Sensor Network For Monitoring Research Articles

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.

Energy Management and Networking for Sensor-based Transmission Line State Monitoring

The transmission line condition monitoring sensor network is large in scale and difficult to maintain. Its long-chain network topology differs from general wireless sensor networks, so it is necessary to develop a self-organizing sensor network with self-energy harvesting. In this regard, a three-layer network model is designed. Three types of node models are constructed. The sensor nodes implement round-robin sleep/wake and energy management mechanisms, powered by self-harvested energy. Additionally, reliable routing between nodes is established. Simulation results show that the developed simulation model is suitable for the application of transmission line condition monitoring. The expected functions of each module have been realized, and the stable and reliable transmission of perceived data of key components of transmission lines has been achieved, thereby providing technical and data basis for the deployment of practical networks.

Study on smart grid fault detection based on ZigBee and MapX technologies

As the complexity of the power system and the increasing demand for electricity continue to rise, ensuring the reliability and stability of the power system becomes critically important. This study utilizes ZigBee communication modules to establish a sensor network for real-time monitoring of the power system's status and employs MapX technology to achieve geographic information visualization of the power system. Through an improved RBF-PCA-WFCM algorithm, grid data can be dimensionally reduced, enabling simulation training tailored to grid loads, resulting in more accurate algorithm analysis and optimization. This paper introduces fault localization and diagnostic methods, as well as clustering optimization algorithms, to achieve fault monitoring, analysis, and identification in the smart power grid. Research findings demonstrate that the intelligent power grid data analysis and fault detection system based on ZigBee and MapX technologies can enhance the reliability of the power system and better support the increasing demands of the evolving field of electricity.

Autonomous configuration establishment method of wireless network on condition monitoring for coupling and decoupling of railway vehicles

AbstractIn recent years, with the development of IoT, research and development of wireless sensor networks for monitoring has been advanced to monitor the condition of various devices in railway vehicles and notify train crews. In the operation of railway vehicles, the composition may be changed at stations by coupling and decoupling, and it is necessary to set up inter‐vehicle wireless network considering them. Therefore, to transmit sensor data in railway vehicles, we propose an autonomous configuration establishment method of wireless network for railway vehicles considering coupling and decoupling.

A systematic and comprehensive review and investigation of intelligent IoT-based healthcare systems in rural societies and governments

Healthcare needs in rural areas differ significantly from those in urban areas. Addressing the healthcare challenges in rural communities is of paramount importance, as these regions often lack access to adequate healthcare facilities. Moreover, technological advancements, particularly in the realm of the Internet of Things (IoT), have brought about significant changes in the healthcare industry. IoT involves connecting real-world objects to digital devices, opening up various possibilities for improving healthcare delivery. One promising application of IoT is its use in monitoring the spread of diseases in remote villages through interconnected sensors and devices. Surprisingly, there has been a noticeable absence of comprehensive research on this topic. Therefore, the primary objective of this study is to conduct a thorough and systematic review of intelligent IoT-based healthcare systems in rural communities and their governance. The analysis covers research papers published until December 2022 to provide valuable insights for future researchers. The selected articles have been categorized into three main groups: monitoring, intelligent services, and body sensor networks. The findings indicate that IoT research has garnered significant attention within the healthcare community. Furthermore, the results illustrate the potential benefits of IoT for governments, especially in rural areas, in improving public health and strengthening economic ties. It is worth noting that establishing a robust security infrastructure is essential for implementing IoT effectively, given its innovative operational principles. In summary, this review enhances scholars' understanding of the current state of IoT research in rural healthcare settings while highlighting areas that warrant further investigation. Additionally, it keeps healthcare professionals informed about the latest advancements and applications of IoT in rural healthcare.

Data-driven model for predicting indoor air quality and thermal comfort levels in naturally ventilated educational buildings using easily accessible data for schools

