with the methodology of determining the decision boundary for classifying the damage location using a support vector machine to rapidly detect the damage using a minimum number of sensors. Finally, a nonintrusive sensor system was installed on the fire main pipeline system, and the damage detection performance was experimentally validated. The developed nonintrusive sensor system can be easily installed on operating pipes to monitor the inner pressure of a pipe in real-time and accurately detect damages in a pipeline network. [ABSTRACT FROM AUTHOR]

Background and Objectives. Few studies have evaluated differences in muscle architecture in quadriceps femoris constituents with sensor electrodes positioned over vastus lateralis (VL) and vastus medialis (VM) motor points during a neuromuscular electrical stimulation (NMES) session. We aimed to investigate the changes in muscle architecture of the rectus femoris (RF), VL, VM, and vastus intermedius (VI) portions during evoked contractions with sensor electrodes placed over VL and VM motor points. Materials and Methods. The study is a crossover, repeated-measure design, conducted with healthy males aged 24.0 ± 4.6 years. Ultrasonography at rest and evoked contraction at 40% of maximum voluntary contraction (MVC) were used to assess the pennation angle (θ p) and fascicle length (Lf) of RF, VL, VM, and VI portions. Results. The mean torque observed was 201.14 ± 50.22 N.m during MVC and at 40% of MVC was 80.45 ± 20.08 N.m. There was no difference for θ p comparing four components of the quadriceps femoris (p = 0.27). There was a significant (p < 0.05) muscle evoked contraction interaction for Lf without relevant clinical importance to the study. Conclusions. There is no difference in the changes in the muscle architecture of quadriceps femoris constituents during stimulation with the electrodes placed on the VL and VM motor points. Therefore, clinicians can choose either VL or VM motor points for sensor electrode positioning and expect similar muscle architecture adaptation for a given evoked torque. Future clinical studies should be conducted to establish the optimal electrode positioning over different portions of the quadriceps muscle to optimize more rational NMES clinical settings. [ABSTRACT FROM AUTHOR]

Localization is crucial to wireless sensor networks. Among the recently proposed localization algorithms, the mobile anchor-assisted localization (MAL) algorithm seems promising. A MAL algorithm using a single mobile anchor has low energy consumption but a high localization error. Conversely, a MAL algorithm with three or more mobile anchors has minor localization errors but high energy consumption. By balancing energy consumption and localization accuracy, our study developed a localization algorithm assisted by two mobile anchors. A mobile anchor traverses the network along a double anchor SCAN (DASCAN) path, which divides the deployment region into grids and requires the two mobile anchors to traverse different horizontal lines in a zigzag pattern. Sensor nodes estimate their locations using a multiple-disturbance strategy grey wolf optimization (MDS-GWO) algorithm, which improves optimization by introducing a nonlinearly decreasing weight, a random perturbation of grey wolves and a mirror grey wolf. Using MATLAB, DASCAN was compared with GTURN, GSCAN, PP-MMAN, H-Curves, M-Curves, and SCAN paths by their energy consumption and localization rates. The localization error of MDS-GWO was compared with trilateration, PSO, WOA, and GWO. The impacts of radio irregularity, radio radius, and fading effect on MDS-GWO with different paths were also analyzed. The simulation results showed that the energy consumption of DASCAN was, on average, 30.1% less than GSCAN, GTURN, and PP-MMAN, but they had almost the same localization accuracy. The energy consumption of DASCAN was an average of 18.67% more than M-Curves, H-Curves, and SCAN, but the localization error of DASCAN was average of 32.3% less than SCAN, H-Curves, and M-Curves. The localization error of MDS-GWO was average of 25.5% less than trilateration, PSO, WOA, and GWO. Moreover, the performance of the proposed algorithm was less affected by different setups than the compared methods. [ABSTRACT FROM AUTHOR]

Sensing is the cornerstone of any functional structural health monitoring technology, with sensor number and placement being a key aspect for reliable monitoring. We introduce for the first time a robust methodology for optimal sensor configuration based on the value of information that accounts for (1) uncertainties from updatable and nonupdatable parameters, (2) variability of the objective function with respect to nonupdatable parameters, and (3) the spatial correlation between sensors. The optimal sensor configuration is obtained by maximizing the expected value of information, which leads to a cost‐benefit analysis that entails model parameter uncertainties. The proposed methodology is demonstrated on an application of structural health monitoring in plate‐like structures using ultrasonic guided waves. We show that accounting for uncertainties is critical for an accurate diagnosis of damage. Furthermore, we provide critical assessment of the role of both the effect of modeling and measurement uncertainties and the optimization algorithm on the resulting sensor placement. The results on the health monitoring of an aluminum plate indicate the effectiveness and efficiency of the proposed methodology in discovering optimal sensor configurations. [ABSTRACT FROM AUTHOR]

