Sensor Network For Monitoring Research Articles

Metrology for sensor networks: metrological traceability and measurement uncertainties for air quality monitoring

Abstract In situ calibration of sensors delivering SI traceable measurement results still provides an open question to the design and operation of sensor networks. Particularly when addressing low-cost sensors, currently, the use of sensor networks for air quality monitoring is limited by the low or unknown accuracy of measurements that they can achieve, while the data quality of individual sensor networks is mainly derived by algorithms. Standardization bodies like DIN and CEN therefore announced the need for investigations of validation methods on gas phase species and particulate matter on the one hand side, and for the development of fully digitized quality assurance/quality control and calibration techniques for sensor networks on the other (CEN/CENELEC, Opportunity for Standardisation to Contribute to the European Partnership on Metrology EPM under Horizon Europe). This contribution concentrates on the metrological traceability of sensor networks for air quality monitoring to the international system of units (SI) based on FAIRified intra-network communications (M. Wilkinson, et al., “The FAIR guiding principles for scientific data management and stewardship,” Sci. Data, vol. 3, 2016, Art. no. 160018) and including delocalized Optical Gas Standards operated according to the digital TILSAM method (O. Werhahn, et al., The TILSAM Method Adapted into Optical Gas Standards – Complementing Gaseous Reference Materials, PTB Open Access Repository, 2021). Informed by related activities in EURAMET (Partnership project FunSNM, EMNs COO & POLMO, TC-IM 1551) (European Metrology Network Climate and Ocean Observation (COO), European Metrology Network Pollution Monitoring (POLMO), EURAMET Project TC-IM 1551, Project Database) this contribution discusses the importance of measurement uncertainties in the context of sensor networks, comprising different sensor principles and promoting an efficient uptake of state-of-the-art methods. We discuss how the sensor network case can be addressed with sensors individually using the GUM principles (Joint Committee for Guides in Metrology, Guide to the Expression of Uncertainty in Measurement (GUM), JCGM 100: 2008 (E)). For sensor network measurements becoming metrologically traceable to the SI, documented and unbroken chains of calibrations need to be implemented each contributing to the measurement uncertainty. This applies to each individual sensor of the network including the potential gold standard among them, but also to the network’s output viewed as a single entity. The contribution provides first approaches to be tested and validated that are underpinned by fundamental design strategies for sensor networks. It follows on practical applications in real world scenarios aside from model uncertainties discussed in artificial intelligence prospects.

Design of a low power consumption wireless sensor network for structural monitoring of bridges

Design of a low power consumption wireless sensor network for structural monitoring of bridges

Region Based Cluster Aided Routing Protocol for Environment Monitoring in Heterogeneous Wireless Sensor Networks

Heterogeneous wireless sensing networks (HWSNs) that are limited by their batteries' power consumption might benefit greatly from more efficient routing algorithms. It is essential that routing accounts for the diversity of network nodes to achieve optimal performance. This letter considers sensor nodes with random initial energies and random disparities in data output rate (traffic) to build a realistic clustering-based WSN suitable for heterogeneous sensing applications. The protocol divides the space into numerous zones with different distance thresholds to deal with the hotspot problem that the multi-hop method creates. The advantages of region-divided routing ensure that only qualified nodes compete for the role of cluster's head during the choice phase and that the cluster heads with the greatest remaining energy in the extremely energetic area are selected as the relay node throughout the inter-cluster routing that includes several hops phase. The simulation findings demonstrate that the protocol may efficiently equalize energy consumption throughout the network, eliminate the issue of "hot spots," be used in an energy-diverse system, and lengthen the service life of the network.

3D printed sub-terahertz photonic crystal for wireless passive biosensing

Monitoring pathogens has become a major challenge for society and research in recent years. Of great interest are refractive index sensors, which are based on the interaction between analytes and electromagnetic waves and allow label-free and fast detection. In addition, the electromagnetic waves can be exploited for wireless communication. However, current refractive index biosensors can only be read from a few centimeters. Here, we demonstrate an innovative concept of a passive wireless sensor based on a sub-terahertz photonic crystal resonator. The fabricated sensors have a reading range of up to 0.9 m and elevation and azimuth acceptance angles of around 90°. We demonstrate the stand-off detection of sub-µm thin-film proteins as test analytes. The proposed wireless sensor opens the door to a non-electronic, compact, and low-cost solution and can be extended to a wireless sensor network monitoring airborne pathogen, which may provide a pre-infection detection to prevent their spread efficiently.

