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Knowledge of modern state-of-the-practice traffic flow sensors provides traffic managers, researchers, and students an understanding of the operation, strengths, and limitations of current sensor technologies and enables them to make an informed decision as to which is appropriate for a particular application. Accordingly, this article describes the intrusive and nonintrusive traffic flow sensor technologies in use today, their applications and selection criteria, and typical output data. Furthermore, it provides examples of representative sensor models. The technologies discussed are mature with respect to current traffic management applications, although some may not provide the data or accuracy required for a specific application or may not perform as needed under the operational conditions anticipated at the installation site. Sensors selected for a first-time application should be field tested under conditions that will be encountered in day-to-day operation before large-scale purchases of the devices are made. As alternative traffic data and information sources, such as commercial data vendors, Wi-Fi and Bluetooth sensing of smartphone locations, and connected and automated vehicle data, become increasingly available, they are progressively finding their way into modern traffic management systems as a complement to conventional roadside sensors. [ABSTRACT FROM AUTHOR]

Luojia 3-01 is the world's first intelligent remote sensing satellite, equipped with various imaging modes including video, frame-pushing, and scan-pushing. It boasts submeter level multimode optical imaging capabilities, on-orbit intelligent processing, and real-time intra-satellite and satellite-to-ground data transmission. Throughout the satellite's design and development, we have established the real-time intelligent information service architecture for Luojia 3-01, marking a new paradigm in on-orbit processing and real-time services for intelligent remote sensing satellites. We have proposed the on-orbit real-time processing architecture of Luojia 3-01, addressing the challenges of limited computational and storage resources, particularly in on-orbit processing of vast volumes of remote sensing data, including core algorithmic bottlenecks in “correction-extraction-compression.” A novel intelligent remote sensing satellite system has been developed, featuring multimode imaging, an open platform, intelligent processing, and satellite-to-ground interconnectivity, which significantly reduces the response time of remote sensing services to less than 8 min, thereby shortening the cycle from data acquisition to intelligent information service. This innovation spearheads a technological leap in remote sensing satellite services from data to information, from post-event to real-time, and from professional to widespread public applications, laying a solid foundation for the popularization and commercialization of China's intelligent remote sensing satellites.

The air ventilation of enclosed premises has a direct impact on the occupants’ well-being. If not properly regulated, the air ventilation can originate a multitude of diseases and pathologies. The present study proposes a new KPI (ventilation KPI) adapted to Smart Cities. It is especially designed for academic environments (Smart Universities) in which community members spend a long time gathered in classrooms, seminars, laboratories, etc. The ventilation KPI (or KPIv) was designed to support decision making and is based on the estimation of the number of occupants of an enclosed space and the accumulation of existing CO2. Two AI techniques are proposed to perform these estimations, specifically, two regressive neural networks. The resulting models, together with the KPI, were implemented through the development of value-added services for the University of Alicante’s Smart University platform. The network models were designed to be embedded within the built IoT device prototypes. These prototypes are small and inexpensive. They act as intelligent sensors and are connected via a low consumption and emission network (LoRa). The case study showed that it is possible to take advantage of the preexisting services and resources of these platforms and to validate the KPIv. [ABSTRACT FROM AUTHOR]

Digital Object Identifier 10.1109/ TSM. 2024.3411140Authorized licensed use limited to the terms of the applicable license agreement with IEEE. Restrictions apply. Authorized licensed use limited to the terms of the applicable license agreement with IEEE. Restrictions apply. [Extracted from the article]

In this article, a 2.5m five-stage inductive power transfer (IPT) system based on domino cylindrical solenoid coupler (CSC) compensated, respectively, in layers is proposed. The system is considered to be used as 220 kV post insulators application, transferring power for sensors and measuring instruments of smart grids. The distributed series compensation method is applied to double-layer CSC. Compared with the two types of parallel schemes, this method can significantly reduce the current of the main circuit. Compared with the concentrated series compensation method, this method can effectively reduce the resistance caused by the stray capacitance. The interaction between the post insulator and the magnetic coupling structure is analyzed. The circuit model of the system with constant voltage output characteristics is analyzed. A prototype with a maximum efficiency of 66.7% is built. When the power is transferred to a larger value of 77.8W, the efficiency can still reach 62%. Comparing with previous works, the wireless distance is extended while the efficiency is improved. [ABSTRACT FROM AUTHOR]

Precision agriculture is a high-level agricultural production process that achieves high quality and high efficiency by combining traditional agricultural technology with the Internet of Things (IoT) technology and artificial intelligence (AI) technology. This method achieves high-quality, high-tech, and high-level agricultural production. Value-added agricultural products are the target. Intelligent greenhouse planting technology facilitates the control and management of the greenhouse environment through intelligent analysis and decision-making while preserving the traditional agricultural cultivation experience and management and expanding the operating area, reducing the burden and expenditure of farm management. In this article, an orchid growth inspection system is proposed for detecting the growth status of orchids in greenhouses. This system can be used to manage environmental factors, flower names, and the growth status of orchids in the greenhouse. In addition, an emerging self-supervised learning method is proposed for training and identifying orchid models to recognize the growth status of orchids. Many orchid species have the same appearance, color, and shape. The difficulty of identifying orchids can hinder their proper management. Therefore, if the name and growth status of the orchid can be established, the managers of the orchid greenhouse can more effectively manage orchid growth. The experimental results show that the orchid recognition accuracy reached 98.6%. [ABSTRACT FROM AUTHOR]