Achieving maximum network coverage with a limited number of sensor nodes is key to node deployment of wireless sensor network (WSN). This paper proposes an improved marine predator algorithm (IMPA) for 3D surface wireless sensor network deployment. A population evolution strategy based on random opposition-based learning and differential evolution operator is proposed to enrich the population diversity and improve the global search capability of the algorithm. The grouping idea of the Shuffled Frog Leaping Algorithm (SFLA) is then introduced. A local search strategy based on the SFLA is proposed to replace the FADs effect of MPA and enhance the ability of the algorithm to escape from the local optimum. A quasireflected opposition-based learning strategy is also presented to improve the optimization accuracy, accelerate the convergence speed of the algorithm, and improve the quality of the solution. Fifteen benchmark functions are selected for testing. The results are compared with seven different algorithms. The results show that the improved algorithm has excellent optimization performances. Finally, the IMPA is applied to optimize WSN coverage on 3D surfaces. The experimental results show that the proposed IMPA has good terrain adaptation and optimal deployment capabilities. It can improve the coverage of the network, reduce the deployment cost, and extend the network life cycle. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a sensor network configuration optimization approach is proposed to design informative and energy‐efficient wireless sensor networks. In particular, the design of cluster‐based versatile wireless sensor networks for structural health monitoring is considered. In contrast to conventional cluster‐based wireless sensor placement methods, a clustering optimization algorithm is proposed to determine the optimal locations of the cluster heads and the base station to enhance the energy efficiency of the network. The proposed approach determines the optimal wireless sensor network configuration that achieves the required estimation accuracy with minimal energy cost. Moreover, the proposed approach utilizes a holistic measure to assess the overall performance of multitype sensing devices. Furthermore, by implementing a genetic algorithm (GA) strategy, the proposed approach is computationally efficient and widely applicable for large‐scale civil engineering infrastructures. To demonstrate the performance of the proposed approach, the wireless sensor network configuration design of a bridge model and a space truss model is presented. [ABSTRACT FROM AUTHOR]

Summary: Since concrete structures are usually accompanied with cracks during service life, it is of great significance to conduct health monitoring for damage assessment and warning. In recent years, piezoceramic transducers have shown their unique advantages in structural damage monitoring. However, multidirectional crack monitoring of concrete structures with complex geometries based on piezoceramic transducers is still a research gap. In addition, there is a lack of strategies for sensor placement of a given concrete structure. This paper presents the authors' recent work to perform multidirectional crack monitoring of concrete structures using embedded 3D piezoceramic sensing array, which is composed of spherical smart aggregate (SSA) and smart aggregate (SA). A case study was investigated on a full‐scaled Skew Inverted‐T Bent Cap (60‐ITBC) specimen. Concrete damaged plasticity (CDP) analysis based on finite element model was first carried out to provide guidance for sensor placement strategy. The 60‐ITBC specimen with pre‐embedded SSA‐SA sensing array was then tested under static load. Meanwhile, SSA‐SA sensing array was controlled by active sensing approach to monitor crack development during loading process. In order to characterize damage development, two damage quantitative indexes DI and DT were presented based on received piezoelectric signals from experiment and tensile damage evolution parameter from simulation, respectively. The comparison analysis showed that experiment result DI shows good agreement with simulation result DT and real crack development of specimen. The proposed 3D piezoceramic sensing array can be applied on multidirectional crack monitoring in concrete structure and the numerical‐guided sensor placement strategy is reliable. [ABSTRACT FROM AUTHOR]

Objective. To study energy consumption and QoS optimization coverage mechanism in wireless sensor networks based on swarm sensing algorithm. Methods. The swarm sensor algorithm is reported to optimize the configuration of wireless sensor nodes, improve network service performance, and provide feedback on effective sensor placement. Results. Two WSN coverage optimization schemes were proposed in this essay. The first scheme applied drosophila intelligent algorithm to WSN coverage optimization, and the second scheme integrated particle swarm optimization algorithm and improved firefly algorithm to complement each other. Both schemes can achieve good results in practical application. Scheme one can increase the coverage rate by 6.36% compared with previous schemes, while scheme two can increase it by 6.48%. Both schemes are basically the same, so they can be applied in the optimization of WSN node deployment. Conclusion. As an optimization method, group perception algorithm is inspired by biology. Through the continuous enrichment and development of this algorithm, it has been effectively used in the coverage optimization of the wireless sensor networks. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are a widely studied area in the field of networked embedded computing. They are made up of several sensor nodes, which keep track of a variety of physical and environmental parameters, like temperature and humidity. The nodes are autonomous, self-configuring, and wireless. A significant problem in WSNs is that sensors in these networks consume a lot of energy. Energy consumption is a big issue when it comes to the deployment of sensor networks. The reason for this is the cost of operating a sensor node and the cost incurred due to energy consumption. Energy optimization is based on intelligent energy management. This paper presents a reinforcement learning-based and clustering-enhanced method. Reinforcement learning is a set of algorithms inspired by operant conditioning in animal behavior, and clustering-based methods have been extensively used for devising energy-efficient protocols. The proposed method is able to plan and schedule the nodes to ensure an extended network lifetime. In this work, we aim to assess and increase the efficiency of power consumption and reduce sensor node energy loss. The simulation results prove that the presented protocol effectively reduces the energy consumption of sensor nodes and ensures a prolonged lifetime of the sensor network. [ABSTRACT FROM AUTHOR]