An efficient dual layer data aggregation scheme in clustered wireless sensor networks

AbstractIn wireless sensor network (WSN) monitoring systems, redundant data from sluggish environmental changes and overlapping sensing ranges can increase the volume of data sent by nodes, degrade the efficiency of information collection, and lead to the death of sensor nodes. To reduce the energy consumption of sensor nodes and prolong the life of WSNs, this study proposes a dual layer intracluster data fusion scheme based on ring buffer. To reduce redundant data and temporary anomalous data while guaranteeing the temporal coherence of data, the source nodes employ a binarized similarity function and sliding quartile detection based on the ring buffer. Based on the improved support degree function of weighted Pearson distance, the cluster head node performs a weighted fusion on the data received from the source nodes. Experimental results reveal that the scheme proposed in this study has clear advantages in three aspects: the number of remaining nodes, residual energy, and the number of packets transmitted. The data fusion of the proposed scheme is confined to the data fusion of the same attribute environment parameters.

Transforming construction: The growing role of mechatronics in building our infrastructure

Mechatronics, the synergistic integration of mechanical engineering, electrical engineering, control engineering, and computer science, is progressively transforming the construction industry. This paper explores how mechatronic technologies are being applied across the construction lifecycle to enhance productivity, quality, safety, sustainability, and cost-effectiveness. Core mechatronic technologies changing construction include automated material handling systems, advanced construction robotics, sensor networks for monitoring, laser scanning for accurate modeling and control, and building information modeling (BIM) software. Mechatronics offers multifaceted benefits spanning from enhanced labor productivity and construction velocity to superior quality control, safety, and process optimization. However, realizing the immense potential of mechatronics in construction requires overcoming key challenges including high upfront costs, lack of technical skills, organizational resistance, integration difficulties, and reliability concerns. With thoughtful leadership and implementation, mechatronics promises to revolutionize construction, shifting it towards more automated, streamlined, and optimized techniques. This paper delineates the tremendous potential of mechatronics in construction regarding benefits, technologies, and challenges. The outlook is promising for intelligent mechatronic systems to profoundly enhance productivity, quality, and safety as construction progresses towards greater automation and optimization.

Landslide monitoring and prediction system using geosensors and wireless sensor network

Landslides in hilly regions are a frequently occurring natural phenomenon which takes a heavy toll of human lives and causes damage to different properties. Hence, prediction of landslide is essential for averting its deleterious effects by providing early warning to neighboring residents about the impending hazard of landslide. A landslide monitoring system has been designed using geosensors and high-range wireless network for on-line monitoring and prediction of landslides with the application of multivariate statistical analysis of prevailing site parameters. The system consists of various geosensors, wireless sensor network, server, and landslide monitoring and forecasting software. Crackmeter, in-place inclinometer, raingauge, tiltmeter, piezometer and other sensors are set up in the selected hill slope prone to landslide for continuous monitoring of influencing parameters. The system measures real-time landslide parameters using the said geosensors connected with wireless nodes and establishing a dynamic wireless network to overcome redundancy issue using wireless nodes of around 1200 m communication range using a high performance low power microcontroller, integrated solar panel and additional external omni-directional antenna for monitoring landslide in a large and hazardous hilly region from a distant safer location. The application software consists of different modules, namely data monitoring, analysis, storing, viewing, prediction, and generation of audio-visual, SMS and email alerts for 3 levels of landslides situations. The paper enumerates the system architecture and the application software details.Graphical

Artificial Intelligence-Assisted RFID Tag-Integrated Multi-Sensor for Quality Assessment and Sensing.

Radio frequency identification (RFID) is well known as an identification, track, and trace approach and is considered to be the key physical layer technology for the industrial internet of things (IIoT). However, IIoT systems have to introduce additional complex sensor networks for pervasive monitoring, and there are still challenges related to item-level sensing and data recording. To overcome the shortage, this work proposes an artificial intelligence (AI)-assisted RFID-based multi-sensing technology. Both passive and semi-passive RFID tag-integrated multi-sensors are developed. The main contributions and the novelty of this investigation are as follows. A UHF RFID tag-integrated multi-sensor with a boosted charge pump is proposed; it enables high RF signal sensitivity and a long operational range. The whole hardware design, including the antenna and energy harvester, are studied. Moreover, a demonstration with real-world ham product sensing data is conducted. This work also proposes and successfully demonstrates the integration of machine learning algorithms, specifically the NARX neural network, with RFID sensing data for food product quality assessment and sensing (QAS). This application of machine learning to RFID-generated data for quality assessment is also a novel aspect of the research. The deployment of an autoregressive model with an exogenous input (NARX) neural network model, tailored for nonlinear processes, emerges as the most effective, achieving a root mean square error (RMSE) of 0.007 and an R-squared value of 0.99 for ham product QAS. By deploying the technology, low-cost, timely, and flexible product QAS can be achieved in manufacturing industries, which helps product quality improvement and the optimization of the manufacturing line and supply chain.