Modeling indoor air quality and thermal conditions in educational buildings is significant for protecting students' health, well-being, and productivity. The predictive models in existing studies were mainly built and applied in controlled environments with HVAC systems. These models did not involve occupant-related factors, had limited scope in a single building or space, and required indoor environmental monitoring data for the model input. This limits the applicability and generalization ability of the model in a large number of schools relying on natural ventilation, where indoor air quality and thermal conditions are significantly affected by occupants’ activities and ventilation practices. Hence, this paper proposes a methodology to develop a data-driven model for predicting the level of indoor air quality and thermal comfort in naturally ventilated educational buildings, and identifies the key influential factors. The model was developed using the class-weighted random forest algorithm with data collected from a large measurement campaign. The developed model demonstrated good accuracy, generalization ability, and robustness. The analysis concluded that occupancy, windows and doors operation, and outdoor environmental parameters are key factors must be involved, whereas building characteristics have no practical contribution to the prediction. The model inputs are easily accessible data for schools. Once the model is developed with data collected from an initial measurement campaign in representative educational buildings, it can be used in all local schools without requiring monitoring sensor networks, thereby rendering a “cost-effective” way of assessing indoor air quality and thermal comfort to help relevant stakeholders improve building management practices in schools.

Multilevel Adaptive Near-Lossless Compression in Edge Collaborative Wireless Sensor Networks for Mechanical Vibration Monitoring

To address the difficulties of severe lack of storage and computational resources and high delay of transmitting massive vibration data in wireless sensor networks (WSN) for mechanical vibration monitoring (MVM), this paper proposes a novel multilevel adaptive near-lossless compression in edge collaborative WSN (MANLC-ECWSN) for MVM, which could effectively solve the above problems. On the one hand, the sparse pattern of mechanical fault signals is analyzed. A multi-level adaptive near-lossless compression (MANLC) method is proposed to characterize mechanical fault feature information with high accuracy in low storage space, and the proposed method is implemented on the self-developed acquisition node (AN), which effectively improves the storage space and transmission efficiency. On the other hand, the edge computing (EC) technique is integrated into WSN, and data reconstruction and high-precision feature detection are efficiently implemented on the self-developed edge computing node (ECN), which effectively reduces the storage and computing pressure of the data center server. The comprehensive experiments demonstrate that high precision data reconstruction and feature detection could be achieved in the proposed approach from measurements that occupy a little storage space of the AN. The computational power and transmission efficiency of WSN are significantly improved, which provides a potential solution for practical engineering applications.

Noncooperative Topology Inference of Wireless Networks With Monitoring Sensors

With the widespread application of wireless networks, the importance of intelligent analysis of network behaviors is becoming increasingly prominent. In the analysis of networks behaviors, learning and reasoning about the connectivity of unknown networks is a fundamental problem. To obtain the topology information of a non-cooperative wireless network that could not be accessed by the monitoring sensors, we propose a topology inference algorithm based on the network two-dimensional spatio-temporal features (TDSTF). Specifically, the monitoring sensor network monitors the power of the non-cooperative network and locates the nodes of the non-cooperative network exploiting the neural network (NN)-based method. Then, the communication time and distance between the non-cooperative nodes are used as characteristics to infer the topology of the non-cooperative network based on K-Nearest Neighbors (KNN). Simulation results validate that the proposed TDSTF topology inference algorithm outperforms other topology inference algorithms that do not consider both spatial and temporal features and can greatly improve the inference accuracy.

Design and Optimization of Smart Campus Framework Based on Artificial Intelligence22

In this study, an artificial intelligence (AI)--based smart campus framework is built and optimized with the aim of improving user happiness, raising AI model performance, maximizing resource utilization, and promoting smart campus adoption. The study technique employs a mixed-methods approach that combines quantitative data analysis and qualitative user feedback in order to completely evaluate the effectiveness of the framework. Literature reviews, Questionnaires of 544, interviews of 56 persons, and observations are used to collect data on user satisfaction, AI model performance, optimization strategies, and adoption of smart campuses AI models are built using statistical methodology and AI techniques for performance evaluation. In the Smart Campus Framework based on Artificial Intelligence, we gathered the data by constructing IoT sensor networks for real-time monitoring and merging student data to provide insights into academic performance and student engagement. The findings indicate that, on average, users are satisfied, and the performance ratings for the AI models vary from 7.25 to 8.25. The smart campus framework is effective, as evidenced by the optimization metric's 7.53 average score. A score of 7.4 for smart campus adoption combines user knowledge, perceived utility, and perceived ease of use. The practical implications include better user experience, cost optimization, and smart campus architecture. Theoretical implications include the verification of the mixed-methods strategy and the creation of a framework for AI model optimization. The study's findings act as a model for upcoming smart campus research, spurring creativity and change in institutions of higher learning. The study’s limitations suggest that results can be generalized with minor contextual change and this is the biggest challenge for researchers and policy makers.