Search

Water pollution is the most important factor affecting the environment. Appropriate monitoring is a big challenge to make sustainable growth by maintaining it for society. In recent times, water monitoring has turned into a smart monitoring system for water pollution (SMS-wp), with the advances on the Internet of things (IoT), machine learning (ML), and the improvement in current sensors. River Ganga is one of the major sources of water for drinking, irrigation, and industries in the northern part of India. Day by day, Ganga River is getting polluted, due to anthropogenic activities, such as the construction of dams, extensive use of fertilizers in agriculture, and untreated discharges from industries. Contamination in the river water is adversely affecting human health and river biota. Therefore, to improve the river ecosystem and to check infections and diseases, water quality assessment is very much important. The main aim of this study is to determine the Water Quality Index (WQI) of the River Ganges at the upper part of the Indo-Gangetic plain, just downstream of the Himalayan foothill using the last 3 years of data (2017–2019). Trend analysis for River Ganga water at considered locations is also a part of this study. Trend analysis is presenting the water quality of river Ganga in the coming years up to 2025. Twelve physicochemical parameters (TDS, chlorides, alkalinity, DO, temperature, COD, BOD, pH, magnesium, hardness, total coliform, and calcium) were analyzed to determine the water quality of River Ganga. As a result, WQI for next 5 years (from 2020 to 2025) is forecasted as an increment of 17.34% at Haridwar, 4.16% at Roorkee, and 21.63% at Dehradun. Results of the study indicated that WQI values just downstream of the Himalayan foothills in the upper reaches of the Gangetic plain are increasing every year. The authors have concentrated on how the advances in sensor innovation, the Internet of things, and machine learning techniques make water pollution monitoring a genuinely brilliant checking framework. Finally, the system of robust strategies for ML, denoising techniques, and advancement of appropriate guidelines for wireless sensor networks (WSNs) have been recommended. [ABSTRACT FROM AUTHOR]

The efficient operation of smart farms relies on the precise monitoring of farm environmental information, necessitating the deployment of a large number of wireless sensors. These sensors must be integrated with their specific locations within the fields to ensure data accuracy. Therefore, efficiently and rapidly determining the positions of sensor nodes presents a significant challenge. To address this issue, this paper proposes a hybrid optimization DV-Hop localization algorithm based on the chaotic crested porcupine optimizer. The algorithm leverages the received signal strength indicator, combined with node hierarchical values, to achieve graded processing of the minimum number of hops. Polynomial fitting methods are employed to reduce the estimation distance error from the beacon nodes to unknown nodes. Finally, the chaotic optimization crested porcupine optimizer is designed for intelligent optimization. Simulation experiments verify the proposed algorithm's localization performance across different monitoring areas, varying beacon node ratios, and assorted communication radii. The simulation results demonstrate that the proposed algorithm effectively enhances node localization accuracy and significantly reduces localization errors compared to the results for other algorithms. In future work, we plan to consider the impact of algorithm complexity on the lifespan of wireless sensor networks and to further evaluate the algorithm in a pH monitoring system for farmland. [ABSTRACT FROM AUTHOR]

When wireless sensor networks are used in smart homes, the connection state will be unstable due to signal masking attenuation. This will cause low packet rate, high time delay and high cost in the network. In this paper, a network routing algorithm for wireless sensing based on connection conditions is designed. Secondly, the expected number of sends is proposed to evaluate the stability of links. Based on this, the following network signal delivery situation is forecasted in real time and quickly. According to the estimated expected number of transmissions, the path is dynamically corrected to effectively avoid attenuation in the channel and achieve optimal system performance. Experimental results show that the method proposed in this paper can improve the efficiency of message sending and reduce the routing cost under the condition of masking effect. [ABSTRACT FROM AUTHOR]

The reliability and scalability of Linear Wireless Sensor Networks (LWSNs) are limited by the high packet loss probabilities (PLP) experienced by the packets generated at nodes far from the sink node. This is an important limitation in Smart City applications, where timely data collection is critical for decision making. Unfortunately, previous works have not addressed this problem and have only focused on improving the network's overall performance. In this work, we propose a Distance-Based Queuing (DBQ) scheme that can be incorporated into MAC protocols for LWSNs to improve reliability and scalability without requiring extra local processing or additional signaling at the nodes. The DBQ scheme prioritizes the transmission of relay packets based on their hop distance to the sink node, ensuring that all packets experience the same PLP. To evaluate the effectiveness of our proposal, we developed an analytical model and conducted extensive discrete-event simulations. Our numerical results demonstrate that the DBQ scheme significantly improves the reliability and scalability of the network by achieving the same average PLP and throughput for all nodes, regardless of traffic intensities and network sizes. [ABSTRACT FROM AUTHOR]

Copyright of Informatics Control Measurement in Economy & Environment Protection / Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska is the property of Lublin University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Railway bridges are crucial for transferring passengers and commodities in the transportation network. Railway bridges require continuous monitoring to observe their performance. A structural health monitoring system is one method for assessing the viability of a railway bridge structure. The functioning of railroad bridge structures has been extensively observed using wireless technology. This research aims to implement smart wireless sensors for monitoring the structural health of the railway bridge online in real-time during operation. Many sensors were installed on the railway bridge, including strain gauges, accelerometers, linear variable displacement transducers, and proximity sensors. Geometric modeling and numerical simulation were performed to find critical frame locations on the railway bridge where the instrumentation sensors would be placed. In this study, MONITA® is employed for data acquisition modules. The MONITA® system consists of a combination of hardware and software that functions to retrieve, send, store, and process data. This paper describes the result of establishing this method to comprehend the performance of the steel railway bridge structure in real-time via the human-machine interface display dashboard. As a result, the monitoring system can appropriately assess a structural railway bridge in real time. This study may be helpful to practicing engineers and researchers in future studies of steel railway bridge evaluation. This could be a valuable reference for future studies in implementing such systems as the railway bridge early warning system technique in detecting bridge damage. [ABSTRACT FROM AUTHOR]

Analog-to-feature (A2F) conversion based on non-uniform wavelet sampling (NUWS) has demonstrated the ability to reduce energy consumption in wireless sensors while employed for electrocardiogram (ECG) anomaly detection. The technique involves extracting only relevant features for a given task directly from analog signals and conducting classification in the digital domain. Building on this approach, we extended the application of the proposed generic A2F converter to address a human activity recognition (HAR) task. The performed simulations include the training and evaluation of neural network (NN) classifiers built for each application. The corresponding results enabled the definition of valuable features and the hardware specifications for the ongoing complete circuit design. One of the principal elements constituting the developed converter, the integrator brought from the state-of-the-art design, was modified and simulated at the circuit level to meet our requirements. The revised value of its power consumption served to estimate the energy spent by the communication chain with the A2F converter. It consumes at least 20 and 5 times less than the chain employing the Nyquist approach in arrhythmia detection and HAR tasks, respectively. This fact highlights the potential of A2F conversion with NUWS in achieving flexible and energy-efficient sensor systems for diverse applications. [ABSTRACT FROM AUTHOR]