Multi-objective SHM sensor path optimisation for damage detection in large composite stiffened panels

This work proposes a novel methodology for the automatic multi-objective optimisation of sensor paths in structural health monitoring (SHM) sensor networks using archived multi-objective simulated annealing. Using all of the sensor paths within a sensor network may not always be beneficial during damage detection. Many sensor paths may experience significant signal noise, attenuation, and wave mode conversion due to the presence of features, such as stiffeners, and hence impair the detection accuracy of the overall system. Many paths will also contribute little to the overall coverage level or damage detection accuracy of the network and can be ignored, reducing complexity. Knowing which paths to include, and which to exclude, can require significant prior expert knowledge, which may not always be available. Furthermore, even when expert knowledge is considered, the optimum path selection might not be achieved. Therefore, this work proposes a novel automatic procedure for optimising the sensor paths of an SHM sensor network to maximise coverage level, maximise damage detection accuracy and minimise the overall signal noise in the network due to geometric features. This procedure was tested on a real-world large composite stiffened panel with many geometric features in the form of frames and stiffeners. Compared to using all of the available sensor pairs, the optimised network exhibits superior performance in terms of detection accuracy and overall noise. It was also found to provide very similar performance, in terms of coverage level and overall signal noise, to a sensor path network designed based on prior expert knowledge but provided up to 35% higher damage detection accuracy. As a result, the novel procedure proposed in this work has the capability to design high-performing SHM sensor path networks for structures with complex geometries but without the need for prior expert knowledge, making SHM more accessible to the engineering community.

5G NR launching in Greece: Preliminary in situ and monitoring network measurements of electromagnetic fields exposure levels at rooftops.

In Greece, 5G New Radio (NR) has started launching in the end of 2020, at the 3400-3800 MHz (FR1) frequency band. Focusing on 117 Base Stations (BSs) which were already equipped with 5G NR antennas, in situ broadband and frequency selective measurements have been conducted at minimum three points of interest, at adjacent rooftops (when accessible). The points have been selected according to the sweeping method and the electric field strength (E) value has been stored on the selected worst-case scenario point. Spectrum analysis was conducted in the FR1, for the allocated spectrum that corresponds to each mobile communication provider, in order to get preliminary results concerning the contribution of the 5G NR emissions in the general public exposure levels. The vast majority of the in situ measurements has been conducted in urban environments from the beginning of 2021 until the mid of 2022, since in Greece 5G NR services launching started from the big cities. Additionally, a 5G NR BS, installed in a suburban environment (in the city of Kalamata) is thoroughly investigated during its pilot and regular operation, based on broadband and frequency selective measurements data derived by the National Observatory of Electromagnetic Fields (NOEF) monitoring sensor network. Insitu measurement data within the 5G NR frequency range are verified via the NOEF's output. The 5G NR contribution to the total E-field levels is assessed in time, from pilot to regular operation of the BS. In all cases, compliance with the reference levels for general public exposure is affirmed.

Synergistic Integration of Nanogenerators and Solar Cells: Advanced Hybrid Structures and Applications

AbstractThe rapid growth of global energy consumption and the increasing demand for sustainable and renewable energy sources have urged vast research into harnessing energy from various sources. Among them, the most promising approaches are nanogenerators (NGs) and solar cells (SCs), which independently offer innovative solutions for energy harvesting. This review paper presents a comprehensive analysis of the integration of NGs and SCs, exploring advanced hybrid structures and their diverse applications. First, an overview of the principles and working mechanisms of NGs and SCs is provided for seamless hybrid integrations. Then, various design strategies are discussed, such as piezoelectric and triboelectric NGs with different types of SCs. Finally, a wide range of applications are explored that benefit from the synergistic integration of NGs and SCs, including self‐powered electronics, wearable devices, environmental monitoring, and wireless sensor networks. The potential for these hybrid systems is highlighted to address real‐world energy needs and contribute to developing sustainable and self‐sufficient technologies. In conclusion, this review provides valuable insights into the state‐of‐the‐art developments in NGs and SCs integration, shedding light on advanced hybrid structures and their diverse applications.

