Search Results (104)

The increasing prevalence of IoT technology in smart homes has significantly enhanced convenience but also introduced new security and safety challenges. Traditional security solutions, reliant on sequences of IoT-generated event data (e.g., notifications of device status changes and sensor readings), are vulnerable to cyberattacks, such as message forgery and interception and delaying attacks, and fail to monitor non-smart devices. Moreover, fragmented smart home ecosystems require vendor cooperation or system modifications for comprehensive monitoring, limiting the practicality of the existing approaches. To address these issues, we propose IoTBystander, a non-intrusive dual-channel smart home security monitoring framework that utilizes two ubiquitous platform-agnostic signals, i.e., audio and network, to monitor user and device activities. We introduce a novel dual-channel aggregation mechanism that integrates insights from both channels and cross-verifies the integrity of monitoring results. This approach expands the monitoring scope to include non-smart devices and provides richer context for anomaly detection, failure diagnosis, and configuration debugging. Empirical evaluations on a real-world testbed with nine smart and eleven non-smart devices demonstrate the high accuracy of IoTBystander in event recognition: 92.86% for recognizing events of smart devices, 95.09% for non-smart devices, and 94.27% for all devices. A case study on five anomaly scenarios further shows significant improvements in anomaly detection performance by combining the strengths of both channels. Full article

Advancements in electronics and sensor technologies are driving the deployment of ubiquitous sensor networks across various applications, including asset monitoring, security, and networking. At the same time, ensuring the integrity and confidentiality of data collected by sensor nodes is crucial to prevent unauthorized access or modification. However, the limited resources f low-power sensor networks present significant challenges for securing innovative Internet-of-Medical Things (IoMT) applications in complex environments. These miniature sensing systems, essential for diverse healthcare applications, grapple with constrained computational power and energy budgets. To address this challenge, this study proposes a dynamic power management strategy within a resource-constrained FPGA-based cryptoprocessor core for secure IoMT networks. The sensor node design comprises two main modules: an 8-bit reduced instruction set computer (RISC) and a cryptographic engine. These modules collaboratively manage their power consumption during the operational stages of data acquisition, encryption, transmission, and sleep mode activation. The cryptographic engine employs a pseudorandom number generator to generate a keystream for data encryption, utilizing direct sequence spread spectrum (DSSS) encoding to ensure secure communication. The experimental results demonstrate the effectiveness of the proposed dynamic power management strategy within the resource-constrained cryptoprocessor core. The sensor node achieves an average power consumption of 0.1 mW while utilizing 2414 logic cells and 5292 registers. A comparative analysis with other state-of-the-art lightweight sensor nodes highlights the advantages of our dynamic power management approach within the cryptoprocessor sensing system. Full article

Naturally occurring radionuclides are ubiquitous at various levels of concentration, while exposure to ionizing radiation by humans is of global concern. Radiological health risk assessment due to the consumption of natural spring mineral water is critical for ensuring public health and safety. This study aims at investigating the radioactivity concentration levels of natural radionuclides ²²⁶Ra, ²³²Th and ⁴⁰K and the associated radiological health risk in commercial natural spring bottled water in South Africa. A total of 21 of the most-consumed bottled drinking water brands from grocery stores, were analysed using the HPGe gamma detector. The results indicate that the range of radioactivity concentrations is from 1.060 ± 0.067 to 2.571 ± 0.143 BqL⁻¹, with a mean of 1.766 ± 0.399 BqL⁻¹ for ²²⁶Ra; 1.736 ± 0.112 to 7.807 ± 0.099 BqL⁻¹, with a mean of 3.688 ± 1.371 BqL⁻¹ for ²³²Th and 149.000 ± 38.480 to 242.900 ± 59.700 BqL⁻¹ with a mean of 220.229 ± 22.297 BqL⁻¹ for ⁴⁰K. The potential radiological health risks evaluated show mean values for Ra_eq, D_Ab, AEID and AGED as 23.976 ± 0.446 BqL⁻¹, 12.232 ± 1.445 nGyh⁻¹, 0.060 ± 0.007 mSvy⁻¹ and 0.090 ± 0.027 mSvy⁻¹, respectively. The radiation dose based on age group is in the order of infants (≤1 year) > teenagers (12–17 years) > children (1–12 years) > adults (>17 years). The activity concentrations of radionuclides in bottled water are ranked in the order of ⁴⁰K > ²³²Th > ²²⁶Ra, with ²³²Th contributing the highest radiation dose, consistent with findings reported in previous studies. The findings reveal that the activity concentration levels and estimated radiological health risks are within the permissible limits set by UNSCEAR guidelines. Therefore, the consumption of bottled water is radiologically safe. However, the findings also suggest that 12 out of 1000 individuals may suffer cancer fatality, while 6 out of 1 million individuals may experience hereditary effects over their lifetime from the consumption of bottled water. Regular monitoring and stringent regulatory controls are recommended to ensure the radiological safety of bottled drinking water in South Africa. Full article