Wireless sensor-based smart home systems are increasingly gaining prevalence, offering new opportunities for automation and an improved quality of life. However, the seamless integration of multi-layer wireless sensor networks remains a significant challenge. This article delves into a comprehensive approach to integrate wireless networks that employ distinct protocols, such as Zigbee, Z-Wave, Wi-Fi, Bluetooth, and LoRa, within the context of Home Assistant-based smart homes. The article explores architectural solutions and data transformation methods, and presents the outcomes of performance experiments, as well as the stability of multi-layered wireless sensor networks. These findings culminate in the identification of an optimal configuration. The formulation of this research problem sets the stage for novel perspectives in the development of efficient and adaptive smart homes. [ABSTRACT FROM AUTHOR]

The Aquasense project is a multi-site Innovative Training Network (ITN) in water and food quality monitoring using autonomous sensors and intelligent data gathering and analysis. This paper aims to study the most suitable communication system model regarding radio signal coverage and networking performance in this frame. This objective is achieved by evaluating the expected performance of three long-range communications technologies, 4G/LTE, NB-IoT, and LoRa, to support a wireless sensor network reporting data from the sensing devices to a remote central station where they are collected and analyzed. In order for the performance to be assessed, the WinProp software suite is adopted to reproduce the communications scenarios and estimate the network throughput. The results clearly show that LTE outperforms the other two technologies as it achieves the highest throughput but is limited in terms of coverage. On the other hand, the LoRa and NB-IoT technologies achieve outstanding connectivity for large areas far away from the base stations, making the NB-IoT the solution offering the most valuable trade-of between connectivity and throughput. [ABSTRACT FROM AUTHOR]

The growing cognizance of spectrum scarcity has become a more significant concern in wireless radio communications. Due to the exponential growth of data transmission in intelligent wireless sensor networks, energy spectrum detection has become a promising solution for resolving spectrum shortages. Primary user emulation attack (PUEA) has been identified as a significant attack vector in the cognitive radio (CR) domain's physical layer. In comparison, the CR is a promising method to increase spectrum efficiency by allowing unlicensed secondary users (SUs) to access licensed frequency bands without interfering with approved primary users (PUs). The study's primary findings are the methodology for preventing PUEA using authentication tags, which are unique sequences. This research blends SC‐FDMA with CR to protect CR networks from PUEA attacks, a Latin square (LS) matrix tag generation system is proposed to mitigate the PUEA effect. The technology is meant to provide effective authentication and protection against malicious users. In a secured environment, the LS tag technique is utilized to track and estimate the PU. For example, the BER of both techniques is virtually identical between 0 and 4 dB, while the BER performance of the suggested LS tag generation improves with increasing signal‐to‐noise ratio (SNR). As a result, the suggested LS tag generation is less susceptible to PUEA. To diminish the influence of PUEA in CR networks, an efficient enlightening approach for making the future Green Cognitive Radio Wireless networks structure is proposed. The simulation results also demonstrate the resilience of the proposed CR spectrum sensing techniques for energy‐efficient knowledge at varying degrees to reduce the adverse effects of environmental technologies. [ABSTRACT FROM AUTHOR]

The underwater acoustic sensor network (UASN) plays a crucial role in collecting real-time data from remote areas of the ocean. However, the deployment of UASN poses significant challenges due to the demanding environmental conditions and the considerable expenses associated with its implementation. Therefore, it is essential to design an appropriate routing protocol to effectively address the issues of packet delivery delay, routing void, and energy consumption. In this paper, an adaptive support vector machine (SVM)-based routing (ASVMR) protocol is proposed for the UASN to minimize end-to-end delay and prolong the network lifetime. The proposed protocol employs a distributed routing approach that dynamically optimizes the routing path in real time by considering four types of node state information. Moreover, the ASVMR protocol establishes a "routing vector" spanning from the current node to the sink node and selects a suitable pipe radius according to the packet delivery ratio (PDR). In addition, the ASVMR protocol incorporates future states of sensor nodes into the decision-making process, along with the adoption of a waiting time mechanism and routing void recovery mechanism. Extensive simulation results demonstrate that the proposed ASVMR protocol performs well in terms of the PDR, the hop count, the end-to-end delay, and the energy efficiency in dynamic underwater environments. [ABSTRACT FROM AUTHOR]

The launching of 5G technology provides excellent opportunity for the prosperous development of Internet of Things (IoT) devices and intelligent wireless sensor nodes. However, deploying of tremendous wireless sensor nodes network presents a great challenge to sustainable power supply and self‐powered active sensing. Triboelectric nanogenerator (TENG) has shown great capability for powering wireless sensors and work as self‐powered sensors since its discovery in 2012. Nevertheless, its inherent property of large internal impedance and pulsed "high‐voltage and low‐current" output characteristic seriously limit its direct application as stable power supply. Herein, a generic triboelectric sensor module (TSM) is developed toward managing the high output of TENG into signals that can be directly utilized by commercial electronics. Finally, an IoT‐based smart switching system is realized by integrating the TSM with a typical vertical contact–separation mode TENG and microcontroller, which is able to monitor the real‐time appliance status and location information. Such design of a universal energy solution for triboelectric sensors is applicable for managing and normalizing the wide output range generated from various working modes of TENGs and suitable for facile integration with IoT platform, representing a significant step toward scaling up TENG applications in future smart sensing. [ABSTRACT FROM AUTHOR]

This paper describes the design and implementation of a wireless control area network (CAN bus) protocol for communication between the smart NOx (nitrogen oxide) sensor on diesel engines and the engine control unit (ECU). In this research, the approach taken is based on a case study of Wärtsilä's smart NOx sensor on a W4L20 diesel engine with the objective of replacing the wired CAN protocol with a wireless CAN communication node. In the current setup, the smart NOx sensor is connected to the engine control unit (ECU) with a wired CAN bus connection. The XBee module, which uses the ZigBee (IEEE 802.15.4) technology was used in the design and implementation of the wireless CAN prototype. With the emergence of 5G networks and the era of IoT, the topic of wireless industrial automation becomes essential in the modern industry. In addition to the great advantages and opportunities that the use of wireless nodes has in automation systems, there are many real challenges. The practical design challenges have been addressed in this paper. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet and computers, large-scale network applications have become a research hotspot in the communication industry, engineering and other fields. Building a new wireless sensor network and intelligent communication transmission system can promote the further upgrading and improvement of the capability industry, which is also a subject that the current network academia and communication academia are trying to explore. Based on the above background environment, this paper proposes the research on energy prediction and intelligent routing algorithm of wireless sensor networks in the development of sixth generation (6G) networks. A node coverage and scheduling algorithm based on energy prediction is proposed for the overlap and non-uniformity of regions in network transmission. Secondly, the problem of energy efficient utilization in wireless sensor networks is solved, and the clustering algorithm is used to predict the use process. The working state model of network sensor is established, and the energy parameters are added to the actual prediction calculation as influencing factors. Finally, in the intelligent routing mechanism, clustering mechanism and particle swarm optimization algorithm are used to improve the path selection, so as to improve the fault tolerance of wireless network transmission in the face of large amounts of dynamic data. The research results show that the optimization and intelligent routing algorithm based on energy prediction for wireless sensor networks can prolong the service life cycle and ensure the network transmission efficiency and coverage in a long work cycle. It can ensure the energy supply of the sensor and prolong the service time through the balanced node scheduling. [ABSTRACT FROM AUTHOR]

