Sensors In Network Research Articles

Deep Reinforcement Learning-Based Energy Consumption Optimization for Peer-to-Peer (P2P) Communication in Wireless Sensor Networks.

The fast development of the sensors in the wireless sensor networks (WSN) brings a big challenge of low energy consumption requirements, and Peer-to-peer (P2P) communication becomes the important way to break this bottleneck. However, the interference caused by different sensors sharing the spectrum and the power limitations seriously constrains the improvement of WSN. Therefore, in this paper, we proposed a deep reinforcement learning-based energy consumption optimization for P2P communication in WSN. Specifically, P2P sensors (PUs) are considered agents to share the spectrum of authorized sensors (AUs). An authorized sensor has permission to access specific data or systems, while a P2P sensor directly communicates with other sensors without needing a central server. One involves permission, the other is direct communication between sensors. Each agent can control the power and select the resources to avoid interference. Moreover, we use a double deep Q network (DDQN) algorithm to help the agent learn more detailed features of the interference. Simulation results show that the proposed algorithm can obtain a higher performance than the deep Q network scheme and the traditional algorithm, which can effectively lower the energy consumption for P2P communication in WSN.

Assessing Sensor Integrity for Nuclear Waste Monitoring Using Graph Neural Networks.

A deep geological repository for radioactive waste, such as Andra's Cigéo project, requires long-term (persistent) monitoring. To achieve this goal, data from a network of sensors are acquired. This network is subject to deterioration over time due to environmental effects (radioactivity, mechanical deterioration of the cell, etc.), and it is paramount to assess each sensor's integrity and ensure data consistency to enable the precise monitoring of the facilities. Graph neural networks (GNNs) are suitable for detecting faulty sensors in complex networks because they accurately depict physical phenomena that occur in a system and take the sensor network's local structure into consideration in the predictions. In this work, we leveraged the availability of the experimental data acquired in Andra's Underground Research Laboratory (URL) to train a graph neural network for the assessment of data integrity. The experiment considered in this work emulated the thermal loading of a high-level waste (HLW) demonstrator cell (i.e., the heating of the containment cell by nuclear waste). Using real experiment data acquired in Andra's URL in a deep geological layer was one of the novelties of this work. The used model was a GNN that inputted the temperature field from the sensors (at the current and past steps) and returned the state of each individual sensor, i.e., faulty or not. The other novelty of this work lay in the application of the GraphSAGE model which was modified with elements of the Graph Net framework to detect faulty sensors, with up to half of the sensors in the network being faulty at once. This proportion of faulty sensors was explained by the use of distributed sensors (optic fiber) and the environmental effects on the cell. The GNNs trained on the experimental data were ultimately compared against other standard classification methods (thresholding, artificial neural networks, etc.), which demonstrated their effectiveness in the assessment of data integrity.

Modeling of Sports Training Simulation Based on Energy Harvesting in Wireless Sensor Networks

For patients with limb motor dysfunction, the effect of physical exercise is directly related to their future quality of life. This article combines the physical training plan of rehabilitation therapists with the training of rehabilitation robots, which can effectively improve the training performance of existing lower limb rehabilitation robots. Therefore, a teaching and training method and a wireless data acquisition system based on energy acquisition wireless network sensor are proposed. Based on wireless wearable technology, wireless network sensors, PCs and electronic devices are used to monitor the activity information of human walking and standing in real time, and the physical fitness is tested by means of mean, variance, and standard deviation. Through the analysis of rehabilitation health, this article consists of two parts: power module and physical exercise. Finally, experiments show that the accuracy of wireless network sensors based on SVM algorithm is the most accurate under physical training. It provides a good means for wireless body area network technology.

Robust sequential fusion Kalman estimators with asymptotic equivalence and stability for networked uncertain sensor systems

For networked uncertain sensor systems (NUSSs) with an improved unified sensor model with “hard” and “soft” sensors including five uncertainties plus two-step random measurement delays and colored measurement noises, applying the block matrix inversion formula, a new sequential inverse covariance fusion (SICF) approach is presented. It can sequentially compute the global inverse covariance according to the time order of the received data, but need not wait until all data are received. The corresponding minimax robust time-varying and steady-state sequential fusion (SF) Kalman estimators (predictor, filter, and smoother) are presented in the sense that their actual error variances are guaranteed to have the corresponding minimal upper bounds for all admissible uncertainties. They have lower computation complexities and higher accuracies equivalent to ones of the robust optimal batch-fusion (BF) Kalman estimators, and are suitable for real-time applications. Their asymptotic equivalence, absolute asymptotic stability, and accuracy relations are proved. Their strict complexity analysis is given. They constitute a novel universal robust SF estimation, convergence and stability theory for NUSSs. One simulation example applied to the uncertain ARMA signal processing verifies the effectiveness of the proposed approach and theory.

