Smart Monitoring Research Articles

Smart Monitoring and Control System for Crude Oil Pipeline Vandalism

Crude oil pipeline vandalism, theft, and unauthorized interference present a significant challenge to the oil industry's security, profitability, and sustainability. Despite initial measures such as security patrols, traditional methods have proven inadequate, particularly in difficult terrains and aquatic regions. These methods often fail to promptly detect and prevent vandalism and theft, which results in significant economic, environmental, and safety risks. This study developed and implemented a smart monitoring and control system to address these challenges by improving the detection of oil pipeline vandalism, including incidents in aquatic environments. The Prototyping methodology was adopted, allowing for continuous testing and improvements throughout the development process. The system was built using C#.NET under the .NET Framework, with Microsoft SQL Server as the database, incorporating an encryption layer for database security. To enhance its effectiveness, an oil theft detection algorithm was integrated with a web-based interface to facilitate real-time monitoring, visualization, and reporting. Testing scenarios demonstrated that the system successfully detected threats and ensured seamless data flow from sensors to the central system. This solution marks a significant advancement in the security and sustainability of oil infrastructure, contributing to environmental protection and the safety of stakeholders

A Comprehensive Review of Sensor-Based Smart Building Monitoring and Data Gathering Techniques

In an era where buildings are increasingly becoming multifaceted entities, the paradigm of smart buildings has witnessed significant evolution. This advancement integrates sophisticated communication technologies, the Internet of Things (IoT), artificial intelligence (AI), and data analytics. Intending to design an effective smart building monitoring system, this research paper explores and compares various solutions for measuring building parameters by identifying a broad spectrum of review articles considering building occupant behavior, sensor deployment, and implementation complexity. The objective of our paper is to compile diverse information on various sensors used for monitoring building conditions and provide a comprehensive overview of data structuring and processing, all within a single article. Additionally, this paper addresses the challenges of combining data from decentralized systems and the need for managerial tools to optimize user experiences. The findings contribute to the advancement of smart building management, offering valuable insights for improving building performance and user experience as well as evaluating future research directions in this field. This review is designed to serve as an introduction for anyone venturing into the field of building monitoring.

Information-Centric IoT-Based Smart Home Control and Monitoring System

Information-Centric IoT-Based Smart Home Control and Monitoring System

Iontophoresis and electroporation-assisted microneedles: advancements and therapeutic potentials in transdermal drug delivery.

Transdermal drug delivery (TDD) using electrically assisted microneedle (MN) systems has emerged as a promising alternative to traditional drug administration routes. This review explores recent advancements in this technology across various therapeutic applications. Integrating iontophoresis (IP) and electroporation (EP) with MN technology has shown significant potential in improving treatment outcomes for various conditions. Studies demonstrate their effectiveness in enhancing vaccine and DNA delivery, improving diabetes management, and increasing efficacy in dermatological applications. The technology has also exhibited promise in delivering nonsteroidal anti-inflammatory drugs (NSAIDs), treating multiple sclerosis, and advancing obesity and cancer therapy. These systems offer improved drug permeation, targeted delivery, and enhanced therapeutic effects. While challenges remain, including safety concerns and technological limitations, ongoing research focuses on optimizing these systems for broader clinical applications. The future of electrically assisted MN technologies in TDD appears promising, with potential advancements in personalized medicine, smart monitoring systems, and expanded therapeutic applications.

A smart, multi-configuration, and low-cost system for water turbidity monitoring

Measuring the amount of suspended sediment in rivers is important for a better understanding of phenomena like soil erosion or floods. Since such phenomena are not spatially homogeneous, a distributed measurement approach is required to obtain fine-grained and realistic models. We designed and implemented a low-cost, flexible, and smart suspended sediment concentration monitor. The system only uses components that are easy to find and assemble. This, together with the reduced costs, enables the use of multiple instances for distributed monitoring. The system can operate according to different configurations, fixed or mobile, and using a variety of connection technologies, to possibly cover the various necessities that arise from the diverse installation sites. Each measuring unit automatically retrieves its operational parameters, easing the configuration of a possibly large distributed measurement system.