In order to meet the needs of vocal feature recognition, the author proposes a system design based on the Internet of things technology. The principle of the method is that the body sensory layer of the body puts the sound sensors in different positions, records the primary sound signal, and monitors and makes the sound signal using the TMS320VC5402 light red processor. The network transport layer audio signal processes and sends the voice signal database in the process layer. The sound characteristic analysis module in the application layer uses a real-time dynamic change algorithm to obtain the best similarity between the experimental model and the design, identify the characteristics of the sound set red, and identify the content of the voice feature, the music, and the emotional results of the song. Experiments have shown that the system is capable of recognizing speech in a noisy environment, with an accuracy of approximately 95%, resulting in better sound control and the ability to switch switches and remote control. The system, developed with the Internet of things technology, has been proven to improve voice recognition. [ABSTRACT FROM AUTHOR]

In recent years, communication technology has improved exponentially, partly owing to the locations and nature of the deployment of sensor nodes. Wireless sensor networks (WSNs) comprise these sensor nodes and can provide real-time physical and environmental measurements. The sensor nodes have limited power, which reduces their lifespan, especially when placed in human-inaccessible locations. This paper reviews energy-efficient protocols for environmental monitoring applications and energy harvesting-wireless sensor networks. The dynamic deployment and communication challenges associated with environmental monitoring applications (EMAs) make this paper take into account the WSN protocol stack, focusing on the physical layer, network layer (routing), and medium access control (MAC). The paper will delve deeper into the security challenges of deploying sensor nodes for environmental monitoring applications (EMAs). The paper further describes scientific approaches that churn out innovative and engineering applications that must be followed to improve environmental monitoring applications. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth study and analysis of the localization and tracking of multiple targets in soccer training using a distributed intelligent sensor approach. An event-triggered mechanism is used to drive the acoustic array sensors in the distributed acoustic array sensor network, which solves the problem of increased communication load caused by frequent communication of microphones and effectively reduces the communication load between microphones as well as the energy consumption of the acoustic array sensor network. By designing a suitable state estimation equation for the acoustic source target and fully utilizing the measurement and state estimation information of its nodes as well as the state estimation information of neighboring nodes, the next moment state of the acoustic source target can be accurately predicted. A correlation filtering tracking algorithm based on multiscale spatial co-localization is proposed. In the proposed algorithm, the tracker contains a total of several subfilters with different sampling ranges. Then, this paper also proposes a collaborative discrimination method to judge the spatial response of the target image samples of each filter and jointly localize the target online. Based on this, this paper further explores the potential of correlation filter tracking algorithms in complex environments and proposes a robust correlation filter tracking algorithm that fuses multiscale spatial views. The cross-view geometric similarity measure based on multiframe pose information is proposed, and the matching effect is better than that based on single-frame cross-view geometric similarity; to solve the problem of player appearance similarity interference, a graph model-based cross-view appearance similarity measure learning method is further proposed, with players in each view as nodes, player appearance depth features as node attributes, and connections between cross-view players as edges to construct a cross-view player graph. The similarity obtained by the graph convolutional neural network training is better than the appearance similarity calculated based on simple cosine distance. [ABSTRACT FROM AUTHOR]

In human-computer interaction research, prototypes allow for communicating design ideas and conducting early user studies to understand user experience without developing the actual product. For investigating deformation-based interaction, functional prototyping becomes challenging due to the unavailability of commercial platforms and the marginal availability of flexible electronic components. During functional prototyping, incurred time and cost are essential factors that further depend on the ease of stiffness customization, reproduction, and upgrade. To offer these advantages, this work presents the fabrication workflow of Nāmya, a smartphone-sized flexible prototype that can detect bend gestures and touch-based inputs using off-the-shelf sensors and flexible materials. This do-it-yourself (DIY) approach to fabricating deformable prototypes focuses on addressing the challenges of selecting flexible material, type of sensor, and sensor positions. We also demonstrate that the proposed use of a flexible three-dimensional- (3D-) printed internal structure with sensor pockets and the one-part silicone cast allows the development of robust deformable prototypes. This fabrication process offers the opportunity to easily customize device stiffness, reproduce prototypes with similar physical properties, and upgrade existing prototypes. [ABSTRACT FROM AUTHOR]