Towards a hygroscopic growth calibration for low-cost PM2.5 sensors

Abstract. Low-cost particulate matter (PM) sensors continue to grow in popularity, but issues such as aerosol-size-dependent sensitivity drive the need for effective calibration schemes. Here we devise a time-evolving calibration method for the Plantower PMS5003 PM2.5 mass concentration measurements. We use 2 years of measurements from the Berkeley Environmental Air-quality and CO2 Network sensors deployed in San Francisco and Los Angeles in our analysis. The calibration uses a hygroscopic growth correction factor derived from κ-Köhler theory, where the calibration parameters are determined empirically using US Environmental Protection Agency Air Quality System (EPA AQS) reference data at co-location sites during the period from 2021–2022. The parameters are found to vary cyclically through the seasons, and the seasonal cycles match changes in sulfate and elemental carbon PM composition fractions throughout the year. In both regions, the seasonal RH dependence calibration performs better than the uncalibrated data and data calibrated with the EPA's national Plantower calibration algorithm. In the San Francisco Bay Area, the seasonal RH dependence calibration reduces the root mean square error (RMSE) by ∼40 % from the uncalibrated data and maintains a mean bias much smaller than the EPA national calibration scheme (−0.90 vs −2.73 µg m−3). We also find that calibration parameters forecasted beyond those fit with the EPA reference data continue to outperform the uncalibrated data and EPA calibration data, enabling real-time application of the calibration scheme even in the absence of reference data. While the correction greatly improves the data accuracy, non-Gaussian distribution of the residuals suggests that other processes besides hygroscopic growth can be parameterized for future improvement of this calibration.

Estimation of Muscle Forces of Lower Limbs Based on CNN-LSTM Neural Network and Wearable Sensor System.

Estimation of vivo muscle forces during human motion is important for understanding human motion control mechanisms and joint mechanics. This paper combined the advantages of the convolutional neural network (CNN) and long-short-term memory (LSTM) and proposed a novel muscle force estimation method based on CNN-LSTM. A wearable sensor system was also developed to collect the angles and angular velocities of the hip, knee, and ankle joints in the sagittal plane during walking, and the collected kinematic data were used as the input for the neural network model. In this paper, the muscle forces calculated using OpenSim based on the Static Optimization (SO) method were used as the standard value to train the neural network model. Four lower limb muscles of the left leg, including gluteus maximus (GM), rectus femoris (RF), gastrocnemius (GAST), and soleus (SOL), were selected as the studying objects in this paper. The experiment results showed that compared to the standard CNN and the standard LSTM, the CNN-LSTM performed better in muscle forces estimation under slow (1.2 m/s), medium (1.5 m/s), and fast walking speeds (1.8 m/s). The average correlation coefficients between true and estimated values of four muscle forces under slow, medium, and fast walking speeds were 0.9801, 0.9829, and 0.9809, respectively. The average correlation coefficients had smaller fluctuations under different walking speeds, which indicated that the model had good robustness. The external testing experiment showed that the CNN-LSTM also had good generalization. The model performed well when the estimated object was not included in the training sample. This article proposed a convenient method for estimating muscle forces, which could provide theoretical assistance for the quantitative analysis of human motion and muscle injury. The method has established the relationship between joint kinematic signals and muscle forces during walking based on a neural network model; compared to the SO method to calculate muscle forces in OpenSim, it is more convenient and efficient in clinical analysis or engineering applications.

Through the Soil Long Range Wireless Power Transfer for Agricultural IoT Networks

Increasing the spatial and temporal density of data using networked sensors, known as the Internet of Things (IoT), can lead to enhanced productivity and cost savings in a host of industries. Where applications involve large outdoor expanses, such as farming, oil and gas, or defense, large regions of unelectrified land could yield significant benefits if instrumented with a high density of IoT systems. The major limitation of expanding IoT networks in such applications stems from the challenge of delivering power to each sensing device. Batteries, generators, and renewable sources have predominately been used to address the challenge, but these solutions require constant maintenance or are sensitive to environmental factors. This work presents a novel approach where conduction currents through soil are utilized for the wireless powering of sensor networks, initial investigation is within an 0.8-ha (2-acre) area. The technique is not line-of-sight, powers all devices simultaneously through near-field mechanics, and has the ability to be minimally invasive to the working environment. A theory of operation is presented and the technique is experimentally demonstrated in an agricultural setting. Scaling and transfer parameters are discussed.

