Wireless Sensor Network Technology Research Articles

Design of intelligent terminal app for digital manufacturing technology based on virtual reality and wireless sensor network technology

Design of intelligent terminal app for digital manufacturing technology based on virtual reality and wireless sensor network technology

An IoT Healthcare System With Deep Learning Functionality for Patient Monitoring

ABSTRACTCurrently, healthcare systems operate under conventional management practices and entail storing and processing substantial medical data. Integrating the Internet of Things (IoT) and wireless sensor networks (WSNs) technologies has facilitated the development of IoT‐enabled healthcare, which possesses advanced data processing capabilities and extensive data storage. This paper proposes a WSN and IoT framework for patient monitoring in high‐speed 5G communications. Based on an artificial neural network (ANN), an intelligent health monitoring system was developed using IoT technology to monitor a person's blood pressure, heart rate, oxygen level, and temperature. Furthermore, the system helps the elderly being in critical cases in their homes to communicate and update their medical condition with the hospital, especially in critical cases, to be treated as soon as possible, especially in remote areas. The experimental results showed the superiority and effectiveness of the proposed system. Moreover, relying on ANNs to extract the basic features, the accuracy reached 96%. The proposed system was implemented practically, and the results were displayed in real time and compared with commercial medical devices. Maximum relative errors are heart rate (2.19), body temperature (2.94), systolic blood pressure (3.4), diastolic blood pressure (2.89), and SpO2 (1.05). On the other hand, the proposed system is much faster than other wireless communication methods, regardless of the detection quality.

Design and Implementation of an IoT-Based Intravenous Infusion Monitoring System Using Wireless Sensor Network

This research presents a Wireless Sensor Network (WSN) based infusion monitoring system that utilizes Internet of Things (IoT) technology to enhance infusion management in healthcare. Manual supervision often lacks the necessary accuracy and control, making automated solutions crucial. Our system employs optocoupler and load cell sensors to calculate the infusion drop rate per minute and assess the remaining fluid percentage. Tests conducted demonstrated that optocoupler sensors effectively measured the drop rate, while load cell sensors accurately determined fluid levels. Measurement results are easily monitored via smartphone, granting healthcare professionals rapid access to vital data. A key innovation of this system is its ability to enable remote monitoring through WSN, allowing a single nurse to oversee multiple infusions with just one smartphone. The test results indicate impressive accuracy, achieving an average precision rate of 97% for drop rate measurements and 93% for remaining fluid percentage measurements. Overall, this system offers an efficient and controlled approach to infusion fluid management, ensuring optimal care for patients. By integrating IoT and WSN technologies, this research lays the groundwork for developing more advanced and interconnected infusion monitoring systems, ultimately enhancing patient care in today's globalized healthcare environment.

Soil Nutrient Analysis and Automatic Monitoring in Precision Agriculture

ABSTRACT The higher productivity from soil can be achieved through Wireless Sensor Networks (WSNs) technology; for this reason, the implementation of WSNs in precision agriculture is increasing day by day. Among the different technologies for crop monitoring, WSNs are recognized as a powerful option for collecting and processing data in the agricultural domain, with low cost and low energy consumption. The ultimate aim of this paper is to predict the precise values of soil nutrients nitrogen, phosphorus, and potassium (NPK) using a machine learning (ML)-based mathematical model formulation. The proposed system will use Global System for Mobile Communications (GSM) technology to automatically monitor soil parameters. It transfers soil data over the mobile network using a GSM modem. In the hardware part, sensor node units were developed to fetch and load data through the energy-efficient GSM, utilizing a bi-directional multi-level text messaging option to improve the alarm system’s efficiency. The system contains sensors such as potential of hydrogen and humidity (PH), which are used to monitor soil data collected from the sensors by the microcontroller. Additionally, as a novel contribution, machine learning based on regression models is applied to the NPK data. A novel data interpolation approach is proposed for data oversampling. The R2 values are compared, achieving nearly 99.5% accuracy in all cases. This paper focuses on various challenges and scalability issues encountered in research and discusses different results obtained by monitoring various soil parameters in a real-time agricultural environment.

DDS-P: Stochastic models based performance of IoT disaster detection systems across multiple geographic areas

Effective management of catastrophic events in high-risk zones necessitates a holistic technological approach to protect ecosystems, biodiversity, and native populations. Limitations in sensor range and connectivity hamper real-time data gathering in secluded areas, while financial and technical hurdles hinder the creation of cost-effective, automated systems. This study presents stochastic models, the LoRaW protocol, and cloud technology to enhance sensor deployment simulations. Wireless Sensor Networks and LoRa technology are crucial for extensive monitoring and communication infrastructures. Stochastic Petri Net models optimize system components by assessing crucial performance indicators, such as average response time and system utilization, thus improving disaster response and supporting research hypotheses.