Recently, research on singing assessment has focused on analyzing singer characteristics rather than building an automated evaluating approach. Hence, there are many methods for evaluating the accuracy of singer's singing performance. Traditional methods mostly use quantitative analysis approach for the accuracy analysis of vocal artistic performance, and the results have obvious ambiguity and correlation. Therefore, this paper proposes a method for evaluating the accuracy of vocal art expression of singers' voices based on a sensor spatial localization algorithm. The adaptive cascade retrieval control approach is used to find and mine the singers' performance based on their voice characteristics. The sensor spatial location algorithm and huge vocal music resources with high precision are utilized to locate the vocal music sources in the mining results. The vocal music of the singers is received through the vocal music preprocessing platform, which sends and controls musical compositions. The adaptive frequency's shift filter and the wavelet transform method are used to filter out any noise in the music. Finally, the accurate evaluation of singer vocal artistic performance is realized from the aspects of music beat detection, note boundary accuracy analysis, and voiced segment accuracy detection. The experimental results show that our proposed method can reduce the error of the sound source's localization and can effectively filter out the noise in the music signal. [ABSTRACT FROM AUTHOR]

Summary: Dynamic components of the control systems involving actuator time delay can strongly influence the dynamics of the controlled floor system. In this paper, an independent modal space design process for active control of floor vibration caused by human‐induced impact excitation is developed based on modal direct velocity feedback control with a phase‐lead compensator using the root locus method to enhance the stability margins and increase the damping of each filtered mode. Spillover effects under residual modes are considered through the placement of the actuator and sensors. Verification experiments using a steel plate specimen are conducted for the proposed procedure, and the test result shows that effective reduction of the vibration level is successfully achieved. [ABSTRACT FROM AUTHOR]

Summary: In this study, the Q‐learning algorithm is integrated into the binary water strider algorithm to adaptively control the search operators and repair strategies. The proposed algorithm (QWSA) is applied to optimal sensor placement in structures with numerous candidate locations. To handle the constraint related to the number of sensors, an efficient strategy based on the triaxial driving‐point residue (DPR3) coefficient is developed, in addition to the random solution repair strategy. The reinforcement learning algorithm with softmax policy determines which strategy should be adopted for the generated solutions. Since the utilized sensors are triaxial, the tridimensional modal assurance criterion (TMAC) is applied as the objective function. To verify the effectiveness of the proposed method, two large‐scale dome‐shaped trusses are investigated, and the results are compared with the basic water strider algorithm and several other well‐known and modern metaheuristics in the literature. The results demonstrate that placements obtained by QWSA have in average lower than half of the cost value obtained by compared methods. Additionally, average DPR3 (DPR3¯) of the QWSA is higher than the rest of the algorithms. The Wilcoxon test indicates that, at a 95% confidence level, the results of QWSA are superior to all algorithms. Additionally, the proposed method outperforms them in terms of convergence speed. [ABSTRACT FROM AUTHOR]

Faulty identification plays a vital role in the area of prognostic and the health management (PHM) of the industrial equipment which offers great support to the maintenance strategy decision. Owing to the complexity of the machine internal component-system structure, the precise prediction of the heavy machine is hard to be obtained, thus full of uncertainty. Moreover, even for a single component, the feature representation of the acquired conditional monitoring signal can be different due to the different deployment of the sensor location and environmental inference, causing difficulty in feature selection and uncertainty in faulty identification. In order to improve the model identification reliability, a novel hybrid machine faulty identification approach based on sparse autoencoder- (SAE-) and deep belief network- (DBN-) based ensemble learning is proposed in this paper. First, six kinds of statistical features are extracted and normalized from multiple sensors monitoring the same target component. Second, the six extracted features are fused by the two-stage SAE proposed in this paper from the sensor dimension and feature dimension, respectively. The composite feature fused in the feature dimension is regarded as the comprehensive representation of the corresponding component. Finally, the fused features containing comprehensive representation of different components are utilized to predict the machine health condition by the ensemble of multiple deep belief classifiers. The effectiveness of the proposed method is validated by the two case studies of wind turbine gearbox and industrial port crane. The experimental result shows that the proposed ensemble learning approach outperforms other traditional deep learning approaches in terms of the prediction accuracy and the prediction stability when dealing with multisensor feature fusion and the precise faulty identification of the industrial heavy machine. [ABSTRACT FROM AUTHOR]

Many real-life problems have different contradicting goals and no simple solution. Therefore, an analysis is made to select the appropriate solution based on the scenario, which is considered the best compromise toward the achievement of a goal. In literature, it is known as complex problem-solving and is a kind of paradigm that has been around since the last century, but the cognition involved in complex problem-solving has purely relied on experts in the field. However, with the evolution of the current stack of technologies such as artificial intelligence and the Internet of Things, it is quite possible to perform the cognition process with the help of machines based on the previously-trained historical data. Our previous work proposed the complex problem-solving as a service for smart cities. In this article, we extend this work and propose a generic architecture for complex problem-solving using task orchestration and predictive optimization in Internet of Things–enabled generic smart space. The proposed framework makes use of historical data for artificial cognition of the complexity of the given problem. For this, predictive optimization is used, which identifies the problem and intelligently predict the solution based on the given constraints. The task orchestration architecture is used to decompose the complex problem into small tasks for real-world deployment into sensors and actuators. The architecture is evaluated against different load conditions and different categories of problems, and the results suggest that the proposed architecture can be used a commonplace for different smart space solutions. [ABSTRACT FROM AUTHOR]