A hierarchical multistage holistic model for acoustic emission source monitoring in composites

Abstract This paper introduces a multistage smart structural health monitoring (SHM) model for carbon-fiber composites, with a focus on multiple types of acoustic emission (AE) source localization and classification. The SHM model uses time-frequency data from various AE events (such as tool drops, impact, and artificial debonding) across different zones of a composite structure. The SHM strategy demonstrates a robust smart monitoring of composites with high accuracy. Further, a hypothesis testing has been carried out that supports the superiority of a 2-stage identification process, revealing statistically significant higher accuracy and confidence intervals across all zones and AE source types. This research establishes a novel framework for solving a hierarchical multistage holistic damage source identification problem, offering robustness in identifying various damage scenarios and quantifying associated prediction uncertainties.

The next-generation of metaverse embodiment interaction devices: A self-powered sensing smart monitoring system

Wearable electronic devices collect user data to provide personalized experiences and real time interactions for the metaverse, thereby driving its development. This paper introduces an innovative self-powered sensing smart monitoring system (SSSMS) designed to harvest human low-frequency inertial energy and achieve gait recognition and fall monitoring (GR-FM) capabilities. The SSSMS enhances energy harvesting efficiency through Halbach electromagnetic enhancement mechanisms, achieving self-sustainability. Additionally, the conical roller triboelectric nanosensor (CR-TENS) accurately captures various human gaits, transmitting these states in the form of electrical signals to a computer via OpenBCI in real time. These signals are integrated with a deep learning-driven diagnostic module to achieve GR-FM. The experimental results demonstrate that under optimal external excitation, the output power is 2.27 mW, with a power density of 32 W/m3. After 100,000 cycles, both the EMG and CR-TENS modules still maintain good performance, sufficiently providing stable and continuous charging for the battery and offering a stable signal for signal processing. Both the EMG and CR-TENS modules exhibit robust feature capture capabilities, effectively sensing different excitation frequencies, amplitudes, and gait patterns. The SSSMS based on the GRU deep learning model achieves recognition accuracies of 96.13 %, 96.60 %, and 92.22 % for frequency, amplitude, and gait, respectively. Deployment experiments demonstrate high accuracy and sensitivity in frequency, amplitude, gait recognition, and fall monitoring, highlighting the advantageous design of EMG and CR-TENS for perceiving subtle motion state changes. The SSSMS demonstrates outstanding capabilities in self-sustainability and real time user embodiment information capture, showcasing its potential as a next-generation interaction device for the metaverse.

Combining Artificial Neural Networks and Mathematical Models for Unbalance Estimation in a Rotating System under the Nonlinear Journal Bearing Approach

Rotating systems are essential components and play a critical role in many industrial sectors. Unbalance is a very common and serious fault that can cause machine downtime, unplanned maintenance, and potential damage to vital rotating machines. Accurately estimating unbalance in rotor–bearing systems is crucial for ensuring the reliable and efficient operation of machinery. This research paper presents a novel approach utilizing artificial neural networks (ANNs) to estimate the unbalance masses in a multidisk system based on simulation data from a nonlinear rotor–bearing system. Additionally, this study explores the effect of various operating parameters on oil film stability and vibration response through a combination of bifurcation diagrams, spectrum cascades, Poincare maps, and orbit and FFT plots. This study demonstrates the effectiveness of ANNs for unbalance estimation in a fast and accurate way and discusses the potential of ANNs in smart online condition monitoring systems.

Self-assembled conductive coating and bifacial microstructures Collaboratively Adjusting the sensing performance and high reliability of the flexible pressure sensors

Flexible pressure sensors are being studied intensively for applications such as smart wearables and health monitoring. While ensuring the excellent sensing performance, the reliability (durability and environmental resistance) of the sensors are also key indicators that determine whether they can be used in practice. In this research, a strategy to adjust the sensing performance and high reliability of the flexible pressure sensors by self-assembled conductive coating and bifacial microstructures (BM) is proposed. Polydimethylsiloxane/carbon nanotube films with BM are first modified to reduce the surface contact angle. Then poly(dimethyl diallyl ammonium chloride) (PDAC) and hydroxylated carbon nanotubes/carboxylated cellulose nanofibers/glycerol (CNT-OH/CNF-C/GL) are successively assembled on the BM surface to construct conductive coatings (PCCG). The sensor based on PCCG and BM (the PCCG-BM sensor) shows a high sensitivity (9.97 kPa−1), a wide detection range (0–330 kPa), and a fast response time (59 ms). The PCCG-BM sensor has excellent repeatability, and it still outputs stable current change signals after 240,000 pressure cycles. What’s more, the PCCG-BM sensor is treated with extreme environments such as bending, stretching, washing, and high pressure, and the sensor still maintains excellent performances. This preparation strategy provides a new perspective for the fabrication of high-performance and high-reliability flexible sensors.