Infrastructure-Wide and Intent-Based Networking Dataset for 5G-and-beyond AI-Driven Autonomous Networks

In the era of Autonomous Networks (ANs), artificial intelligence (AI) plays a crucial role for their development in cellular networks, especially in 5G-and-beyond networks. The availability of high-quality networking datasets is one of the essential aspects for creating data-driven algorithms in network management and optimisation tasks. These datasets serve as the foundation for empowering AI algorithms to make informed decisions and optimise network resources efficiently. In this research work, we propose the IW-IB-5GNET networking dataset: an infrastructure-wide and intent-based dataset that is intended to be of use in research and development of network management and optimisation solutions in 5G-and-beyond networks. It is infrastructure wide due to the fact that the dataset includes information from all layers of the 5G network. It is also intent based as it is initiated based on predefined user intents. The proposed dataset has been generated in an emulated 5G network, with a wide deployment of network sensors for its creation. The IW-IB-5GNET dataset is promising to facilitate the development of autonomous and intelligent network management solutions that enhance network performance and optimisation.

Enhancing anomaly detection with adaptive node inspection in large-scale networks with binary sensors

Maintaining the stability and reliability of large-scale networks and graph structures is a practical challenge, particularly in sensor-intensive systems. One critical task in such networks is to identify anomalies and the origin of disturbances in a timely manner. However, successful anomaly detection requires sufficient and accurate data from sensors across the network. This work aims to develop an innovative framework to improve the accuracy of anomaly detection tasks with binary sensor data through intelligent node selection and inspection. Instead of relying only on stochastic insights obtained from network sensors, we explore how a specific small set of nodes can be inspected using a Bayesian framework so that the anomaly detection performance is improved. We demonstrate the effectiveness of the proposed model with a set of numerical experiments.

Data-driven approaches for deriving a soft sensor in a district heating network

To facilitate the ongoing transition to fourth-generation district heating systems, it is necessary to find resource-efficient methods to model the differential pressure in a district heating network accurately. This paper has developed, tested, and compared data-driven methods to create a soft sensor for a pressure transmitter in the district heating network. The sensor is used to control the district heating load and therefore, is the most critical spot of the sensor network. The data-based modelling approaches chosen were transfer functions and neural networks. The data set was collected from Hafslund Oslo Celsio’s historical database for January–March 2021, when the heating demand is highest. The best convolutional neural network and a first-order transfer function give acceptable results in estimating the pressure transmitter signal. Both models have the simplest architectures within their model type, suggesting that the need for complex models in either approach is redundant.

Research on multiple damage localisation based on fusion of the Lamb wave ellipse algorithm and RAPID algorithm

Current damage localisation methods often require many sensors and complex signal processing methods. This paper proposes a fusion algorithm based on elliptical localisation and the reconstruction algorithm for probabilistic inspection of damage (RAPID) to locate and image multiple damages. Experimental verification of the damage algorithm was conducted. An ultrasonic probe was used to excite Lamb signals on an aluminium alloy plate, the ultrasonic response signals at different positions within the plate under multiple damages were measured and the constructed algorithm was employed to image the damage location. In the experiment, this method improved localisation efficiency by excluding invalid sensing paths in the sensor network, saving 31.32% of computational time. When some sensors in the sensor network were damaged, this algorithm ensured a positioning accuracy with a positioning error of 5.83 mm. Finally, the algorithm was used to locate multiple damages in the sensor network and the results showed the good robustness of the algorithm.

Conditions for Estimation of Sensitivities of Voltage Magnitudes to Complex Power Injections

Voltage phase angle measurements are often unavailable from sensors in distribution networks and transmission network boundaries. Therefore, this paper addresses the conditions for estimating sensitivities of voltage magnitudes with respect to complex (active and reactive) electric power injections based on sensor measurements. These sensitivities represent submatrices of the inverse power flow Jacobian. We extend previous results to show that the sensitivities of a bus voltage magnitude with respect to active power injections are unique and different from those with respect to reactive power. The classical Newton-Raphson power flow model is used to derive a novel representation of bus voltage magnitudes as an underdetermined linear operator of the active and reactive power injections—parameterized by the bus power factors. Two conditions that ensure the existence of unique complex power injections given voltage magnitudes are established for this underdetermined linear system, thereby compressing the solution space. The first is a sufficient condition based on the bus power factors. The second is a necessary and sufficient condition based on the system eigenvalues. We use matrix completion theory to develop estimation methods for recovering sensitivity matrices with varying levels of sensor availability. Simulations verify the results and demonstrate engineering use of the proposed methods.