Background: Legionella species are the causative agent of Legionnaires’ disease and, as ubiquitous waterborne bacteria, are prone to antimicrobial resistance gene (ARG) acquisition and dissemination due to the antimicrobial contamination of natural environments. Given the potential health risks associated with ARGs, it is crucial to assess their presence in the Legionella population. Methods: The ARGs lpeAB and tet56 were detected in 348 samples, isolates, and DNA extracts using conventional PCR. In a subset of lpeAB-positive isolates, azithromycin (AZT) MIC values were obtained using the EUCAST protocol and LpeAB activity was evaluated through an efflux pump inhibition assay. Results: The lpeAB gene was found in 19% (66/348) of samples, with higher detection rates in the L. pneumophila and L. pneumophila sg1 subgroups, at 30% and 41%, respectively. A positive association between lpeAB and L. pneumophila sg1 was found. The MIC values of the lpeAB-positive isolates ranged from 0.064 to 2 mg/L. LpeAB inhibition resulted in 2- and 4-fold MIC reductions in 10 of the 13 isolates analyzed. One sample each of L. longbeacheae and L. bozemanae was found to possess the tet56 gene. Conclusions: The lpeAB gene is predominant in L. pneumophila sg1. A few isolates with the lpeAB gene exhibited MIC values below the EUCAST tentative highest MIC values for wild-type isolates. Expanding ARG monitoring in Legionella is essential to assess the public health risk of Legionnaires’ disease. Full article

With the compelling popularity of integrated sensing and communication (ISAC), Wi-Fi sensing has drawn increasing attention in recent years. Starting from 2010, Wi-Fi channel state information (CSI)-based wireless sensing has enabled various exciting applications such as indoor localization, target imaging, activity recognition, and vital sign monitoring. In this paper, we retrospect the latest achievements of Wi-Fi sensing using commodity-off-the-shelf (COTS) devices from the past 5 years in detail. Specifically, this paper first presents the background of the CSI signal and related sensing models. Then, recent studies are categorized from two perspectives, i.e., according to their application scenario diversity and the corresponding sensing methodology difference, respectively. Next, this paper points out the challenges faced by Wi-Fi sensing, including domain dependency and sensing range limitation. Finally, three imperative research directions are highlighted, which are critical for realizing more ubiquitous and practical Wi-Fi sensing in real-life applications. Full article

Hyperspectral imaging is currently under active development as a method for remote sensing, environmental monitoring and biomedical diagnostics. The development of hyperspectral sensors is aimed at their miniaturization and reducing the cost of components for the purpose of the widespread use of such devices on unmanned aerial vehicles and satellites. In this review, we present a broad overview of recent work on the development of hyperspectral devices’ configurations, studies aimed at modifying sensors and the possibility of reducing the cost of components of such devices. In addition, we will present the main trends in the development of hyperspectral device configurations for ubiquitous applications. Full article