An Invisible Dermal Nanotattoo-Based Smart Wearable Sensor for eDiagnostics of Jaundice.

Despite substantial progress in the diagnosis of jaundice/hyperbilirubinemia as the most common disease and cause of hospitalization of newborns, on the eve of Industry/Healthcare 5.0, the development of accurate and reliable wearable diagnostic sensors for noninvasive smart monitoring of bilirubin (BIL) is still in high demand. Aiming to fabricate a smart wearable sensor for early diagnosis of neonatal jaundice and its therapeutic monitoring, we here report a fluorescent dermal nanotattoo that further coupled with an IoT-integrated wearable optoelectronic reader for minimally invasive, continuous, and real-time monitoring of BIL in interstitial fluid. Selective recovery of quenched fluorescence of the dermal tattoo sensor, composed of biocompatible dissolving/hydrogel microneedles loaded with fluorescent carbon quantum dots, upon blue light exposure used for jaundice phototherapy was utilized for highly selective BIL sensing. The fascinating features of our developed smart wearable tattoo sensor and its successful results with high correlation with blood BIL results make it a highly promising sensor for easy, minimally invasive, reliable, and smart eDiagnostics and continuous therapeutic eMonitoring of jaundice and other BIL-induced diseases at the point of care. We envision that the developed nanotattoo sensing bioplatform will inspire the development of future smart tattoo sensors in various diagnostic and monitoring scenarios.

Clinical Validation of a Prototype Smart Non-Invasive Pregnancy Glucose Monitor

The aim of this study is to evaluate the effectiveness of a smart non-invasive blood glucose monitor prototype during pregnancy through clinical validation. The monitor utilizes near-infrared spectroscopy combined with AI big data analysis of photoelectric volumetric pulse wave data to achieve non-invasive monitoring of blood glucose in women during pregnancy. The research team developed a monitor that employs a sensing chip, effectively overcoming the problems of weak signals and individual differences in non-invasive blood glucose monitoring. The user experience is enhanced by visualizing the test results on the accompanying cell phone APP (application) of the smart non-invasive pregnancy blood glucose monitor. Clinical validation revealed that the non-invasive monitoring data for pregnant women aged 20~30 years significantly differed from those obtained via traditional blood glucose measurement methods, whereas no significant difference ( P<0.05) was observed for pregnant women aged 31~42 years. The study concluded that further calibration of the monitor and an expansion of the sample size are necessary to enhance consistency with invasive glucose monitoring results.

Smart Monitoring of Microgrid-Integrated Renewable-Energy-Powered Electric Vehicle Charging Stations Using Synchrophasor Technology

With the growing concern over climate change and energy security, the Government of India expedited enhancing the share of renewable energy (RE) derived from solar, wind and biomass sources within the energy blend. In this paper, a techno-economic and environmental analysis of a microgrid-integrated electric vehicle charging stations fueled by renewable energy is proposed for a typical area in the State of Karnataka, South India. The power transaction with the grid and the sell-back price to the national grid were investigated. Carbon emissions were also assessed, and 128,406 CO2 kg/Yr can be saved in the grid-connected mode. Also, in this work, different scenarios such as injecting active power, reactive power, and active and reactive power, and injecting active and absorbing reactive power to the grid are comprehensively assessed. Out of four types, type 3 (inject real and reactive power) provides significant reduction in power losses by up to 80.99%. The synchrophasor-technology-based monitoring method is adopted in order to enhance the microgrid system’s overall performance. The execution times for different cases with distributed generators (DGs) and electric vehicle charging stations (EVCSs) for conventional systems and micro-phasor measurement units (µPMU) were observed to be 19.07 s and 5.64 s, respectively, which is well accepted in the case of online monitoring.

Revealing the Impact of Composition on Oil Monitoring Performance of Ni-Ti-Cu Coated Optical Fiber

Shape Memory Alloy-coated optical fiber provides an economical, Smart, and real-time monitoring technological solution for Industry 4.0. In this work, the compositional influence of Ni-Ti-Cu coating on optical signal actuation has been studied. Morphological studies showed improvement in the grain size, surface roughness, and a better shape memory effect. Thermal characteristics revealed an increase in the phase transformation temperature of NiTiCu with higher copper content. Experimental analysis of optical fiber sensors in Mineral Oil resulted in a gradual shift in the actuation temperature range. A sensitivity of ∼52 mV/°C was recorded in the actuation window, showcasing the improved response time for copper-rich composition. A comparison of coated-uncoated optical fiber performance in oil has also been presented, and delamination studies were conducted by continuously exposing the samples to hot oil. The work can be referred for requirement-based composition selection for real-time and smart monitoring capability in the Oil industries.