The cluster technique involves the creation of clusters and the selection of a cluster head (CH), which connects sensor nodes, known as cluster members (CM), to the CH. The CH receives data from the CM and collects data from sensor nodes, removing unnecessary data to conserve energy. It compresses the data and transmits them to base stations through multi-hop to reduce network load. Since CMs only communicate with their CH and have a limited range, they avoid redundant information. However, the CH's routing, compression, and aggregation functions consume power quickly compared to other protocols, like TPGF, LQEAR, MPRM, and P-LQCLR. To address energy usage in wireless sensor networks (WSNs), heterogeneous high-power nodes (HPN) are used to balance energy consumption. CHs close to the base station require effective algorithms for improvement. The cluster-based glow-worm optimization technique utilizes random clustering, distributed cluster leader selection, and link-based routing. The cluster head routes data to the next group leader, balancing energy utilization in the WSN. This algorithm reduces energy consumption through multi-hop communication, cluster construction, and cluster head election. The glow-worm optimization technique allows for faster convergence and improved multi-parameter selection. By combining these methods, a new routing scheme is proposed to extend the network's lifetime and balance energy in various environments. However, the proposed model consumes more energy than TPGF, and other protocols for packets with 0 or 1 retransmission count in a 260-node network. This is mainly due to the short INFO packets during the neighbor discovery period and the increased hop count of the proposed derived pathways. Herein, simulations are conducted to evaluate the technique's throughput and energy efficiency. [ABSTRACT FROM AUTHOR]

Introduction: The echo states that networks in the Internet of Things (IoT) are currently being implemented in the widest sense. Echo state networks are fast and efficient recurrent neural networks. This consists of an input layer, a reservoir with many sparsely connected neurons, and an output layer. Issues: In the existing wireless sensor networks, strong mobility may disrupt an existing link between two communicating nodes. There is an inconvenience in data communication, and then it searches for a new node to build a better connection. Methods: To overcome these issues, the recently introduced echo state network (ESN) model opened the way to an extremely efficient approach for designing neural networks for temporal data. The study focuses on the ESN-enabled Intelligent Smart Sensor Design (IS2D) for creating the robotic nervous system with a smart healthcare Digital Nervous System (DNS) using the techniques of IoT, DNS, and Smart Sensor Design and Strain Sensor Fabrication (SSF). Results: Experimental results demonstrate the training set testing against the IS2D, the confusion matrix for ESN outcome, the real-time healthcare monitoring for the DNS, the IS2D sensor accuracy, and the DNS intensity calculation. Discussion: The performance analysis of the proposed model in realistic environments attests to the benefits of energy-centric metrics such as energy consumption, network lifetime, delay, and throughput. Finally, we discuss the challenges and opportunities by summarizing the study and proposing possible future works. The training set testing against the IS2D is based on time count, and the voltage result is estimated. The first portion of the data set should be 11.46% at the initial level. Further, this will increase from 1% to 5%, from 6% to 10%, and from 16% to 28% at the consecutive data set. The confusion matrix for ESN outcome is based on accuracy 28.45% higher than the existing strategies. In this part, the initial accuracy is 8.45% while accessing the initial stage. This value should increase with consecutive data sets from 18.45% to 28.45%. [ABSTRACT FROM AUTHOR]

This paper intends to apply the Intelligent Wireless Sensor Network (IWSN) to the archaeological excavation site and make the dynamic site of archaeological excavation can be monitored intelligently in real-time. This paper firstly discusses the structure and function of Wireless Sensor Network (WSN). Secondly, according to the problem of limited dynamic monitoring of WSN, an IWSN is established. The dynamic environmental monitoring of archaeological excavation sites is achieved by using Geographic Information System (GIS) and remote sensing technology combined with IWSN. Finally, this paper analyses the environmental monitoring results of the archaeological excavation site, the dynamic environmental monitoring location of the archaeological excavation site of the IWSN, and the simulation results of the Intelligent Wireless Sensor Network-Geographic Information System (IWSN-GIS) in the dynamic environmental monitoring of the archaeological excavation site. The results show that the Inverse Distance to a Power (IDP) algorithm in temperature and humidity monitoring data on the overall downward trend, the algorithm performance is relatively stable, and the algorithm performance is better than the other two algorithms. The monitoring performance of IWSN-GIS system is better than that of IWSN system. It shows that with the increase in the communication distance of sensor nodes, the relative error of different monitoring systems for the dynamic monitoring position of the archaeological excavation site is decreasing. IWSN-GIS can monitor the environment before, during, and after the archaeological site. This paper realizes the intelligent, scientific, and technological archaeology of archaeological excavation through IWSN, GIS, and remote sensing image technology, and provides technical support for digital archaeology in archaeological research. [ABSTRACT FROM AUTHOR]

A smart city has a complex hierarchical communication system with various components. It must meet the requirements of fast connection, reliability, and security without data compromise. Internet of Things technology is widely used to provide connectivity and control solutions for smart sensors and other devices using heterogeneous networking technologies. In this paper, we propose a routing solution for Wireless Sensor Networks (WSN) and Mobile Ad hoc NETworks (MANET) with increasing speed, reliability, and sufficient security. Many routing protocols have been proposed for WSNs and MANETs. We combine the Secret Sharing Schemes (SSS) and Redundant Residual Number Systems (RRNS) to provide an efficient mechanism for a Distributed dynamic heterogeneous network Transmission (DT) with new security and reliability routing protocol (DT-RRNS). We analyze the concept of data transmission based on RRNS that divides data into smaller encoded shares and transmits them in parallel, protecting them from attacks on routes by adaptive multipath secured transmission and providing self-correcting properties that improve the reliability and fault tolerance of the entire system. [ABSTRACT FROM AUTHOR]