Localization algorithm of soil moisture monitoring in irrigation area based on weighted correction of distance measurement

At present, Wireless Sensor Network technology are widely used in the fields of agricultural environment monitoring. Whether we use ground network or ground-air coordination, the position coordinates of unknown nodes are an important feature of sensor information collection. To achieve the goal of a wireless monitoring system for field soil irrigation and its node positioning, we proposed an RSSI (Received Signal Intensity Indication)-based agricultural environment irrigation monitoring system. The algorithm has three stages: RSSI ranging, error correction, and localization. In the RSSI ranging phase, we obtain inter-node measurement distance by wireless channel modeling. In the distance-weighted correction phase, considering the difference between the measured distance of the beacon nodes and the actual distance, we analyze and select the relative error coefficient for the beacon nodes to correct the measured distance between the unknown nodes and the beacon nodes within their communication range. In the positioning stage for the nodes to be located, we estimated the required node coordinates using a weighted centroid localization method. In addition, by analyzing the monitoring data, we know the changes in soil moisture in real-time, which provides new ideas for irrigation automation and the efficient utilization of water resources. When designing the algorithm, we thoroughly considered the influence of RSSI ranging inaccuracy and the quantity of beacon nodes on the localization precision. The test demonstrated that the method presented in this paper has higher localization precision and lower computation cost.

Node Role Selection and Rotation Scheme for Energy Efficiency in Multi-Level IoT-Based Heterogeneous Wireless Sensor Networks (HWSNs).

The emergence of Internet of Things (IoT)-based heterogeneous wireless sensor network (HWSN) technology has become widespread, playing a significant role in the development of diverse human-centric applications. The role of efficient resource utilisation, particularly energy, becomes further critical in IoT-based HWSNs than it was in WSNs. Researchers have proposed numerous approaches to either increase the provisioned resources on network devices or to achieve efficient utilisation of these resources during network operations. The application of a vast proportion of such methods is either limited to homogeneous networks or to a single parameter and limited-level heterogeneity. In this work, we propose a multi-parameter and multi-level heterogeneity model along with a cluster-head rotation method that balances energy and maximizes lifetime. This method achieves up to a 57% increase in throughput to the base station, owing to improved intra-cluster communication in the IoT-based HWSN. Furthermore, for inter-cluster communication, a mathematical framework is proposed that first assesses whether the single-hop or multi-hop inter-cluster communication is more energy efficient, and then computes the region where the next energy-efficient hop should occur. Finally, a relay-role rotation method is proposed among the potential next-hop nodes. Results confirm that the proposed methods achieve 57.44%, 51.75%, and 17.63% increase in throughput of the IoT-based HWSN as compared to RLEACH, CRPFCM, and EERPMS, respectively.

Simulation of Optical Sensors in IoT Motion Training Systems in Wireless Sensor Networks and Cloud Technology Environments

Simulation of Optical Sensors in IoT Motion Training Systems in Wireless Sensor Networks and Cloud Technology Environments

A Smart Farm Monitoring and Control System using Wireless Sensor Network

Agriculture is one of the most important sector that accounts for economic growth in the developing countries of the world. Many developing countries are now focusing on agricultural development, yet the sector is not without challenges including climate changes, drought, flooding to mention but a few. These result in poor yield of agricultural products. In this work, we l developed a robust smart system to enhance both irrigation and flooding monitoring and control, leveraging on Wireless Sensor Networks (WSNs) to boost agricultural production. In the system’s implementation, Arduino based instrumentation, integrated with temperature, soil moisture and water level sensor shall be adopted to monitor the agricultural environment, while reporting the status wirelessly through the Radio Frequency (RF) modules to the base station. The base station will evaluate the received data and either activates or deactivates the irrigation or drainage pump using specified threshold values. The remote reporting the state of farm shall be done by deploying a 900Mhz transmitter interfaced with the system’s controller. This work shall be validated using an experimental test bed, which shall be used for field experiments, data collection and evaluation. The result of the work shall highlight the potentials of WSN technology in monitoring and control technology of both irrigation and flooding within the farm and in turn boost productivity

Hybrid algorithm GA-PSO-BP in secure localization technology for wireless sensor networks