Advancements and Best Practices in Hard Disk Drives (HDDs) Repair: A Comprehensive Review of Diagnostic Techniques, Repair Methods, and Preventive Strategies

Hard Disk Drives (HDDs) remain a crucial component of data storage systems despite the advent of Solid State Drives (SSDs). However, the mechanical and electronic nature of HDDs makes them susceptible to various failures over time. This article provides a comprehensive examination of HDD repair, focusing on the anatomy and function of these devices, common causes of failure, diagnostic techniques, and effective repair methods. Mechanical failures, such as head crashes and spindle motor issues, electronic failures like PCB damage, and logical failures including file system corruption and bad sectors are explored in detail. Diagnostic approaches utilizing visual inspection, diagnostic software tools, hardware-based diagnostics, and SMART monitoring are discussed to aid in identifying issues accurately. Repair methods covered include data recovery techniques, mechanical repairs, electronic component replacement, and firmware fixes. Real-world case studies illustrate practical challenges and solutions in HDD repair, highlighting the complexities involved. Additionally, best practices and safety measures for handling and maintaining HDDs are provided to prevent future failures and ensure data integrity. This article aims to equip data recovery professionals, IT technicians, and tech enthusiasts with the knowledge and skills necessary to diagnose and repair HDD issues effectively. By integrating technical insights and practical approaches, it serves as a valuable resource for enhancing the reliability and longevity of HDDs in various applications.

Real-Time Monitoring and Control with Wireless Sensor and Actuator Technology

This paper explores the implementation of a smart monitoring system within a wireless sensor network, with a particular emphasis on developing a robust routing framework using the Routing Protocol for Low-power and Lossy Networks (RPL). This protocol, is designed to address the unique challenges of low-power and lossy environments. Our approach involves using a streamlined version of the Representational State Transfer (REST) architecture, implemented through a binary web service. This setup minimizes overhead and maximizes efficiency, which is critical for resource-constrained networks. Additionally, we use a publish/subscribe model, where each node in the network makes its resources—such as environmental sensors—available to other nodes interested in them. This model enhances the flexibility and responsiveness of the network. A significant part of our research involves a detailed performance evaluation of RPL. We conducted a series of experiments to understand how various parameters of the RPL protocol affect its performance in a smart grid scenario. Our analysis looks at key metrics such as routing efficiency, energy consumption, and overall network reliability. Through these experiments, we aim to provide valuable insights into how different configurations of RPL can impact its effectiveness. Our findings are intended to guide the optimization of RPL for specific applications, offering practical recommendations for deploying smart monitoring systems in similar low-power, lossy environments. This research not only sheds light on RPL’s performance but also contributes to the advancement of more efficient and reliable wireless sensor networks for smart grids and other related applications.

Optimizing smart home appliance energy monitoring using Factorial Hidden Markov Models for Internet of Behavior

The energy demand which is increasing globally strongly underlines the necessity of solutions that will improve power efficiency. This study addresses the research problem of enhancing energy management in smart homes through Non-Intrusive Load Monitoring (NILM), a method that enables consumers and companies to optimize their energy usage by disaggregating total household energy consumption into specific appliance-level data. The research focuses on the creation and establishment of an Internet of Behavior (IoB) model that encourages NILM to develop more energy-efficient and clever residential buildings as its main goal. The Factorial Hidden Markov Model (FHMM) is employed as the core methodology in this NILM process, facilitating the disaggregation of overall household energy usage into individual appliance consumption patterns. This method was used over the known Reference Energy Disaggregation Dataset (REDD) for comparing the actual energy consumption of the appliances with the projected estimates. The results are evidence that the FHMM method is an energy disaggregation technique that can provide important information about the customer usage of energy and, hence will help in the strategic shifting of high-energy appliances to non-peak hours for the user. This study's main value lies in the new application of IoB-based NILM methods to increase energy efficiency, which is the real solution for energy costs and the grid peak load problem. The results of the proposed method are compared with other methods and real data. The comparisons show the acceptable response of the proposed method when it is compared with the real data and is better than another method.