A Versatile RF Energy Harvester for IoT Sensors in 5G Network With Extended Input Power Range

A Versatile RF Energy Harvester for IoT Sensors in 5G Network With Extended Input Power Range

Design and Development of a University Outdoor Air Quality Monitoring System

An increase in air quality monitoring research using low-cost air quality sensors has been observed. Existing air quality monitoring systems provide real-time data on at most five primary pollutants, cover a short area, and could be made expandable. An air quality monitoring system utilizing a scalable, low power wide area network, centralized system, and low cost sensors is proposed. The nodes gather data on air pollutants and environmental factors. Data conversion, storage, and calculation of air quality indices were processed on the central subsystem and visualization through IoT cloud. The results suggest improvement in the design and integration of prediction models, battery, and power management techniques.

ACCURACY OF THE ITERATIVE PROCESS FOR A PROBLEM OF TWO-DIMENSIONAL DISTRIBUTED SEARCH

The characteristics of the computational process performed by a set of nodes of a mobile distributed sensor network are investigated. The goal of the computational process is to find a smaller subregion in a given area on a plane, in which objects of a certain type are located. The sought subdomain is represented as a convex hull of a set of points. Objects are visible to network sensors only during random periods of time. Network nodes move in accordance with the random walk model during the search. The dependence of the convergence rate of the computational process on the number of network nodes, the radii of sensor observation, the radii of the radio communication zone, and the random walk parameters is analyzed. A method for estimating the process quality function in the vicinity of selected points in the parameter space is proposed. The results are presented in dependency plots.

Speech recognition enhancement based on wireless network sensors application in interactive intelligent teaching system

The speech function used in the multi-dimensional speech recognition model based on MTL and RNN is the commonly used MFCC function, and in feature extraction, after various filtering and conversion, deleting part of the speech information is a requirement for 3D audio recognition. Use multidimensional information as much as possible. Based on this, this article not only brings the development of convolutional neural network intelligent teaching, but also proposes a new education model. Heuristic education like flipped classroom is still the content of education research in recent years, and it is important to the current education form in China. It is mainly based on the traditional lecture mode or the classroom form. This article focuses on undergraduate education. It is designed in the classroom based on the video data of the undergraduate interactive activities and the interactive system. It has important research significance and practicality. At the same time, the interactive content of the traditional teaching method is used to perform voice recognition, and the recognition effect of the main body of the interactive system is further improved through the voice information. The spatial perspective is used to divide the lens perspective into seminars and experimental forms, and the pixel change value of each frame provides a specific reference for it to determine the reflection of student interaction activities and the evaluation of student seminars. This article introduces the implementation process of interactive content and interactive activities of the teaching system in the classroom, introduces the speech recognition framework of the MARS Chinese teaching system, uses the model principle, and compares the general speech data set test with the actual ARST model audio. The results show that, MAS model performance is more advantageous.

Nature 4.0: A networked sensor system for integrated biodiversity monitoring.

Ecosystem functions and services are severely threatened by unprecedented global loss in biodiversity. To counteract these trends, it is essential to develop systems to monitor changes in biodiversity for planning, evaluating, and implementing conservation and mitigation actions. However, the implementation of monitoring systems suffers from a trade-off between grain (i.e., the level of detail), extent (i.e., the number of study sites), and temporal repetition. Here, we present an applied and realized networked sensor system for integrated biodiversity monitoring in the Nature 4.0 project as a solution to these challenges, which considers plants and animals not only as targets of investigation, but also as parts of the modular sensor network by carrying sensors. Our networked sensor system consists of three main closely interlinked components with a modular structure: sensors, data transmission, and data storage, which are integrated into pipelines for automated biodiversity monitoring. We present our own real-world examples of applications, share our experiences in operating them, and provide our collected open data. Our flexible, low-cost, and open-source solutions can be applied for monitoring individual and multiple terrestrial plants and animals as well as their interactions. Ultimately, our system can also be applied to area-wide ecosystem mapping tasks, thereby providing an exemplary cost-efficient and powerful solution for biodiversity monitoring. Building upon our experiences in the Nature 4.0 project, we identified ten key challenges that need to be addressed to better understand and counteract the ongoing loss of biodiversity using networked sensor systems. To tackle these challenges, interdisciplinary collaboration, additional research, and practical solutions are necessary to enhance the capability and applicability of networked sensor systems for researchers and practitioners, ultimately further helping to ensure the sustainable management of ecosystems and the provision of ecosystem services.