The current autonomous driving vehicles cannot collaborate with each other due to the disconnection of sensors between vehicles, which leads to poor performance in traffic efficiency and safety. Recently, with the development of wireless intelligent sensors, vehicular sensor network (VSN) is emerging to provide information sharing between vehicles, so that cooperative autonomous driving (CAD) can be enabled. This paper especially investigates the VSN-assisted straight-going CAD at urban intersections under the rule of traffic signals. First, the function of VSN is presented, and the information sharing mechanism is designed. Then distributed stop-and-go strategies with maintaining the optimal safety spacing between adjacent vehicles are proposed for multiple traffic cases. With the proposed strategies, every vehicle can get an exact time to decelerate or accelerate at signalized intersections. Finally, we use the model predictive control (MPC) scheme to actuate our strategies, and it is demonstrated that vehicles perform well in both passage safety and efficiency as expected. [ABSTRACT FROM AUTHOR]

The stationary hierarchical network faces considerable challenges from hotspots and faster network breakdowns, especially in smart monitoring applications. As a solution to this issue, mobile sinks were recommended since they are associated with huge and balanced ways to transfer data and energy across the network. Again, due to the mobile sink node advertisement around the network latency and the energy utilization overheads introduced across the network, ring routing reduces the control overhead while preserving the benefits of the mobile sink, thereby optimizing the energy and improving the network life span. Consequently, we suggested a novel, distributed advanced ring routing strategy, in this work, for the mobile wireless sensor network. Extensive simulations and performance evaluation, in comparison to previous distributed mobile approaches, reveal a 37% and 40% boost in the network throughput and end-to end delay, respectively. Additionally, the lifespan of a network is determined by the control overhead and energy demand. [ABSTRACT FROM AUTHOR]

For the monitoring and processing of network data, wireless systems are widely used in many industrial applications. With the assistance of wireless sensor networks (WSNs) and the Internet of Things (IoT), smart grids are being explored in many distributed communication systems. They collect data from the surrounding environment and transmit it with the support of a multi-hop system. However, there is still a significant research gap in energy management for IoT devices and smart sensors. Many solutions have been proposed by researchers to cope with efficient routing schemes in smart grid applications. But, reducing energy holes and offering intelligent decisions for forwarding data are remain major problems. Moreover, the management of network traffic on grid nodes while balancing the communication overhead on the routing paths is an also demanding challenge. In this research work, we propose a secure edge-based energy management protocol for a smart grid environment with the support of multi-route management. It strengthens the ability to predict the data forwarding process and improves the management of IoT devices by utilizing a technique of correlation analysis. Moreover, the proposed protocol increases the system's reliability and achieves security goals by employing lightweight authentication with sink coordination. To demonstrate the superiority of our proposed protocol over the chosen existing work, extensive experiments were performed on various network parameters. [ABSTRACT FROM AUTHOR]

A close correlation between CO2 concentration and aerosol enables the wide utilization of CO2 concentration as a good representation of Severe Acute Respiratory Syndrome-Coronavirus-2 infection airborne transmission. On the other side, many indoor air-quality monitoring devices have been developed for indoor monitoring applications. However, most of them are multiparameter air-quality sensor systems and tend to consume relatively high power, are relatively large devices, and are fairly expensive; therefore, they not meet the requirement for indoor monitoring applications. This paper presents a smart wireless sensor node that can measure and monitor CO2 concentration levels. The node was designed to meet the requirements of indoor air-quality monitoring applications by considering several factors, such as compact size, low cost, and low power, as well as providing real-time, continuous, reliable, and remote measurement. Furthermore, the commercial off-the-shelf and low-power consumption components are chosen to fit with the low-cost development and reduce energy consumption. Moreover, a low-power algorithm and cloud-based data logger also were applied to minimize the total power consumption. This power strategy was applied as a preliminary development toward an autonomous sensor node. The node has a compact size and consumes low energy for one cycle of CO2 measurement, accompanied by high accuracy with very low measurement error. The experiment result revealed the node could measure and monitor in real-time continuous, reliable, and remote CO2 concentration levels in indoor and outdoor environments. A user interface visualizes CO2 concentration graphically and numerically using the Adafruit platform for easy accessibility over the Internet of Things. The developed node is very promising and suitable for indoor CO2 monitoring applications with the acquired data that could be utilized as an indicator to minimize the risk of indoor Severe Acute Respiratory Syndrome-Coronavirus-2 airborne transmission. [ABSTRACT FROM AUTHOR]

With the rapid growth of railways in China, the focus has changed to the maintenance of large-scale rail structures. Multi-agent systems (MASs) based on wireless sensor network (WSNs) with soft multi-functional sensors (SMFS) are adopted cooperatively for the structural health monitoring of large-scale rail structures. An MAS framework with three layers, namely the sensing data acquisition layer, sensor data processing layer, and application layer, is built here for collaborative data collection and processing for a rail structure. WSN nodes with strain, temperature, and piezoelectric sensor units are developed for the continuous structural health monitoring of the rail structure. The feature data at different levels are extracted for the online monitoring of the rail structure. Experiments carried out at the Rail Transmit Base at East China Jiaotong University verify that the WSN nodes with SMFS are successfully assembled onto a 100-m-long track for damage detection. Based on the sensing data and feature data, a neural network data fusion agent (DFA) is applied to calculate the damage index value of the track for comprehensive decisions regarding rail damage. The use of WSNs with multi-functional sensors and intelligent algorithms is recommended for cooperative structural health monitoring in railways. [ABSTRACT FROM AUTHOR]

The traditional way of museum exhibition is physical exhibition, which is essentially restricted by the venue, time, space, and display conditions and is no longer applicable to the display form of modern museum exhibits. Therefore, with the development of a large number of modern technologies such as information technology, wireless sensor technology and image processing technology, the way of museum cultural relics display also tends to be more intelligent and intelligent. Based on this, this paper will set up corresponding gateway nodes, routing nodes, and corresponding terminal nodes for each node of the corresponding museum based on the intelligent wireless sensor network technology; transmit the audio impact information of the corresponding museum exhibits through the terminal nodes; and automatically send the node short address information of the corresponding information to the corresponding analysis end in real time for analysis and processing, and then based on the information characteristics of the corresponding exhibits, at the same time, combined with virtual fusion and human-computer interaction technology, a set of augmented reality application system for tourist mobile terminal is developed. Abandoning the disadvantages of traditional museum display methods, this paper creatively designs the corresponding museum heritage intelligent display workflow based on intelligent wireless sensor network through C/S architecture and finally realizes the enhancement effect of virtual reality on tourism terminals. In order to verify the superiority of the intelligent display mode of museum cultural relics based on intelligent wireless sensor network proposed in this paper, this paper compares it with the traditional display mode. The experimental results show that this paper has obvious advantages in the display comprehensiveness, visitor satisfaction, and display effectiveness, which further improves the effect of museum cultural relics display. [ABSTRACT FROM AUTHOR]