Reliable and efficient integration of AI into camera traps for smart wildlife monitoring based on continual learning

In this paper, we comprehensively report on an efficient approach for the integration of artificial intelligence (AI) processing pipelines in camera traps for smart on-site wildlife monitoring. Our work covers hardware, software, and algorithmics. We have built two prototypes of smart camera trap on a maximum bill of materials of 100$. We have also built two datasets, made publicly available, comprising over 17 k images, many of them notably challenging even for humans. Leveraging our broad expertise on embedded systems, specialized software libraries and toolchains, and AI techniques such as transfer learning, explainable AI, and, most importantly, continual learning, we achieve more reliable inference on-site - specifically 10 % higher F1-score - than MegaDetector run off-site on a desktop computer. The paper includes many practical details on system realization and on-site training in addition to a vast set of lab and experimental results.

Smart and autonomous monitoring of cracking in concrete structures with PZT sensor array-based hybrid sensing

Detecting microcracks in concrete structures is crucial for maintaining structural integrity; however, current methods often lack the precision, objectivity, and sensitivity needed for early-stage detection and monitoring. This study introduces a pioneering hybrid sensing approach that combines coherent and incoherent ultrasonic wave fields with surface-mounted PZT-based sensors, significantly advancing the state-of-the-art. The sensors are positioned at specified locations on a notched concrete beam and operated in the actuator-receiver (AR) mode, allowing for the manipulation of fracture width at the notch to achieve distinct degrees of damage. By evaluating the ultrasonic waves received from various AR configurations, we developed robust damage indices: the attenuation factor and diffusivity ratio. These indices provide objective metrics for evaluating the degree of damage, addressing the limitations of existing techniques. Key results demonstrate that the attenuation factor can detect crack widths as small as 10 µm at a 120 kHz frequency, surpassing traditional methods. Nevertheless, its sensitivity diminishes if the crack width exceeds 100 µm. Additionally, the diffusivity ratio remains sensitive to cracks beyond 200 µm and shows a substantial 40 % decrease even for non-visible fractures, offering a comprehensive assessment of fracture propagation. These advancements not only enhance early detection and monitoring precision but also offer a deeper understanding of fracture mechanics, paving the way for improved structural health monitoring and maintenance strategies.

Distributed and Collaborative Lightweight Edge Federated Learning for IoT Zombie Devices Detection

The fast development of artificial intelligence and Internet of Things (IoT) technologies has enabled various applications of smart cities, e.g., smart monitoring and surveillance. However, vulnerabilities of IoT devices bring new threats to the security of smart cities. To identify ubiquitous IoT botnet attacks, a distributed and collaborative lightweight edge federated learning model for IoT zombie devices detection is proposed, named FIOT. To reduce computational complexity and enhance the adaptability to new attack environment at the network edge, FIOT is designed in a lightweight manner based on feature dimensionality reduction and transfer learning. Three IoT botnet datasets are used to validate the effectiveness of the proposed FIOT. Experimental results show that FIOT has an accuracy loss of less than 3% in terms of F1 value compared to the centralized learning, but the training time of FIOT is only 14.3% of that of centralized learning. While ensuring high detection accuracy, the number of parameters of FIOT is compressed to 37.58% of the comparison method.

Advancing Sustainable Cyber-Physical System Development with a Digital Twins and Language Engineering Approach: Smart Greenhouse Applications

In recent years, the integration of Internet of Things technologies in smart agriculture has become critical for sustainability and efficiency, to the extent that recent improvements have transformed greenhouse farming. This study investigated the complexity of IoT architecture in smart greenhouses by introducing a greenhouse language family (GreenH) that comprises three domain-specific languages designed to address various tasks in this domain. The purpose of this research was to streamline the creation, simulation, and monitoring of digital twins, an essential tool for optimizing greenhouse operations. A three-stage methodology was employed to develop the GreenH DSLs, a detailed metamodel for enhanced smart monitoring systems. Our approach used high-level metamodels and extended Backus–Naur form notation to define the DSL syntax and semantics. Through a comprehensive evaluation strategy and a selected language usability metrics, the expressiveness, consistency, readability, correctness, and scalability of the DSL were affirmed, and areas for usability improvement were highlighted. The findings suggest that GreenH languages hold significant potential for advancing digital twin modeling in smart agriculture. Future work should be aimed at refining usability and extending its application range. The anticipated integration with additional model-drive engineering and code generation tools will improve interoperability and contribute to digital transformation in the smart greenhouse domain and promote more sustainable food production systems.