Summary: Unmanned aerial vehicles (UAVs) equipped with onboard sensors empower end‐users to collect data within a wide range of civil engineering applications such as structural condition assessment. While UAVs have been used primarily as mobile sensing platforms to obtain imagery or other data, their mobility can also be used to deploy sensor networks. In this study, the feasibility of using an autonomous UAV to deploy wireless sensors in structural monitoring applications is proposed. While GPS‐based waypoint navigation is available for automating UAV flight operations, this approach does not provide the accuracy necessary for the precision placement of sensor payloads on structures. Computer vision‐based pose estimation is proposed to improve the accuracy of UAV localization for sensor placement. Variably sized fiducial markers integrated into a single pattern are applied to the surface of the structure and adopted as navigation and precision landing targets that identify sensor placement locations. Visual and inertial measurements are fused by means of a discrete Kalman filter to further increase the robustness of the relative position estimation algorithm that is included in the proportional‐integral‐derivative (PID) control law used for UAV landing. Outdoor experiments under realistic operational conditions are conducted to validate the proposed vision‐aided control of the UAV for sensor placement; the UAV is able to land on a predefined landing point within 10 cm. A UAV moving a wireless accelerometer to locations on a beam is used to experimentally show the validity of automating UAV sensor placement for modal analysis using reconfigurable sensor network topologies. [ABSTRACT FROM AUTHOR]

In order to solve the problems of security hidden dangers in the process of node location due to the characteristics of limited resources, open deployment, and being unattended in wireless sensor networks, this paper proposes a mainstream location algorithm combined with the current WSN node. By reducing the error in network positioning, the wireless sensor network positioning technology is put into practical benefits, and the WSN emitter positioning based on node resources and limited capacity is realized. Some positioning technologies are applied to emitter positioning, and some meaningful results are obtained. Aiming at the problems existing in the positioning algorithm of the main nodes in the wireless sensor network, the power consumption and positioning accuracy of the positioning technology are deeply studied to reduce the positioning error. Experiments show that when nodes are sent to different states, the number of nodes 150 remains the same, the communication radius is the same, the environment output is the same, and the number of bones in the network can be changed. After many simulation experiments, both algorithms can see the positioning result curve of the positioning program affected by the anchor node part. [ABSTRACT FROM AUTHOR]

Acquiring precise localization information of sensor nodes is very important in wireless sensor networks. The 3DDV-hop localization algorithm suffers from large localization errors and high energy consumption. In order to improve positioning accuracy and reduce energy consumption, a 3DDV-hop node localization algorithm (3D-HCSSA) based on hop size correction and improved sparrow search optimization is proposed. The algorithm redefines the amendment factor and reduces the cumulative error caused by the hop counts in the traditional algorithm. A maximum distance similar link method based on a similar path search is proposed to find the most similar known node path pair from the target node to the noncoplanar known node link and correct the hop size between multihop counts. The sparrow search algorithm is improved by using the k -means clustering and sine cosine search strategy, which solves the problem that the traditional sparrow algorithm is easy to fall into the local optimum, accelerates the convergence speed, corrects the position deviation of the target node, and improves the positioning accuracy. Experiments demonstrate that the 3D-HCSSA algorithm can improve positioning accuracy and reduce energy consumption. Compared with the 3DDV-hop algorithm, 3D-GAIDV-hop algorithm, and HCLSO-3D algorithm, the 3D-HCSSA positioning accuracy is significantly improved. [ABSTRACT FROM AUTHOR]

Accurate location information is essential for various emerging applications in wireless sensor network (WSN). In order to improve localization accuracy, it is of paramount importance to reduce the effects of noisy distance measurements. This paper proposes an anchor node selection scheme for Received Signal Strength- (RSS-) based localization in WSN. In the proposed approach, the nodes are sorted firstly to select anchor nodes reasonably, and to further reduce the influence of range error, the weight is assigned to each selected anchor node. Finally, an effective modified cuckoo search algorithm is used to compute the coordinates of unknown nodes. Extensive experiments are conducted to study the effects of anchor node ratio, ranging error factor and node density on the localization accuracy performance of the proposed method. The experimental results demonstrate that the proposed method performs better in improving localization accuracy compared with the localization technique without special anchor selection scheme which selects all anchors' information received and the localization technique selecting nearest anchors. [ABSTRACT FROM AUTHOR]