The antibiotic resistance crisis has seriously jeopardized public health and human safety. As one of the ways of horizontal transfer, transformation enables bacteria to acquire exogenous genes naturally. Bisphenol compounds are now widely used in plastics, food, and beverage packaging, and have become a new environmental pollutant. However, their potential relationship with the spread of antibiotic resistance genes (ARGs) in the environment remains largely unexplored. In this study, we aimed to assess whether the ubiquitous bisphenol S (BPS) could promote the transformation of plasmid-borne ARGs. Using plasmid pUC19 carrying the ampicillin resistance gene as an extracellular ARG and model microorganism E. coli DH5α as the recipient, we established a transformation system. Transformation assays revealed that environmentally relevant concentrations of BPS (0.1–10 μg/mL) markedly enhanced the transformation frequency of plasmid-borne ARGs into E. coli DH5α up to 2.02-fold. Fluorescent probes and transcript-level analyses suggest that BPS stimulated increased reactive oxygen species (ROS) production, activated the SOS response, induced membrane damage, and increased membrane fluidity, which weakened the barrier for plasmid transfer, allowing foreign DNA to be more easily absorbed. Moreover, BPS stimulates ATP supply by activating the tricarboxylic acid (TCA) cycle, which promotes flagellar motility and expands the search for foreign DNA. Overall, these findings provide important insight into the role of bisphenol compounds in facilitating the horizontal spread of ARGs and emphasize the need to monitor the residues of these environmental contaminants. Full article

Monitoring human activities, such as walking, falling, and jumping, provides valuable information for personalized health assistants. Existing solutions require the user to carry/wear certain smart devices to capture motion/audio data, use a high-definition camera to record video data, or deploy dedicated devices to collect wireless data. However, none of these solutions are widely adopted for reasons such as discomfort, privacy, and overheads. Therefore, an effective solution to provide non-intrusive, secure, and low-cost human activity monitoring is needed. In this study, we developed a contactless human activity monitoring system that utilizes channel state information (CSI) of the existing ubiquitous WiFi signals. Specifically, we deployed a low-cost commercial off-the-shelf (COTS) router as a transmitter and reused a desktop equipped with an Intel WiFi Link 5300 NIC as a receiver, allowing us to obtain CSI data that recorded human activities. To remove the outliers and ambient noise existing in raw CSI signals, an integrated filter consisting of Hampel, wavelet, and moving average filters was designed. Then, a new metric based on kurtosis and standard deviation was designed to obtain an optimal set of subcarriers that is sensitive to all target activities from the candidate 30 subcarriers. Finally, we selected a group of features, including time- and frequency-domain features, and trained a classification model to recognize different indoor human activities. Our experimental results demonstrate that the proposed system can achieve a mean accuracy of above 93%, even in the face of a long sensing distance. Full article

Electrocardiography (ECG) has emerged as a ubiquitous diagnostic tool for the identification and characterization of diverse cardiovascular pathologies. Wearable health monitoring devices, equipped with on-device biomedical artificial intelligence (AI) processors, have revolutionized the acquisition, analysis, and interpretation of ECG data. However, these systems necessitate AI processors that exhibit flexible configuration, facilitate portability, and demonstrate optimal performance in terms of power consumption and latency for the realization of various functionalities. To address these challenges, this study proposes an instruction-driven convolutional neural network (CNN) processor. This processor incorporates three key features: (1) An instruction-driven CNN processor to support versatile ECG-based application. (2) A Processing element (PE) array design that simultaneously considers parallelism and data reuse. (3) An activation unit based on the CORDIC algorithm, supporting both Tanh and Sigmoid computations. The design has been implemented using 110 nm CMOS process technology, occupying a die area of 1.35 mm² with 12.94 µW power consumption. It has been demonstrated with two typical ECG AI applications, including two-class (i.e., normal/abnormal) classification and five-class classification. The proposed 1-D CNN algorithm performs with a 97.95% accuracy for the two-class classification and 97.9% for the five-class classification, respectively. Full article