ABSTRACT Wireless sensors face problems such as poor positioning accuracy in their applications. A hybrid indoor space safety positioning technology is proposed for this purpose. This technology uses genetic models and particle swarm optimization models to optimize the parameters of neural networks. Considering the vulnerability of wireless sensors to Sybil attacks during the positioning process, communication encryption and location awareness are used for optimization. In the positioning experiment, the proposed method had maximum positioning errors of 0.23 m and 0.43 m on the first and second floors, respectively, which were superior to other methods. In the positioning security experiment, the positioning errors of the first and second floors after being attacked by Sybil were 4 m and 5 m respectively, while the proposed security positioning technology had an error range of 1 m. In terms of security comparison, when there are 31–35 attack samples, the proposed method has a maximum root mean square error and average absolute error of 0.72 m and 0.79 m, respectively, which is superior to other models. It can be seen that the proposed technology has excellent positioning effect and stability. The research content will provide important technical support for the practical deployment and security management of wireless sensor networks.

An effective metaheuristic based optimized type‐II fuzzy inference system for fault node identification in wireless sensor network

SummaryDeploying a wireless sensor network (WSN) for accessing the network of distant environments is more crucial. Node defect detection is a core technology of WSN and is essential for most WSN networks. The defective node reduces the overall service quality (OSQ) of the global WSN network system. Therefore, a type II fuzzy inference system (T2_FIS) is proposed for detecting defective nodes in the WSN network. The adaptive genetic algorithm (AGA) and proposed method are introduced for tuning the parameters in the T2_FIS system. The proposed T2_FIS method effectively detects the broken node in the WSN network using the fuzzy rules. In addition, the improved Mud Ring (IMR) optimization algorithm is proposed to replace the faulty node with the neighborhood node in the network. The defective nodes are identified and replaced with the closest nodes based on the membership function (MF) generated by the T2_FIS. Furthermore, the lifetime and throughput of the WSN network are increased by minimizing energy consumption. The overall performance is evaluated using the MATLAB tool, and the implementation results are compared with the existing methods. The total energy consumption for the proposed method is 50.451, with a throughput and lifetime of 153.657 and 22979.25. Furthermore, the performance metrics for the existing and proposed strategies are analyzed, and the accuracy of the proposed method is proven to be 99.3827%, the false alarm rate (FAR) is 0.0008, and the false positive rate (FPR) is 0.0617. The results show the effectiveness and performance of the proposed method.

Addressing out-of-hospital cardiac arrest with current technology advances: Breaking the deadlock with a mobile network.

Out-of-hospital cardiac arrest (OHCA) is a global public health problem, with survival rates remaining low at around 10% or less despite widespread cardiopulmonary resuscitation (CPR) training and availability of automated external defibrillators (AEDs). This is partly due to the challenges of knowing when and where a sudden OHCA occurs and where the nearest AED is located. In response, countries around the world have begun to use network technology-based smartphone applications. These applications are activated by emergency medical service dispatchers and alert preregistered volunteer first responders (VFRs) to nearby OHCAs using Global Positioning System localization. Accumulating evidence, although mostly from observational studies, shows their effectiveness in increasing the rate of bystander CPR, defibrillation, and patient survival. Current guidelines recommend the use of these VFR alerting systems, and the results of ongoing randomized trials are awaited for further dissemination. This article also proposed the concept of a life-saving mobile network (LMN), which uses opportunistic network and wireless sensor network technologies to create a dynamic mesh network of potential victims, rescuers, and defibrillators. The LMN works by detecting a fatal arrhythmia with a wearable sensor device, localizing the victim and the nearest AED with nearby smartphones, and notifying VFRs through peer-to-peer communication. While there are challenges and limitations to implementing the LMN in society, this innovative network technology would reduce the tragedy of sudden cardiac death from OHCA.

A Critical Review of the Propagation Models Employed in LoRa Systems.

LoRa systems are emerging as a promising technology for wireless sensor networks due to their exceptional range and low power consumption. The successful deployment of LoRa networks relies on accurate propagation models to facilitate effective network planning. Therefore, this review explores the landscape of propagation models supporting LoRa networks. Specifically, we examine empirical propagation models commonly employed in communication systems, assessing their applicability across various environments such as outdoor, indoor, and within vegetation. Our investigation underscores the prevalence of logarithmic decay in most empirical models. In addition, we survey the relationship between model parameters and environmental factors, clearing their nuanced interplay. Analyzing published measurement results, we extract the log-distance model parameters to decipher environmental influences comprehensively. Drawing insights from published measurement results for LoRa, we compare them with the model's outcomes, highlighting successes and limitations. We additionally explore the application of multi-slope models to LoRa measurements to evaluate its effectiveness in enhancing the accuracy of path loss prediction. Finally, we propose new lines for future research in propagation modelling to improve empirical models.