A digital twin-based energy-efficient wireless multimedia sensor network for waterbirds monitoring

Wetlands play a critical role in maintaining the global climate, regulating the hydrological cycle, and protecting human health. However, they are rapidly disappearing due to human activities. Waterbirds are valuable bio-indicators of wetland health, but it is challenging to monitor them effectively. Wireless Multimedia Sensor Networks (WMSNs) offer a promising technology for monitoring wetlands. Nonetheless, these networks are constrained in terms of energy, and also encounter challenges associated with large-scale deployments under natural environmental conditions. These conditions introduce harsh circumstances that may not have been anticipated during the pre-deployment testing phase. This paper proposes a Digital Twin (DT) based energy-efficient WMSN monitoring system specifically tailored for waterbirds in wetlands. The system utilizes a unique approach that combines local audio identification and image compression with DT technology to optimize network performance and minimize energy consumption. To reduce unnecessary image transmissions, the system employs a real-time, low-complexity local audio identification phase before triggering image capture. A denoising step is employed to achieve highly accurate bird recognition despite surrounding noises. Each image undergoes a low-complexity compression scheme prior to transmission, further enhancing energy efficiency. To enhance the system’s overall efficiency and effectiveness, DT technology is integrated to create real-time replicas of the WMSN and the monitoring application. A synergistic interaction between the two DTs enables cooperative data-making decision that ensures both QoS (Quality of Service) and QoE (Quality of Experience) requirements are met. Transmission rate control is done using a fuzzy logic decision-making technique. Real-time feedback provides rapid and accurate analysis of the current state of the WMSN, allowing for dynamic adjustments. The ”what-if scenarios” feature of the implemented DTs has been effectively leveraged to find the most suitable settings for the controller. The effectiveness and performance enhancements achieved by integrating DT into our WMSN-based surveillance system are validated through comprehensive experiments in scenarios that correspond to a real-world wetland. Comparative analyses demonstrate the undeniable benefits of the DT-integrated system compared to a conventional WMSN-based surveillance setup. In particular, the results demonstrate the system’s superior performance in terms of energy efficiency, real-time monitoring capabilities, and ability to handle multiple video sources.

Net Zero Dairy Farming—Advancing Climate Goals with Big Data and Artificial Intelligence

This paper explores the transformative potential of Big Data and Artificial Intelligence (AI) in propelling the dairy industry toward net zero emissions, a critical objective in the global fight against climate change. Employing the Canadian dairy sector as a case study, the study extrapolates its findings to demonstrate the global applicability of these technologies in enhancing environmental sustainability across the agricultural spectrum. We begin by delineating the environmental challenges confronting the dairy industry worldwide, with an emphasis on greenhouse gas (GHG) emissions, including methane from enteric fermentation and nitrous oxide from manure management. The pressing need for innovative approaches in light of the accelerating climate crisis forms the crux of our argument. Our analysis delves into the role of Big Data and AI in revolutionizing emission management in dairy farming. This includes applications in optimizing feed efficiency, refining manure management, and improving energy utilization. Technological solutions such as predictive analytics for feed optimization, AI in herd health management, and sensor networks for real-time monitoring are thoroughly examined. Crucially, the paper addresses the wider implications of integrating these technologies in dairy farming. We discuss the development of benchmarking standards for emissions, the importance of data privacy, and the essential role of policy in promoting sustainable practices. These aspects are vital in supporting the adoption of technology, ensuring ethical use, and aligning with international climate commitments. Concluding, our comprehensive study not only suggests a pathway for the dairy industry towards environmental sustainability but also provides insights into the role of digital technologies in broader agricultural practices, aligning with global environmental sustainability efforts.

Retracted: Wireless Intelligent Sensor Network in Dynamic Environmental Monitoring of Archaeological Excavation Site

Retracted: Wireless Intelligent Sensor Network in Dynamic Environmental Monitoring of Archaeological Excavation Site

In the Direction of an Artificial Intelligence-Enabled Monitoring Platform for Concrete Structures.

In a seismic context, it is fundamental to deploy distributed sensor networks for Structural Health Monitoring (SHM). Indeed, regularly gathering data from a structure/infrastructure gives insight on the structural health status, and Artificial Intelligence (AI) technologies can help in exploiting this information to generate early warnings useful for decision-making purposes. With a perspective of developing a remote monitoring platform for the built environment in a seismic context, the authors tested self-sensing concrete beams in loading tests, focusing on the measured electrical impedance. The formed cracks were objectively assessed through a vision-based system. Also, a comparative analysis of AI-based and statistical prediction methods, including Prophet, ARIMA, and SARIMAX, was conducted for predicting electrical impedance. Results show that the real part of electrical impedance is highly correlated with the applied load (Pearson's correlation coefficient > 0.9); hence, the piezoresistive ability of the manufactured specimens has been confirmed. Concerning prediction methods, the superiority of the Prophet model over statistical techniques was demonstrated (Mean Absolute Percentage Error, MAPE < 1.00%). Thus, the exploitation of electrical impedance sensors, vision-based systems, and AI technologies can be significant to enhance SHM and maintenance needs prediction in the built environment.