Remote patient-monitoring systems are helpful since they can provide timely and effective healthcare facilities. Such online telemedicine is usually achieved with the help of sophisticated and advanced wearable sensor technologies. The modern type of wearable connected devices enable the monitoring of vital sign parameters such as: heart rate variability (HRV) also known as electrocardiogram (ECG), blood pressure (BLP), Respiratory rate and body temperature, blood pressure (BLP), respiratory rate, and body temperature. The ubiquitous problem of wearable devices is their power demand for signal transmission; such devices require frequent battery charging, which causes serious limitations to the continuous monitoring of vital data. To overcome this, the current study provides a primary report on collecting kinetic energy from daily human activities for monitoring vital human signs. The harvested energy is used to sustain the battery autonomy of wearable devices, which allows for a longer monitoring time of vital data. This study proposes a novel type of stress- or exercise-monitoring ECG device based on a microcontroller (PIC18F4550) and a Wi-Fi device (ESP8266), which is cost-effective and enables real-time monitoring of heart rate in the cloud during normal daily activities. In order to achieve both portability and maximum power, the harvester has a small structure and low friction. Neodymium magnets were chosen for their high magnetic strength, versatility, and compact size. Due to the non-linear magnetic force interaction of the magnets, the non-linear part of the dynamic equation has an inverse quadratic form. Electromechanical damping is considered in this study, and the quadratic non-linearity is approximated using MacLaurin expansion, which enables us to find the law of motion for general case studies using classical methods for dynamic equations and the suitable parameters for the harvester. The oscillations are enabled by applying an initial force, and there is a loss of energy due to the electromechanical damping. A typical numerical application is computed with Matlab 2015 software, and an ODE45 solver is used to verify the accuracy of the method. Full article

Officers on Watch (OOWs) of the ship’s bridge play a vital role in maritime navigation safety, monitoring the ship’s navigational status, and ensuring maritime safety. The status of inactive watch officers, such as fatigued driving and negligence on lookout, is one of the main causes of accidents. Intelligent technology for real-time perception and state evaluation of ship OOWs significantly reduces accidents caused by human factors. The traditional computer vision method is difficult to adapt to the complex environment of a ship bridge, and carries strong privacy risks. With the development of Internet of Things technology, sensing technology based on ubiquitous Wi-Fi devices provides a new way to accurately monitor the status of ship OOWs. In this paper, we use commercial off-the-shelf (COTS) Wi-Fi devices to propose a ship driving activity state detection method based on beamforming feedback information (BFI). Using wireless sensing data to sense the number of OOWs and their driving behavior realizes low-cost and high-precision detection of the behavioral status of the ship’s bridge watchkeeper. Experiments were conducted in a ship-driving simulation laboratory and on a real-world Yangtze River cruise ship. The experimental results demonstrate that our proposed method achieves 92.4% and 98.1% accuracy for tracking active status and estimating the number of OOWs, respectively. Full article

With the advancement of technology and increasing industrial activity, anthropogenic contaminants are currently detected where there is no record of their presence or insufficient information about their toxicological impact. Consequently, there are not sufficiently robust local or global regulations, the ecotoxicological and human health risks are critical, and they may not be routinely monitored despite being ubiquitous. The interest in studying environmental contaminants, including micropollutants and emerging contaminants, in complex environmental water samples has grown in the last decade. Due to the concentrations in which they are typically found in the environment and the rapid global dispersion, the detection procedures for these substances must be capable of measuring very low concentrations. Many efforts have been made to improve remediation procedures or develop novel analytical methods for their determination. Although there are several robust and reliable standard analytical techniques for their monitoring, pollutant contamination requires simple and inexpensive methods for massive, in situ monitoring campaigns. In this regard, biosensors have emerged as devices with high selectivity, sensitivity, easy operation, and short analysis times. Aptasensors are biosensors based on a nucleic acid recognition element (aptamer). Due to their synthetic nature, stability, and easy production, aptamers are frequently employed to develop bioassays. This work presents a systematic review of the trends in using aptasensors for detecting environmental contaminants present in environmental water samples, as well as the estimation of the potential technological contribution these devices might give to environmental monitoring. Full article