Selfish attack detection and response using cooperative backoff adjustment in wireless sensor networks

In recent times, the proliferation of intelligent internet of things (IoT) and wireless sensor network (WSN) technologies has significantly enhanced both daily life and industrial operations. Despite their widespread application, these networks face challenges with resource monopolization by selfish nodes, which detrimentally impacts network efficiency. This study introduces a novel detection and response strategy for selfish nodes, utilizing backoff adjustment mechanisms to reduce throughput deterioration. The effectiveness of this approach was rigorously evaluated using the NetSim simulator, focusing on the impact of backoff adjustment on network throughput. Simulation results indicate that the implementation of this method in the presence of selfish nodes yields a noteworthy improvement in network throughput, with approximately more than twice. This advancement holds promise for enhancing the robustness and efficiency of IoT and WSN technologies against disruptive selfish node behaviors.

Improvement of Teach Protocol for Enhancing Features of WSN

. Wireless Sensor Network (WSN) has several applications such as military, industry. and environment. The importance of WSNs in current applications makes the WSN technology highly relevant and significant to the field of communication and computing. However, WSN's performance deals with number of challenges. Energy consumption is the most considerable because nodes use energy to collect, treat, and send data, but they have restricted energy- For this reason. numerous efficient energy routing protocols have been dev eloped to save the consumption or power. Low energy adaptive clustering hierarchy (LEACH) is considered the most attractive one in WSNs, in this projects we evaluate the LEACH approach effectiveness in the cluster-head (CHJ choosing and in data transmission, then we propose an enhanced protocol. proposed algorithm aims to improve energy Consumption and prolong the lifetime of WSN through selecting CHs depending on the remaining power, balancing the number of nodes in clusters. determining abandoned nudes in order to send their data to the sink. Then CHs choose the optimal path to reach the sink, we propose a new approach to achieve better enhancement of WSN in terms of network lifetime and data transmission time represented by reducing the packet delay time. Then, we compare the simulated result of the proposed algorithm with the basic LEACH protocol.

Emerging applications and ongoing challenges in WSN assisted IoT networks

In current scenario, Wireless Sensor Networks (WSN) based on Internet of Things (IoT) have seen a significant growth in research area. IoT is a network that enables sensors, and other events to interact with each other without human interference. In addition, wireless network is an essential part of the Internet of Things, generating an abundance of real-time applications. WS Ns can be recognized by their distributed sensor nodes and application fields including environmental monitoring, healthcare, agriculture, industrial automation, and smart cities. Therefore, an extensive usage of such networks is the outcome of their adaptability in variety of fields. The present study digs into the immense scope of WSN applications and the daunting challenges that come alongside. However, these applications face a slew of obstacles, including those related to energy, scalability, security, quality of service, network dependability, and data management. Further, the study focuses on gathering insights into the changing WSN technology environment by examining these applications and the issues they encounter, allowing to satisfy the demands of both existing and forthcoming deployments. As WSN technology is improving, the present study contributes by investigating prospective solutions, such as machine learning integration, energy harvesting, and standardization efforts.

Graph Laplace Regularization-based pressure sensor placement strategy for leak localization in the water distribution networks under joint hydraulic and topological feature spaces

Urban water distribution networks (WDNs) have wide range and intricate topology, which include leakage, pipe burst and other abnormal states during production and operation. With the continuous development of the Internet of Things (IoT) technology in recent years, the means of monitoring the WDNs by using wireless sensor network technology has gradually received attention and extensive research. Most of the existing researches select the deployment location of sensors according to the hydraulic state of the WDNs, but the connectivity and topology between the nodes of the WDNs are not fully considered and analyzed. In this study, a new method that can integrate the topological features and hydraulic model information of the WDN is proposed to solve the problem of optimal sensor placement. First, the method preprocesses the covariance matrix of the pressure sensitivity matrix of the water distribution network by a diffusion kernel-based data prefiltering method and obtains the new network topology weights and its Laplacian matrix under the constraints of the network topology through a data-based graphical Laplacian learning method. Then, the sensor placement problem is transformed into a matrix minimum eigenvalue constraint problem by the Graph Laplace Regularization (GLR)-based method, and finally the selection of sensor nodes is accomplished by the method based on Gershgorin Disc Alignment (GDA). The proposed strategy is tested on a passive Hanoi network, an active Net 3 network, and a larger network, PA2, and is compared with some existing methods. The results show that the proposed solution achieves good performance in three different leak localization methods.