In today's rapidly developing information technology, computers are becoming faster and more accurate with more and more sensors, and the birth of new technologies such as cloud computing, big data, the Internet of Things, and artificial intelligence has turned the whole world upside down. At the same time, there is a growing interest in more natural and harmonious ways of human-machine interaction, and many people are devoting themselves to research in this area. Its nodes can be distributed arbitrarily in the target environment and can obtain relevant information about the surrounding environment. In a multinode wireless sensor network, the nodes organize themselves with each other through special protocols to realize their respective functions. In this paper, the application of a multisubject collaboration model to sports training is analyzed, and its superiority is verified by means of examples. [ABSTRACT FROM AUTHOR]

In recent years, different types of monitoring systems have been designed for various applications, in order to turn the urban environments into smart cities. Most of these systems consist of wireless sensor networks (WSN)s, and the designing of these systems has faced many problems. The first and most important problem is sensor node deployment. The main function of WSNs is to gather the required information, process it, and send it to remote places. A large number of sensor nodes were deployed in the monitored area, so finding the best deployment algorithm that achieves maximum coverage and connectivity with the minimum number of sensor nodes is the significant point of the research. This paper provides a systematic mapping study that includes the latest recent studies, which are focused on solving the deployment problem using optimization algorithms, especially heuristic and meta-heuristic algorithms in the period (2015–2022). It was found that 35% of these studies updated the swarm optimization algorithms to solve the deployment problem. This paper will be helpful for the practitioners and researchers, in order to work out new algorithms and seek objectives for the sensor deployment. A comparison table is provided, and the basic concepts of a smart city and WSNs are presented. Finally, an overview of the challenges and open issues are illustrated. [ABSTRACT FROM AUTHOR]

In this paper, an in-depth study and analysis of the ecological economy of fine agriculture are carried out using image detection methods of smart sensor networks. The analog signal output from the wireless sensor network is filtered and thresholder to convert into a digital signal to complete the sensor monitoring data preprocessing for digital information analysis. In this paper, with the objectives of good environmental adaptability, low power consumption, low cost, and standardization, the key technologies of wireless sensor networks for fine agriculture are studied, including network structure, networking method, node positioning method, data fusion method, rapid energy self-sufficiency, and energy-saving strategy, and the performance evaluation method of wireless sensor network system, IoT-oriented middleware design method, generic node software and hardware design method, and typical application system. Firstly, a convolutional layer is used instead of a fully connected layer, which makes the network more flexible in terms of input image requirements and enables the calculation of the target rice region. Not only will many complex operations be generated, but it will also limit the generalization ability of the model. Then, by introducing a flexible connection layer based on unit and optimizing the loss function of the network, a crop convolutional neural network (Crop-Net) is finally proposed for training and testing rice images at different growth stages to improve the detection accuracy. In this paper, a network quality of service goal-driven evaluation strategy and evaluation method for agricultural wireless sensor network systems is designed to provide a reference for the establishment of industry standards for wireless sensor network systems for fine agriculture. [ABSTRACT FROM AUTHOR]

In this paper, a standard ray tracing model based on Geometrical Optics (GO) is proposed for predicting the signal strength of Wireless Sensor Network (WSN), ZigBee nodes, in an indoor environment. The signal strength is calculated analytically. The results are compared with numerical analysis implemented in FEKO computational electromagnetic software, and agreement is demonstrated. Also, the model is verified by a simple measurement campaign in a straight corridor section of commercial building, and results agreement is obtained. The results show that the proposed technique is capable of predicting the signal strength of WSN sensors in a corridor section of indoor environment with good accuracy, fast calculation time, and low computational resources and complexity. The proposed analytical model and measurement dataset can help WSN designers select the best locations of ZigBee nodes in a straight corridor section with good signal quality. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, non‐linear distance information characterized by uncertainty and randomness creates the deviation and significantly target nodes position is affected. Non‐linearity effect among distance and received signal strength (RSS) of target nodes is minimized by modelling edge weights using knowledge base in fuzzy logic system. Further, optimum membership functions bases' of RSS and edge weights using application of butterfly optimization algorithm are evolved to minimize the node position error. Here, a single anchor node is used to estimate target nodes 3D coordinates in anisotropic environment using range free localization methods. The anchor node has been deployed at top layer and over beneath layers target nodes are distributed equally. In This paper, simulation results of the proposed method attain substantial performance improvement in target node 3D position accuracy than the earlier proposed range‐free methods. Proposed technique is useful for mapping of several instances like, fire hazards in forests, tracking of workers at different installation sites, solar plant tracking in smart cities and so forth. [ABSTRACT FROM AUTHOR]

Intelligent transportation infrastructure has gained significant research attention recently. In this paper, an innovative sensor network of smart road stud (SRS) is developed to enhance traffic detection infrastructure characterized by its functionality in traffic data collection, long/short range wireless data transmission, self-sustained power supply, and remote custom controlled lighting-based traffic guidance. Compared to the traditional traffic detectors and road studs, SRS nodes are installed on lane lines instead of lane center to enable the additional applications besides the detection function, such as traffic monitoring, congestion warning, routing guidance, and so on. SRS detects vehicles based on three-axis geomagnetic sensors. A vehicle detection algorithm is proposed correspondingly under different operation scenarios to count vehicles in two adjacent lanes. Its detecting accuracy can be further improved by a sensor network of multiple SRSs working cooperatively. Field test results show that the vehicle detection accuracy based on the SRS network is about 98% per lane, which is the same level as the commercial detector installed in center of lane, even under the non-standard driving behaviors such as crossing lane line. The high performance, value-added service, and low cost enable wide-range applications of SRS networks as part of intelligent traffic detection infrastructure. [ABSTRACT FROM AUTHOR]

Adolescents' emotional changes will have a huge impact on themselves; perhaps, they do not understand themselves. However, according to research, many behaviors of adolescents are often accompanied by emotional changes, and the occurrence of these changes will also bring about their unconsciousness. This article first introduces the research background, significance, and development status of smart home sensors and young people's emotions at home and abroad. This article then gives a detailed introduction to the Python language, intelligent sensor networks, and real-time analysis of youth emotions. In the introduction, it mainly explains the design of the intelligent sensor network system and introduces the system architecture and software and hardware design of the wireless sensor network in detail. In the hardware part, it mainly gives a brief overview of information collection, data transmission, and data processing. In the software part, the embedded software design of three types of network nodes and the control center software design based on Python are given. Finally, the neural network algorithm is used to realize the real-time analysis of young people's emotions, and the recognition rate of multiple algorithms and the data situation of multiple emotional factors are tested at the same time. The results show that the highest recognition rate of 58.4% can be achieved on the validation set of the HAPPEI database after preprocessing, which is higher than the recognition result obtained by directly training the network using the training set of the HAPPEI database. [ABSTRACT FROM AUTHOR]