To rich good accuracy in the 2D area for wireless sensor network (WSN) nodes, a localization method has to be selected. The objective of this paper is first to select which localization technique is required (Received Signal Strength Indicator (RSSI)) or (Time of Arrival (ToA)) against anchors placement in a 2D area. Depending on whether the anchor nodes are spaced or not and inspired by the idea of using the RSSI method for small distances and the ToA method for greater distances, we will show which method should be used for the positioning process which mainly guarantees a minimal localization error. Second, a two-dimensional localization scheme for WSN which is called Combined Advantages of ToA-RSSI (CA ToA-RSSI), hereafter, ranging methods, is designed in this work, to make the accuracy better during the positioning process. Results provided through MATLAB simulations show that our new technique improves considerably the positioning accuracy compared with the traditional RSSI and ToA ranging method. The proposed scheme can be run under Line of Sight and (LOS) and Nonline of Sight (NLOS) conditions taking into account a difference in the measurement error. [ABSTRACT FROM AUTHOR]

Sensor arrangement is the primary link of landfill structure health monitoring, and the number of measuring points and the quality of data directly affect the effect of modal identification. Therefore, how to ensure the service safety of landfill structure has become an important research content in the field of landfill structure health monitoring. In this paper, the stress wave propagation principle and stress wave detection theory are analyzed, and an optimal sensor arrangement method based on AGSA (adaptive gravity search algorithm) algorithm is proposed. Use CS (Cuckoo search) algorithm to optimize the objective function value. The corresponding response value is calculated by the finite element model, and the initial proxy model is constructed. By comparing and analyzing the results of model correction, it is found that the error of parameters before and after correction is less than 2.5%. It is further verified that AGSA algorithm can be used to solve the optimal sensor placement problem. In this paper, the use of structural health monitoring technology for health diagnosis and performance evaluation is an important means to ensure structural safety, prolong service life, and reduce maintenance cost. As the primary link of structural health monitoring, sensor system directly determines the accuracy of structural safety diagnosis. [ABSTRACT FROM AUTHOR]

When wireless sensors are randomly deployed in natural environments such as ecological monitoring, military monitoring, and disaster monitoring, the initial position of sensors is generally formed through deployment methods such as air-drop, and then, the second deployment is carried out through the existing optimization methods, but these methods will still lead to serious coverage holes. In order to solve this problem, this paper proposes an algorithm to improve the coverage rate for wireless sensor networks based on an improved metaheuristic algorithm. The sensor deployment coverage model was firstly established, and the sensor network coverage problem was transformed into a high-dimensional multimodal function optimization problem. Secondly, the global searching ability and searching range of the algorithm are enhanced by the reverse expansion of the initial populations. Finally, the firefly principle is introduced to reduce the local binding force of sparrows and avoid the local optimization problem of the population in the search process. The experimental results showed that compared with ALO, GWO, BES, RK, and SSA algorithms, the EFSSA algorithm is better than other algorithms in benchmark function tests, especially in the test of high-dimensional multimodal function. In the tests of different monitoring ranges and number of nodes, the coverage of EFSSA algorithm is higher than other algorithms. The result can tell that EFSSA algorithm can effectively enhance the coverage of sensor deployment. [ABSTRACT FROM AUTHOR]

This paper presents a magnetic matching-aided indoor localization system based on a waist-mounted self-contained sensor array. Our purpose is to localize and track the elderly in nursing homes through the proposed wearable device to ensure their safety. The device consists of a low-cost 9-axis self-contained sensor array, a microcontroller, and a WiFi transmission module. This system uses the step length and heading-based pedestrian dead reckoning (PDR) framework as the backbone to estimate the user's position using the averaged inertial data from the sensor array. A magnetic fingerprint matching (MFM) algorithm is introduced to constrain the drift of the PDR system. Meanwhile, we construct a single-step-based hybrid magnetic fingerprint model to improve the low discernibility of the magnetic field. Finally, we propose an augmented particle filter to fuse the PDR and the MFM algorithms to enhance the system performance further. Experimental results show that 95% of the positioning error after fusion is about 1.47 m, which is 99.3% higher than that of PDR, and the average positioning error after fusion is 0.55 m, which is 61.3% higher than that of PDR. Experimental results have successfully validated the effectiveness and high performance of the proposed magnetic matching-aided wearable indoor localization system. [ABSTRACT FROM AUTHOR]

At present, the global positioning system has been widely used in outdoor positioning and navigation, but in buildings, thick walls made of cement and bricks have a certain blocking effect on satellite signals, resulting in severely weakened GPS signals, and also extremely it may have an impact on the positioning accuracy, so that the positioning task cannot be completed. Inertial sensor is a kind of sensor mainly detecting and measuring acceleration tilt impact vibration rotation and multi-degree-of-freedom motion, is an important part to solve navigation orientation and motion carrier control. Therefore, we need to study the positioning system and positioning method of the people in the building according to the indoor positioning technology. This article mainly uses inertial sensors as indoor positioning technology for related research. This technology mainly includes linked inertial navigation algorithm and pedestrian dead reckoning algorithm. This paper uses pedestrian dead reckoning algorithm to study the positioning system. The research shows that the PDR algorithm designed this time is more accurate for the positioning of pedestrian movement in the scene of the building. Compared with the previous algorithm research, it has increased by 1%-2% accuracy meets the expected accuracy requirements. [ABSTRACT FROM AUTHOR]