Dynamic switched periodic event‐triggered control systems under denial‐of‐service attacks

AbstractPeriodic event‐triggered control (PETC) can save network bandwidth and sensor energy at the same time; therefore, it is a promising approach for applications, especially large scale plants, such as smart water distribution systems. This work extend PETC by considering denial of service (DoS) attacks. For the PETC, a dynamic variable is introduced with a switched strategy. Different from other dynamic variables, our variable keeps store energy when there is no attacks and only release energy when attacks happen. Thus, the inter‐event intervals when attacks happen can be prolonged while guarantees the same stability and performance. If the variable equals to 0 before the ending of the attacks, then characteristic of the attack duration is analyzed to guarantee a slower Lyapunov convergence rate. The presented approach is verified via a numerical example from smart water distribution systems.

Smart sensors and Internet of Things (IoT) for sustainable environmental and agricultural management

With the advancement of smart sensors and the Internet of Things, innovative technologies for environmental monitoring and rural installations have become solutions to increase production sustainably. Thus, this work analyzes the application of smart sensors and the Internet of Things (IoT) in the environmental monitoring of rural facilities, aiming to promote sustainability and efficiency in agricultural production. Smart sensors can collect environmental data in real-time, which is essential in rural environments. IoT enables communication and integration of this data. These technologies bring several benefits, such as improvements in efficiency, productivity, and sustainability. The study addresses types of sensors used in monitoring, such as optical, acoustic, chemical, and physical, in addition to monitored parameters, such as air and soil quality. Emerging technologies are also explored, including wireless network sensors, multispectral monitoring, microsatellites, blockchain, and virtual reality. The review includes case studies on successful applications of IoT and sensors in contexts such as smart irrigation, precision agriculture, and animal facility monitoring. It is concluded that these technologies have a high potential to contribute to more sustainable agricultural systems and animal production. Challenges such as privacy, security, and ethical use of data are highlighted.

An approach to using the AQI components in urban air pollution sources identifying

Problem statement. The fact that the air we breathe is polluted is well known. There are many sources of pollution, especially in big cities. Various sensors are installed to monitor pollutants in the air. One of the global systems for registering the concentration of pollutants in urban air is AQI. Air quality monitors collect data of five major air pollutants – ground-level ozone, particle pollution, carbon monoxide, sulfur dioxide, and nitrogen dioxide, that then convert to the Air Quality Index. And although the sensors themselves are not capable of reducing pollution in the air, based on the data they provide, it is possible to create indicative maps of urban pollution. Such mapping of urban areas will enable authorities to develop and implement plans to improve the most dangerous areas, as is already done in other countries. But there are still no such maps for Kyiv. Study objective is to analyse the air quality index in Kyiv, identify the main source of atmospheric pollution and to visualize urban air pollution. Methodology involves data analysis from 15 sensors of the AQI worldwide network. We analyzed the concentration of 5 main air pollutants contributed to the common Air Quality Index for a certain period in Kyiv and its surroundings. Knowing the exact coordinates of each sensor and the results of their measurements, we drew a map of air pollution in Kyiv using OriginPro 8.1 software and images from the Google maps. Research results. It was determined that the largest contribution to the Air Quality Index is made by the fine particulate matter emissions. We determined that the morning sensor data on the amount of dust in the air is the most informative. It is known transportation is one of the main sources of PM2.5 in the city. Our map clearly shows that the area with the highest AQI value coincides with a major road junction on the north-west outskirts of the city. Thus, atmospheric pollution in Kyiv is mainly determined by the amount of fine dust in the air. Further research will be aimed at identifying the relationship between the amount of PM2.5 in the air and the morphological parameters of indicator plants. Scientific novelty of the research. We showed for the first time that air pollution does not coincide with the official sources of atmospheric pollution given by the Kyiv Bureau of Technical Supervision. We also presented new approach to draw up-to-date, representative, and accurate pollution maps that can be submitted to the representatives of environmental services and other interested parties. Such investigations are of great importance as they can give the opportunity to the government to take real actions on pollutants reducing.