Modern home furnishings have solved the most basic housing problems, but how to make homes more informatized and modern has become the focus of people's growing concern. With the rapid development of information technology, improving the intelligent level of family life and modern lifestyle is bound to be the trend of future development. This paper studies a smart home control system based on wireless sensor network positioning, which can perceive the home environment through the sensor module, and the control module can automatically control common electrical appliances to achieve real-time data monitoring and alarm functions. Specifically, it includes a positioning module, a communication module, and a server: the positioning module is connected to the server through the communication module, and the positioning module is used to obtain user location information in real time and report user location information to the server at intervals. The server receives the user reported at intervals through the communication module. Location information and remotely control smart home devices based on user location information. The research results show that the method proposed in this paper can separately monitor each part of the home and send it to the home appliance control module through the server to control the home appliance, optimize the living environment, and realize the remote control and monitoring of the smart home. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) is considered to have a wide application prospect in a smart distribution network due to its unique low power consumption, fast self-organization, and superior coordination. In order to study the communication MWSN data transmission mechanism based on a wireless sensor network, the difficulty of applying a wireless sensor network to an intelligent distribution network communication is to meet the real-time and reliability requirements of power industry distribution network data communication specifications under the limited network resources. Firstly, the transmission probability is calculated by considering the path loss, the speed of the node, the direction of the node, and the residual energy of the node. In addition, the transmission and replacement order of the message is determined according to the survival time of the data message. Simulation experiments are carried out on MATLAB. The result shows the proposed routing algorithm, compared with the DT algorithm, flooding algorithm, and FAD algorithm, the delivery rate of data messages is increased by at least 6%, and the number of copies is reduced by at least 10% compared with the flooding algorithm and the FAD algorithm. On the basis of satisfying the mobility of sink nodes, the algorithm proposed in this paper can effectively save network energy consumption and prolong the network lifetime. [ABSTRACT FROM AUTHOR]

In the past two decades, technological advancements in smart devices, IoT, and smart sensors have paved the way towards numerous implementations of indoor location systems. Indoor location has many important applications in numerous fields, including structural engineering, behavioral studies, health monitoring, etc. However, with the recent COVID-19 pandemic, indoor location systems have gained considerable attention for detecting violations in physical distancing requirements and monitoring restrictions on occupant capacity. However, existing systems that rely on wearable devices, cameras, or sound signal analysis are intrusive and often violate privacy. In this research, we propose a new framework for indoor location. We present an innovative, non-intrusive implementation of indoor location based on wireless sensor networks. Further, we introduce a new protocol for querying and performing computations in wireless sensor networks (WSNs) that preserves sensor network anonymity and obfuscates computation by using onion routing. We also consider the single point of failure (SPOF) of sink nodes in WSNs and substitute them with a blockchain-based application through smart contracts. Our set of smart contracts is able to build the onion data structure and store the results of computation. Finally, a role-based access control contract is used to secure access to the system. [ABSTRACT FROM AUTHOR]

With the large amount of carbon emissions from many enterprises over the years, the global greenhouse effect is getting worse. In order to protect the climate and environment on which we depend, the world is taking active measures to reduce carbon emissions as much as possible. At present, the domestic carbon auditing and verification system is relatively late and immature, and the traditional system efficiency is relatively low. Wireless sensor network (WSN) is a particularly flexible distributed wireless communication network, which can coordinate the perception, processing, transmission, and collection of data of objects perceived within the network until the data content is transmitted to the user terminal. This paper aims to study and design an intelligent network model based on WSN so as to calculate and verify the carbon emissions of enterprises and countries. In this paper, an intelligent WSN model based on ZigBee technology is proposed to build an efficient, low-consumption, and high-precision carbon audit and verification system framework. In this paper, the system framework is referred to as ZWCA for short, that is, the carbon audit and verification system framework of WSN based on ZigBee. In addition, this paper compares the ZWCA framework with the traditional carbon audit and verification system in several groups to analyze the practical feasibility and advantages of the ZWCA framework. The test results show that the ZWCA framework proposed in this paper can audit and verify the carbon emissions of major enterprises very well, and the initial startup power of the ZWCA framework is only 1/2 of the traditional one. At the same time, its actual running power consumption is only 3/4 of that of the traditional audit and verification system framework, and the calculation accuracy is as high as 99%. Moreover, the number of node deaths has decreased, and the system stability has been significantly improved. [ABSTRACT FROM AUTHOR]

Wireless sensor network is a multisensor wireless network system, which consists of multiple sensors and is configured independently. Because the network generates a large amount of data, the frequency, performance, and computing power of sensor nodes are limited, and they are particularly vulnerable to harsh environments and malicious attackers. This leads to the occurrence of malicious nodes, emergencies, and abnormal data in the sensor network system. Failures can also have a significant impact on sensor network services. The two main functions of wireless sensor network security are abnormal node detection and data anomaly detection. These two directions are mutually independent and complementary. Therefore, under the promotion of the rural revitalization strategy and the precision poverty alleviation strategy, China has increased its agricultural efforts. At this stage, all localities focus on the construction of rural financial systems to ensure that scattered farmers and rural small and micro-enterprises receive comprehensive financial services. The establishment of a rural financial system based on "intelligent forecasting" can improve financial development theories and build new ideas for rural financial development. And, the balance between realization and profitability, and then, through the use of Internet technology to make traditional financial institutions more effective in providing financial services, new online financial platforms can use them to make up for the existing shortcomings of traditional financial institutions as much as possible. In this article, through the research on the intelligent prediction of sensor data anomaly detection, it is applied to the development of rural finance and promotes the development of rural finance. [ABSTRACT FROM AUTHOR]

This article presents a new approach to designing the Frobenius norm-based weighted unbiased finite impulse response (FIR) fusion filter for wireless sensor networks. The weighted Frobenius norm is employed as a cost function to design a local unbiased FIR filter. The design problem is converted into a constrained optimization problem subject to an equality constraint. The Lagrange multiplier method is used to derive the local FIR filter gain. An alternative Frobenius norm is introduced to determine weights for the local unbiased FIR filters in the design of a global fusion FIR filter. The developed FIR fusion filter is demonstrated to have higher robustness against uncertainties than Kalman filter-based methods, such as the optimal fusion Kalman filter, distributed Kalman filter, and distributed weighted Kalman filter, through a numerical example of moving-target tracking employing seven smart sensors and an experiment with temperature and humidity estimation using eight sensors. [ABSTRACT FROM AUTHOR]

The use of facemasks by the general population is recommended worldwide to prevent the spread of SARS-CoV-2. Despite the evidence in favour of facemasks to reduce community transmission, there is also agreement on the potential adverse effects of their prolonged usage, mainly caused by CO2 rebreathing. Herein we report the development of a sensing platform for gaseous CO2 real-time determination inside FFP2 facemasks. The system consists of an opto-chemical sensor combined with a flexible, battery-less, near-field-enabled tag with resolution and limit of detection of 103 and 140 ppm respectively, and sensor lifetime of 8 h, which is comparable with recommended FFP2 facemask usage times. We include a custom smartphone application for wireless powering, data processing, alert management, results displaying and sharing. Through performance tests during daily activity and exercise monitoring, we demonstrate its utility for non-invasive, wearable health assessment and its potential applicability for preclinical research and diagnostics. While facemasks are recommended to prevent the spread of SARS-CoV-2, potential adverse effects may occur upon prolonged usage. Here the authors develop and evaluate an opto-chemical sensor incorporated into a smart FFP2-type facemask for wireless, real-time CO2 monitoring. [ABSTRACT FROM AUTHOR]