Wireless sensor network sensing data is meaningless without any location information, and a large number of anchor node-based localization methods have been proposed to obtain the location information of nodes. However, this kind of method requires a certain number of anchor nodes deployed in the network and complex information interaction between nodes. Therefore, it is not ideal in positioning cost and effect. A WSN positioning method based on the steering antenna of the base station is proposed to address this problem. By rotating the orientation information of the antenna, accurate positioning can be achieved without the assistance of anchor nodes, and no signal interaction between nodes is required. Theoretically, the method is easier to be deployed in the actual scene to realize the positioning of sensor nodes. Experimental results show that the proposed method is better than most positioning methods assisted by anchor node in positioning accuracy, positioning time, and energy consumption. [ABSTRACT FROM AUTHOR]

Wireless sensor networks deploy a large number of wireless sensor nodes in the monitoring area to complete the target monitoring task. The coverage effect is an important index to evaluate wireless sensor networks. The effect of network coverage is realized by the wireless sensor nodes deployed in the monitoring area, because the reasonable deployment of sensor nodes can affect the accuracy, reliability, real time, and integrity of monitoring target data collection. The main purpose of network coverage is to use the least number of wireless sensor nodes to complete the monitoring task of targets in the monitoring area and to avoid the emergence of coverage blind areas. Random node deployment, electronic equipment failure, software error, fire spread, flood, and other phenomena may lead to large-scale failure of wireless sensor nodes. For the failure of some nodes, the phenomenon of coverage blind area appears in the network. In order to repair the coverage blind area in the network, a coverage blind area repair algorithm for wireless sensor networks is proposed in this article. The coverage blind area attracts the nodes, makes the nodes move, and repairs the coverage blind area in the network. [ABSTRACT FROM AUTHOR]

Summary: In this study, a novel sensor fault detection, localization, and reconstruction approach is proposed for online structural identification. The proposed method avoids the requirement of massive training data from the normal operating sensor network and presents a computationally efficient approach to diagnose and estimate the typical sensor faults in a dense sensor network for time‐varying structural systems. First, a two‐level Bayesian model class selection strategy is introduced for sensor fault detection and localization. By evaluating the plausibilities of the model classes in the two‐level strategy, detection and localization of possible faulty sensors can be realized with low computational cost. After detecting and locating the faulty sensors, an online updating algorithm based on a Kalman filter and an extended Kalman filter is then utilized to simultaneously estimate the sensor faults and identify the structural system. Two illustrative examples are presented to validate the efficacy of the proposed method. The results show that the proposed approach offers a reliable and efficient sensor validation methodology for online structural identification. [ABSTRACT FROM AUTHOR]

Summary: Hybrid methods combining physical knowledge and data‐driven techniques have shown great potential for damage prognosis in structural health monitoring (SHM). Current practices consider the physics‐based process and data‐driven measurement equations to describe the damage evolution and the mapping between the damage state and the measurement, respectively. However, the bias between the measurements predicted by the data‐driven equation and the sensor‐based measurements obtained from any SHM system, arising from uncertainties like damage geometries and sensor placement or noise, can lead to inaccurate prognosis results. To improve the prognosis performance in case of bias, this paper adopts a methodology typically applied in sensor fault diagnosis and develops a new hybrid prognostic model with a bias parameter included in the measurement equation and the state vector. Particle filter (PF) serves as the estimation technique to identify the state and parameters relating to the damage as well as the bias parameter, and the remaining useful life (RUL) can be predicted by the physics‐based process equation, with PF posterior estimates of the related parameters and state variables as an input. The experimental study of an aluminum lug structure subject to fatigue crack growth and equipped with a Lamb wave monitoring system demonstrates the improved estimation and prediction performances of the new prognostic model. [ABSTRACT FROM AUTHOR]

Water quality sensor networks can be used for monitoring water environment, early warning and prevention of water pollution through accurate collection of water quality information. Effective deployment of the network can improve its monitoring efficiency. After the uniform deployment of the network, the sensor nodes need to be deployed in the key areas reasonably, so as to save the hardware cost and improve the monitoring effect. In this paper, the water area characteristic model is established to get the key monitoring area. Besides, the Gaussian plume model is applied to obtain the impact range of the key monitoring areas. The experimental results show that Qianhai is the key monitoring area, and its impact range is 10.36 m. On this basis, we deploy the sensors using particle swarm optimisation. Simulation results show that key area can be monitored better, whereas other regions can still guarantee a maximum coverage with a total coverage rate of 79.09%. [ABSTRACT FROM AUTHOR]