Energy-Recognition Clustering Technique Based on Reinforcement Learning In WSN

WSN (Wireless Sensor Network) technology has recently gained a lot of attention. This began with the deployment of small WSNs and moved to the deployment of big and IoT WSNs, all with an emphasis on energy conservation. Wireless sensor networks can benefit from network clustering to increase their energy efficiency. The practise of dividing nodes into clusters before picking multiple cluster heads is known as network clustering (CHs). Clustering in wireless sensor networks is known to save energy and extend the network's lifetime (WSNs). Energy efficiency is a hot topic in existing wireless sensor networks, although it's not generally discussed. In this research, we offer a reinforcement learning (RL) based energy-aware clustering approach, whereby peripheral cluster nodes monitor environmental factors like energy use and choose an optimal cluster leader (CH). Connect the CH (BS) to the base station. In the simulation (PDR), performance factors such as network lifetime, energy tax, network stability period, and packet delivery rate are all taken into account. The simulation results show that the proposed QL-ReLeC performs around 11% better than the reference protocol in terms of PDR and 11% better in terms of energy tax.

Location Protection Technology for Wireless Sensor Networks Based on Differential Privacy using DSRNN-MOA

Wireless Sensor Networks (WSNs) play a pivotal role in modern data-driven applications, yet concerns persist regarding the privacy and security of sensitive location information. The attributes of Wireless Sensor Networks (WSNs) make them susceptible to eavesdropping, enabling attackers to intercept data packets across single or multiple communication links. This interception allows for the extraction of sensitive data from various sensor information, presenting a significant challenge to location privacy. Consequently, it becomes crucial to implement effective measures for safeguarding the privacy of training sample data when utilizing WSNs. In this manuscript, Dynamically Stabilized Recurrent Neural Network (DSRNN) optimized with Mother Optimization Algorithm (DSRNN-MOA) is proposed. Initially data is taken from WSN dataset. Afterward the data is fed to Variational Bayesian-based Maximum Correntropy Cubature Kalman Filtering (VBMCCKF) based pre-processing process. The pre-processing output is provided to the Dynamically Stabilized Recurrent Neural Network to enhance source location protection while addressing challenges related to recurrent network stability and gradient issues. The learnable parameters of the DSRNN is optimized using MOA. The proposed strategy, LPWSN-DSRNN-MOA, is implemented in MATLAB, and its effectiveness is assessed using a number of performance evaluation measures, including ROC analysis, accuracy, precision, recall, f1-score, mean squad error, and recall. The proposed LPWSN-DSRNN-MOA method shows the highest accuracy of 98%, precision of 99%, specificity of 98% and F1-score of 99% while comparing other existing methods such as Location Protection for Wireless Sensor Networks based on Artificial Neural Network(LPWSN-ANN), Location Protection for Wireless Sensor Networks based on Deep Neural Network (LPWSN-DNN), and Location Protection for Wireless Sensor Networks based on Machine Learning (LPWSN-ML) respectively.

Malicious Code Propagation Model Based on Sleep Monitoring Mechanism in Wireless Sensor Networks

<p>Wireless sensor networks (WSNs) are characterized by high node density and finite energy storage. Each node exchanges information with all its neighboring nodes frequently, which makes it easy for nodes to run out of energy, leading to paralysis of the WSNs when facing network malicious code attacks. To address this problem, a malicious code propagation model based on sleep-monitoring technology for WSNs is proposed. As a Multi-compartment propagation, sleep nodes and monitoring nodes are introduced to the conventional SIR model. Sleep nodes can turn the infected node into a dormant state and stop the dissemination of information to save energy. Monitoring nodes can contain malicious codes spread in wireless sensor networks by sharing prevention information in real time. Additionally, by calculating the equilibrium point and propagation threshold of the new feedback model, the corresponding Lyapunov function is constructed to prove the local stability and global stability of the equilibrium point. Finally, the results of numerical simulation experiments show that when the sleep rate is 0.3 and feedback rate is 0.0000005, the number of infected nodes in the wireless sensor network decreases by 43.45%. Therefore, adding sleep-monitoring technology can effectively control the spread of malicious code in the networks.</p> <p> </p>