Based on the wireless sensor network, this paper combines node monitoring data with intelligent network address management. Users can view real-time environmental data through a computer or mobile phone and can manually remotely manage the environmental adjustment equipment of the network address through the mobile phone. This article first discusses the research background of the subject, introduces the current domestic and foreign research status of WSN in environmental detection, and analyzes the reasons for choosing ZigBee network as the wireless transmission environment of the intelligent monitoring system. Secondly, the structure, layered model, and key technologies of wireless sensor networks are introduced, and it is pointed out that ZigBee technology, which has the characteristics of low power consumption, reliable communication, self-organization of the network, strong self-healing ability, and low cost, is very suitable for application in the environment. Then, it analyzes TI's protocol stack Z-Stack based on the ZigBee2006 standard and analyzes the network address assignment and addressing in Z-Stack, the process and steps of node binding, the routing mechanism and routing maintenance, and channel configuration. The realization of other functions was discussed in depth. During the simulation experiment, in the hardware design of the intelligent monitoring system, the network node was divided into two parts: the core board and the backplane. The crystal oscillator, power supply, antenna, and I/O port circuits of the core board were designed, and the data acquisition, relay, and power supply of the backplane were designed. Finally, this paper studies the data security issues in the environmental monitoring network and proposes two solutions to control network access and data encryption. Experimental results show that in terms of low-power design, the energy of the entire system is calculated to determine the factors that affect the power consumption of the system and methods such as increasing the node sleep time to ensure that the system can work for a long time. [ABSTRACT FROM AUTHOR]

In this paper, we conduct an in-depth study and analysis of the suitability of real-time wireless sensor network-assisted smart tourism environment for tourism IoT and evaluate its suitability. We use nodes in wireless sensor networks as relays and collaborative transmission using mutual information accumulation. Using riteless codes, nodes can accumulate information in advance to decode out the original text. Due to the mobility of mobile robots, the planned routing paths can fail quickly, so we propose a dynamic mutual information accumulation collaborative transmission algorithm. For both cases of information transmission from the cloud to the robot and from the robot to the cloud, we dynamically acquire routing paths using neighbour search and chance transmission, respectively. Experimental results show that our dynamic mutual information accumulation collaborative transfer algorithm has lower time complexity and latency. We use a cooperative transmission algorithm with mutual information accumulation, because wireless transmission has the nature of broadcasting, and all neighbour nodes can receive part of the information when the sender starts to transmit information. Riteless codes are used in single-hop transmissions to improve the probability of successful transmissions. Since transmission failure requires waiting for the next neighbour to wake up, which consumes a lot of energy and time in a low duty cycle environment, using riteless codes to improve the transmission success rate of single-hop transmissions can effectively improve the global transmission efficiency. Experimental results show that the opportunity routing algorithm using riteless codes has lower delay and energy consumption. The two major systems, surface, and underground are used to quantitatively measure 23 indicators in five subsystems, including geological environment, soil and water environment, sensitive geological bodies, groundwater resources, and mineral resources, to evaluate the two-way grade of underground space development suitability, revealing the comprehensive bearing characteristics of the study area with an overall high bearing capacity of underground space resources and environment and an overall surplus of bearing status. [ABSTRACT FROM AUTHOR]

This article focuses on intensifying in-vehicle biological wireless sensor networks for the persistence of monitoring the information on a precise vehicle. The wireless sensor networks will have enormous amount of nodules which are interrelated with each other. Therefore, these wireless sensor networks can be installed on a vehicle not only for monitoring perseverance but also for corroborating security with the support of a Global Positioning System expedient. In addition, the projected work focuses on reliable communiqué which is defined in terms of network reliability with discrepancy in reporting rate at each base station. To validate the efficiency of the proposed scheme, the simulation has been abetted using network simulator (NS2) and the outcomes indicate that when the sensors are installed, a robust system can be obtained with improved data transfer between the base stations. Moreover, a fortified in-vehicular sensor can be fixed in each vehicle with minimized path loss. [ABSTRACT FROM AUTHOR]

Massive Machine Type Communication (mMTC) has long been identified as a major vertical sector and enabler of the industry 4.0 technological evolution that will seamlessly ease the dynamics of machine-to-machine communications while leveraging 5G technology. To advance this concept, we have developed and tested an mMTC network slice called Megasense. Megasense is a complete framework that consists of multiple software modules, which is used for collecting and analyzing air pollution data that emanates from a massive amount of air pollution sensors. Taking advantage of 5G networks, Megasense will significantly benefit from an underlying communication network that is traditionally elastic and can accommodate the on-demand changes in requirements of such a use case. As a result, deploying the sensor nodes over a sliceable 5G system is deemed the most appropriate in satisfying the resource requirements of such a use case scenario. In this light, in order to verify how 5G-ready our Megasense solution is, we deployed it over a network slice that is totally composed of virtual resources. We have also evaluated the impact of the network slicing platform on Megasense in terms of bandwidth and resource utilization. We further tested the performances of the Megasense system and come up with different deployment recommendations based on which the Megasense system would function optimally. [ABSTRACT FROM AUTHOR]

Along with the ever-increasing attraction of the internet of things, self-sustainable smart sensor is receiving more and more attention. However, in some industrial sensing applications, such as the monitoring system of the transmission line in power grid, there are still a lot of critical technical issues that remain to be addressed. On the one hand, the power issue has long been an obstacle of the sensors in power grid because the routine maintenance in this case is almost impossible considering its high cost. On the other hand, highly integrated current sensing techniques with large linear range for measuring the transmission current in power line are still limited. To date, in the power grid, autonomous current sensor nodes with free-standing designs are in great demand. In this article, a complete solution for addressing these two issues has been proposed. First, a magnetic energy harvester that can scavenge the ambient power-frequency magnetic field around the transmission line is proposed. The proposed electrodynamic design is based on nonlinear resonance, which is demonstrated to have a higher power density. The power density of the prototype can be as high as 3.42 mW/cm3 (tested at merely 3 Oe applied magnetic field). Second, a power management circuit is specially designed for the energy harvester. The minimum input voltage of the circuit can be as low as 0.33 V. Finally, an anisotropic magnetoresistance (AMR) sensor head with large linear range is customized for sensing the current in power grid. The largest linear range of the AMR sensor can be as large as ±170 Oe. The specially designed sensing system can be a promising solution to realize the new generation of self-sustainable current sensor network in power grid. [ABSTRACT FROM AUTHOR]