There is increasing interest in space domain awareness worldwide, motivating investigation of the use of non-traditional sensors for space surveillance. One such class of sensor is VHF wind profiling radars, which have a low cost relative to other radars typically applied to this task. These radars are ubiquitous throughout the world and may potentially offer complementary space surveillance capabilities to the Space Surveillance Network. This paper updates an initial investigation on the use of Buckland Park VHF wind profiling radars for observing resident space objects in low Earth orbit to further investigate the space surveillance capabilities of the sensor class. The radar was operated during the Australian Defence “SpaceFest” 2019 activity, incorporating new beam scheduling and signal processing functionality that extend upon the capabilities described in the initial investigation. The beam scheduling capability used two-line element propagations to determine the appropriate beam direction to use to observe transiting satellites. The signal processing capabilities used a technique based on the Keystone transform to correct for range migration, allowing the development of new signal processing modes that allow the coherent integration time to be increased to improve the SNR of the observed targets, thereby increasing the detection rate. The results reveal that 5874 objects were detected over 10 days, with 2202 unique objects detected, representing a three-fold increase in detection rate over previous single-beam direction observations. The maximum detection height was 2975.4 km, indicating a capability to detect objects in medium Earth orbit. A minimum detectable RCS at 1000 km of −10.97 dBm² (0.09 m²) was observed. The effects of Faraday rotation resulting from the use of linearly polarised antennae are demonstrated. The radar’s utility for providing total electron content (TEC) measurements is investigated using a high-range resolution mode and high-precision ephemeris data. The short-term Fourier transform is applied to demonstrate the radar’s ability to investigate satellite rotation characteristics and monitor ionospheric plasma waves and instabilities. Full article

The binding of ubiquitous serum ligands (free fatty acids) to human serum albumin (HSA) or its glycation can affect thiol group reactivity, thus influencing its antioxidant activity. The effects of stearic acid (SA) and glucose binding on HSA structural changes and thiol group content and reactivity were monitored by fluoroscopy and the Ellman method during a 14-day incubation in molar ratios to HSA that mimic pathophysiological conditions. Upon incubation with 5 mM glucose, HSA glycation was the same as HSA without it, in three different HSA:SA molar ratios (HSA:SA-1:1-2-4). The protective effect of SA on the antioxidant property of HSA under different glucose regimes (5-10-20 mM) was significantly affected by molar ratios of HSA:SA. Thiol reactivity was fully restored with 5–20 mM glucose at a 1:1 HSA:SA ratio, while the highest thiol content recovery was in pathological glucose regimes at a 1:1 HSA:SA ratio. The SA affinity for HSA increased significantly (1.5- and 1.3-fold, p < 0.01) with 5 and 10 mM glucose compared to the control. These results deepen the knowledge about the possible regulation of the antioxidant role of HSA in diabetes and other pathophysiological conditions and enable the design of future HSA-drug studies which, in turn, is important for clinicians when designing information-based treatments. Full article

Volatile organic compounds (VOCs), with their ubiquitous presence across numerous global industries, pose multifaceted challenges, influencing air pollution and health outcomes. In response, countries such as the United States and Canada have implemented fenceline monitoring systems, enabling real-time tracking of organic solvents, including benzene. Initially, this focus was predominantly placed on the petroleum refining industry, but it has gradually been broadening. This investigation seeks to identify and analyze the specific VOCs produced in the tire manufacturing sector by utilizing both active and passive monitoring methodologies. The findings of the present study aim to recommend prioritized reduction strategies for specific VOCs. Percentage means the ratio of VOCs detected at the research site. At research target facility A, active monitoring demonstrated the presence of Methylene chloride (20.7%) and Carbon tetrachloride (15.3%), whereas passive monitoring identified Carbon tetrachloride (43.4%) and m,p-Xylene (20.8%). After converting these substances to their equivalent concentrations, we found a noteworthy correlation between the active and passive methodologies. At research target facility B, active monitoring detected n-Pentane (45.5%) and Isoprene (11.4%), while passive monitoring revealed Toluene (21.3%) and iso-Hexane (15.8%). Interestingly, even at sites like warehouses and test tracks where VOC concentrations were projected to be low, we observed VOC levels comparable to those in process areas. This underlines the fact that the dispersal of VOCs is considerably influenced by wind direction and speed. Specifically, in the tire manufacturing industry, emissions of Xylene and 3-Methylhexane, both having high photochemical ozone creation potential (POCP), contribute significantly to air pollution. However, the overall detection concentration in the tire manufacturing industry was detected at a low concentration of less than 2 μg/m³. This is less than 9 μg/m³, which is the standard for benzene, which has strong carcinogenicity regulations. This suggests that additional research is needed on synthetic rubber manufacturing rather than tire manufacturing. Full article