In recent decades, although the research on gait recognition of lower limb exoskeleton robot has been widely developed, there are still limitations in rehabilitation training and clinical practice. The emergence of interactive information fusion technology provides a new research idea for the solution of this problem, and it is also the development trend in the future. In order to better explore the issue, this paper summarizes gait recognition based on interactive information fusion of lower limb exoskeleton robots. This review introduces the current research status, methods, and directions for information acquisition, interaction, fusion, and gait recognition of exoskeleton robots. The content involves the research progress of information acquisition methods, sensor placements, target groups, lower limb sports biomechanics, interactive information fusion, and gait recognition model. Finally, the current challenges, possible solutions, and promising prospects are analysed and discussed, which provides a useful reference resource for the study of interactive information fusion and gait recognition of rehabilitation exoskeleton robots. [ABSTRACT FROM AUTHOR]

Since there are many interferences in the indoor environment, it is difficult to achieve the precise positioning of the mobile robot using a single sensor. This paper presents a position estimation and positioning error correction method of mobile robots based on multisensor data. The robot's positioning sensor includes ultra-wideband (UWB) components, inertial measurement unit (IMU), and encoders. UWB multipath interference causes more ranging errors, which can be reduced by the correction equation after data fitting. The real-time coordinates of the UWB robot tag can be calculated based on multiple UWB anchor data and the least squares method. The coordinate data x c , y c are acquired by UWB positioning subsystem, and the velocity data x ̇ c , y ̇ c are collected by IMU together with encoders. The multisensor data continuously update Kalman filter and estimate robot position. In the positioning process, the positioning data of different sensors can be mutually corrected and supplemented. The results of UWB ranging correction experiments indicate that data fitting can improve the UWB positioning accuracy. In the multisensor positioning experiments, compared with a single sensor, the positioning method based on data fusion of UWB, IMU, and encoders has higher accuracy and adaptability. When UWB signals are interfered or invalid, other sensors can still work normally and complete the robot positioning process. The multisensor positioning method not only improves the robot positioning accuracy but also has stronger environmental adaptability. [ABSTRACT FROM AUTHOR]

Large-scale deployment of mobile wireless sensor networks has been widely used in some dangerous and hostile urban security surveillance scenarios. As a new network architecture, software-defined networks was introduced into wireless sensor networks to form a new software-defined wireless sensor networks to solve the problem of balanced large-scale deployment of sensor networks and simplify the complexity of network management. In this article, we first develop an original confident information coverage–based multiplicatively weighted Voronoi diagram through sensor clustering and sensor collaborative sensing. And then, we propose two sensor collaborative redeployment algorithms based on the novel confident information coverage–based multiplicatively weighted Voronoi diagram and software-defined wireless sensor networks architecture to provide high-confidence coverage and improve the coverage ratio. Finally, we demonstrate the superiority of the confident information coverage–based multiplicatively weighted Voronoi diagram and the effectiveness and efficiency of our proposed algorithms via a series of experiments. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we propose a robust sensor placement methodology, which takes into consideration of possible sensor failure encountered in sensory systems. Although the optimal sensor placement problem has been studied for decades, previous works assumed a fully functioned sensory network. In real practice, sensor failure is commonly encountered, and this may seriously degrade the performance of the sensor network. The proposed method considers the impact of sensor failure on the performance of the sensor configurations so that the designed configuration is robust against sensor failure. For this purpose, we define the robust information entropy to quantify the uncertainty of the identified model parameters with consideration of sensor failure. Furthermore, we propose a tailor‐made heuristic sequential search algorithm to enhance the computational efficiency. The proposed methodology is illustrated by designing the robust sensor configuration for a 20‐story shear building and a space truss. Moreover, a case study using the in‐field measurements of the Canton Tower is presented to demonstrate the performance of the proposal methodology. [ABSTRACT FROM AUTHOR]

This paper is aimed at studying underwater object detection and positioning. Objects are detected and positioned through an underwater scene segmentation-based weak object detection algorithm and underwater positioning technology based on the three-dimensional (3D) omnidirectional magnetic induction smart sensor. The proposed weak object detection involves a predesigned U-shaped network- (U-Net-) architectured image segmentation network, which has been improved before application. The key factor of underwater positioning technology based on 3D omnidirectional magnetic induction is the magnetic induction intensity. The results show that the image-enhanced object detection method improves the accuracy of Yellow Croaker, Goldfish, and Mandarin Fish by 3.2%, 1.5%, and 1.6%, respectively. In terms of sensor positioning technology, under the positioning Signal-to-Noise Ratio (SNR) of 15 dB and 20 dB, the curve trends of actual distance and positioning distance are consistent, while SNR = 10 dB , the two curves deviate greatly. The research conclusions read as follows: an underwater scene segmentation-based weak object detection method is proposed for invalid underwater object samples from poor labeling, which can effectively segment the background from underwater objects, remove the negative impact of invalid samples, and improve the precision of weak object detection. The positioning model based on a 3D coil magnetic induction sensor can obtain more accurate positioning coordinates. The effectiveness of 3D omnidirectional magnetic induction coil underwater positioning technology is verified by simulation experiments. [ABSTRACT FROM AUTHOR]