As a new generation of information technology, artificial intelligence and wireless sensor network are of great significance in promoting the development of "smart pension" and improving the quality of life of the elderly. This paper expounds the application of existing relevant artificial intelligence and wireless sensor network in smart pension, including the daily life of the elderly, health care, and spiritual comfort. This study further puts forward corresponding improvement measures for the existing problems in order to provide ideas for the application of wireless sensor network in smart pension and improve the quality of life of the elderly. [ABSTRACT FROM AUTHOR]

In this paper, wireless sensors are used to design and build a location management system for smart aging, and the optimization of this system is analysed. The function and structure of the positioning system are designed and implemented. Next, the system commissioning process and field implementation results are summarized and analysed to guarantee the reliability and integrity of the system. Finally, the system requirements and implementation are integrated to realize the basic requirements of the IoT-based elderly positioning management system and reserve the interface for later expansion. Finally, the system excellence and functional usability tested through unit testing as well as comprehensive testing. In this paper, a theoretical analysis of the prediction algorithm is carried out for the disease prediction module in the health monitoring software of the smart senior care system. To improve the accuracy of the prediction, the traditional BP neural network algorithm is optimized using DS evidence theory, thus fusing multiple sets of prediction results obtained from the BP neural network into a more accurate set of data, and the performance of the algorithm before and after the improvement is compared. The IoT-based home health management for the elderly starts from the health service demand of the elderly, explains the basic concept of IoT technology and home health management for the elderly, and analyses the feasibility of home health management for the elderly and the advantages of IoT technology in-home health management for the elderly; through the field research, the IoT-based home health management platform for the elderly is carried out from three aspects of users, business, and technology. The design covers the platform architecture and functional modules of the IoT-based senior home health management platform, which can solve the problem that the elderly can spend a comfortable life in their old age without going home. [ABSTRACT FROM AUTHOR]

Wireless sensors are a new, high-end, and popular exploration technology. The positioning and tracking of the human body are two important research issues in sensors. Aerobics is a widely popular sports item that is well-loved by the general public and integrates gymnastics, dance, music, fitness, and entertainment. The application of smart sensors helps to improve the coordination and flexibility of movements. In order to deeply study whether wireless smart sensors can play a role in tracking and recognizing aerobics exercise postures, this article uses sensor design methods, motion analysis, and software and hardware equipment architecture methods to collect samples, analyze smart sensors, and streamline algorithms. And it creates a sensor model and system for posture tracking and recognition. When testing the best position for the placement of the sensor, first fix the sensor at a distance of 2.5 cm from the wrist section and at the middle of the lower arm, keep the upper arm still, and do the stretching and contraction movements of the lower arm. Repeat the exercise 5 times and measure the wrist. The movement curve of the juncture, the result shows that the measured juncture curve at a distance of 2.5 cm is basically the same as the actual winding curve, and the fixed position in the middle deviates more from the normal curve point, and its average error is within 0.9 cm, which is correct. To test the effectiveness of the algorithm again, a total of 8 volunteers' data were collected during the experiment, and the duration was more than 25 seconds, and each normal posture tried to maintain stability and lasted for about 6 seconds. The statistical accuracy rate is 90.6%. It shows that the algorithm or the system designed in this paper has extremely high accuracy. It is basically realized that from the theory of wireless sensor network, a system model that can be used for posture tracking and recognition of aerobics is designed. [ABSTRACT FROM AUTHOR]

The wireless sensor network (WSN) plays an essential role in various practical smart applications, e.g., smart grids, smart factories, Internet of Things, and smart homes, etc. WSNs are comprised and embedded wireless smart sensors. With advanced developments in wireless sensor networks research, sensors have been rapidly used in various fields. In the meantime, the WSN performance depends on the coverage ratio of the sensors being used. However, the coverage of sensors generally relates to their cost, which usually has a limit. Hence, a new bi-tuning simplified swarm optimization (SSO) is proposed that is based on the SSO to solve such a budget-limited WSN sensing coverage problem to maximize the number of coverage areas to improve the performance of WSNs. The proposed bi-tuning SSO enhances SSO by integrating the novel concept to tune both the SSO parameters and SSO update mechanism simultaneously. The performance and applicability of the proposed bi-tuning SSO using seven different parameter settings are demonstrated through an experiment involving nine WSN tests ranging from 20, 100, to 300 sensors. The proposed bi-tuning SSO outperforms two state-of-the-art algorithms: genetic algorithm (GA) and particle swarm optimization (PSO), and can efficiently accomplish the goals of this work. [ABSTRACT FROM AUTHOR]

In order to build an intelligent platform that can be applied to singing and nervous system adjustment, this paper optimizes the positioning and information processing algorithms for wireless sensor network perception. Moreover, this article combines binocular vision to realize the singer's real-time positioning, combines the singer's emotion recognition with the intelligent sensor system, and combines the emotion recognition with the adjustment of the nervous system, so that the singer can better control the intelligent platform. In addition, in order to solve the problem of multisensor information fusion, this paper improves the sensor fusion algorithm to make it suitable for the information fusion of vision sensors and information sensors. Finally, this paper designs the functional structure of the system, transmits data through wireless sensor networks, simulates human emotion models, and studies the process of singing and the adjustment of the nervous system. It can be seen from the experimental research results that the method proposed in this paper has a certain effect. [ABSTRACT FROM AUTHOR]

With the continuous development of social economy, information resources have become more and more valued resources. Based on the intelligent monitoring architecture of the multimedia sensor network, this article proposes a nonline-of-sight positioning method that can fit the characteristics of autonomous movement for the object of intelligent terminal, that is, first draw the corresponding position trajectory according to the speed attribute of the node. On this basis, according to the relative position trajectory and radio frequency signal positioning, the two-by-two positioning of position and direction is comprehensively realized, and the positioning result is obtained; the positioning accuracy is evaluated according to the positioning of the radio frequency signal, and the false positioning result of the distorted radio frequency signal is stripped out to reduce the error influences. Practical results show that the method is effective and can meet the needs of positioning accuracy. [ABSTRACT FROM AUTHOR]

In recent years, how to improve the performance of smart factories and reduce the cost of operation has been the focus of industry attention. This study proposes a new type of location-based service (LBS) to improve the accuracy of location information delivered by self-propelled robots. Traditional localization algorithms based on signal strength cannot produce accurate localization results because of the multipath effect. This study proposes a localization algorithm that combines the Kalman filter (KF) and the adaptive-network-based fuzzy inference system (ANFIS). Specifically, the KF was adopted to eliminate noise during the signal transmission process. Through the learning of the ANFIS, the environment parameter suitable for the target was generated, to overcome the deficiency of traditional localization algorithms that cannot obtain real signal strength. In this study, an experiment was conducted in a real environment to compare the proposed localization algorithm with other commonly used algorithms. The experimental results show that the proposed localization algorithm produces minimal errors and stable localization results. [ABSTRACT FROM AUTHOR]