Flexible Sensing Enabled Nondestructive Detection on Viability/Quality of Live Edible Oyster.

Environmental and physiological fluctuations in the live oyster cold chain can result in reduced survival and quality. In this study, a flexible wireless sensor network (F-WSN) monitoring system combined with knowledge engineering was designed and developed to monitor environmental information and physiological fluctuations in the live oyster cold chain. Based on the Hazard Analysis and Critical Control Point (HACCP) plan to identify the critical control points (CCPs) in the live oyster cold chain, the F-WSN was utilized to conduct tracking and collection experiments in real scenarios from Yantai, Shandong Province, to Beijing. The knowledge model for shelf-life and quality prediction based on environmental information and physiological fluctuations was established, and the prediction accuracies of TVB-N, TVC, and pH were 96%, 85%, and 97%, respectively, and the prediction accuracy of viability was 96%. Relevant managers, workers, and experts were invited to participate in the efficiency and applicability assessment of the established system. The results indicated that combining F-WSN monitoring with knowledge-based HACCP modeling is an effective approach to improving the transparency of cold chain management, reducing quality and safety risks in the oyster industry, and promoting the sharing and reuse of HACCP knowledge in the oyster cold chain.

Federated learning-based anomaly detection for environment monitoring sensor networks

Federated learning-based anomaly detection for environment monitoring sensor networks

Several Categories of Energy Harvested Routing Protocols, Challenges, and Characteristics in WSN: A Review

The routing protocol is a technique for determining the most efficient channel for data transmission. The route selection procedure, which relies on the kind of network, channel conditions, and measurement systems, presents several challenges. Routing is essential in Wireless Sensor Networks (WSNs) for environmental monitoring, traffic monitoring, and other applications. WSNs are small nodes that can sense, interpret data, and communicate wirelessly. Many routing, power control, and data dissemination techniques have been developed specifically for WSNs, where energy efficiency is a crucial design factor.&#x0D; On the other hand, the focus has been on energy harvesting and standard routing methods, which can vary depending on the design and network architecture. In a Wireless Sensor Network (WSN), the data collected by the sensor nodes is typically transferred to the base station, which connects the sensor network to other networks (such as the internet), where it is processed and necessary action is taken. WSN has recently been developed to allow various applications, including traffic enforcement building automation, smart warfare, environmental sensing, and many more.WSN integrates several sensors or nodes deployed around a specific node to perform computational processes.

Green IoT Event Detection for Carbon-Emission Monitoring in Sensor Networks.

This research addresses the intersection of low-power microcontroller technology and binary classification of events in the context of carbon-emission reduction. The study introduces an innovative approach leveraging microcontrollers for real-time event detection in a homogeneous hardware/firmware manner and faced with limited resources. This showcases their efficiency in processing sensor data and reducing power consumption without the need for extensive training sets. Two case studies focusing on landfill CO2 emissions and home energy usage demonstrate the feasibility and effectiveness of this approach. The findings highlight significant power savings achieved by minimizing data transmission during non-event periods (94.8-99.8%), in addition to presenting a sustainable alternative to traditional resource-intensive AI/ML platforms that comparatively draw and produce 20,000 times the amount of power and carbon emissions, respectively.

Additively manufactured magic cube platforms for fully integrated wireless sensing nodes for Internet of Things applications

Wireless sensor networks for environmental monitoring are a key feature in developing the Internet of Things. Although there has been much research in developing components for wireless sensing nodes, advances in creating fully integrated sensing nodes is limited. Furthermore, because most sensing nodes that have been developed are intended to perform a fixed task, each new effort to design an integrated sensing node with different functionality must start from scratch. Here we introduce a broadly applicable platform for the development and production of fully integrated wireless sensing nodes. The platform is an additively manufactured cube that has different subsystems occupying separate faces of the 3D structure. While both additively manufactured sensors and cube-shaped wireless sensing nodes have been previously reported, these two approaches have yet to be combined. A key technology that enables this is the use of additively manufactured, nonplanar bent microstrips. This realization offers a “plug-and-play” approach to sensor node design, as the subsystems are considered modular and can be swapped to alter the function of the device. Implementing this concept enables the rapid development and deployment of wireless sensor networks.