Wearable electronics is now revolutionizing the world of smart sensors offering tremendous solutions to a variety of applications that span from biomedical market to gaming and fashion sectors. In this scenario, physical sensors play a crucial role since they offer fast and reliable feedback on human motion, even for fine gestures, and can detect vital physiological parameters such as breathing and heart beating, while being able to be easily integrated into textile. Among wearable physical sensors, thermoplastic materials are utilized for their sensitivity and high stretchability. Moreover, these materials exhibit a good chemical resistance and implement low-cost manufacturing processes. In this work, we report a full characterization of a new thermoplastic nanocomposite material comparing its performances with and without preconditioning for strain up to 20%. Together with a measured gauge factor (GF) of about 10, sensors without preconditioning exhibit very good stability and they result to be a good candidate for wearable applications. We demonstrate this statement by analyzing the performance of a smart wristband prototype that integrates these strain gauges, obtaining very high performance of the sensors without prestrain in gesture recognition tasks with an accuracy and ${F}$ -score of about 94%. [ABSTRACT FROM AUTHOR]

Due to the COVID-19 global pandemic, there is more need for remote patient care, especially in rehabilitation requiring direct contact. However, traditional Chinese rehabilitation technologies, such as gua sha, often need to be implemented by well-trained professionals. To automate and professionalize gua sha, it is necessary to record the nursing and rehabilitation process and reproduce the process in developing smart gua sha equipment. This article proposes a new signal processing and sensor fusion method for developing a piece of smart gua sha equipment. A novel stabilized numerical integration method based on information fusion and detrended fluctuation analysis (SNIF-DFA) is performed to obtain the velocity and displacement information during the gua sha operation. The experimental results show that the proposed method outperforms the traditional numerical integration method with respect to information accuracy and realizes accurate position calculations. This is of great significance in developing robots or automated machines that reproduce the nursing and rehabilitation operations of medical professionals. [ABSTRACT FROM AUTHOR]

With the advances in new technological trends and the reduction in prices of sensor nodes, wireless sensor networks (WSNs) and their applications are proliferating in several areas of our society, such as healthcare, industry, farming, and housing. Accordingly, in recent years, attention to localization has increased significantly since it is one of the main facets of a WSN. In a nutshell, localization is the process in which the position of a sensor node is retrieved by exploiting measurements from and between sensor nodes. Several techniques of localization have been proposed in the literature with different localization accuracies, complexities, and, hence, applicabilities. The localization accuracy is limited by fundamental limitations, theoretical and practical, which restrict the localization accuracy regardless of the technique employed in the localization process. In this article, we pay special attention to such limitations from theoretical and practical points of view and provide a comprehensive review of the state-of-the-art solutions that deal with such limitations. In addition, a discussion on the theoretical and practical limitations together with their recent solutions, remaining challenges, and perspectives is presented. [ABSTRACT FROM AUTHOR]

In this article, a shielding scheme based on ferrite magnetic antennas (SS-FMA) is proposed. The scheme can effectively provide a path for the magnetic field lines of three-dimensional couplers to be concentrated and conducted. The SS-FMA is considered to be applied for long range inductive power transfer (IPT) systems of post insulators to powering smart grid sensors and improve the efficiency under the influence of metal components. The negative influence of the aluminum flanges on the magnetic field distribution is analyzed. The suppression effects of the shielding scheme on the abovementioned influence have been analyzed. Parameters of the SS-FMA are optimized. The equivalent circuit model is established, and the abovementioned process is explained from the perspective of the circuit model. A prototype of a post insulator that includes IPT system and SS-FMA has been built. When the coils of the IPT system are attached to the flanges, the efficiency can still reach 47.8% under the influence of SS-FMA, which is an increase of 13.7% compared with no shielding scheme. When the coils are slightly away from the flanges, efficiency of the system including SS-FMA can be quickly improved to 54.3%. [ABSTRACT FROM AUTHOR]

With the rapid improvement of Industrial Internet of Things and artificial intelligence, predictive maintenance (PdM) has attracted great attention from both academia and industrial practitioners. When equipment is running, the electrical attributes have intrinsic relations. Meanwhile, they are changing over time. However, existing PdM models are often limited as they lack considering both attribute interactions and temporal dependence of the dynamic working system. To address the problem, in this article, we propose an electrical spatio-temporal graph convolutional network (Electrical-STGCN) for PdM. First, it takes a sequence of electrical records as input. Next, both attribute interactions and temporal dependence are established to extract features. Then, the extracted features are fed into a prediction component. Finally, the output of the Electrical-STGCN (i.e., remaining useful life) can help the workers decide whether to carry out equipment maintenance. The effectiveness of the proposed method is verified in real-world cases. Our method achieves 85.2% Accuracy and 0.9 F1-Score, which are better than the other approaches. [ABSTRACT FROM AUTHOR]

With the help of wireless power transfer (WPT) technology, the mobile charger (MC) can transfer energy to the sensor nodes. This technology provides a new solution to prolong the lifetime of wireless rechargeable sensor networks (WRSNs). However, most of the existing studies focused on improving the charging efficiency or minimizing the latency, while very few studies improved the data accuracy of the network. This article proposes an efficient-energy recharging schedule for MC, aiming to maximize the data accuracy of the given network. Initially, the data accuracy of each sensor is measured by considering the spatial and temporal qualities. The proposed recharging schedule considers the spatial quality contribution of each recharging requested sensor. In addition, an energy management strategy is proposed for each requested sensor to locally adjust the sensing time sequence, aiming to improve the temporal quality. Each sensor might have a different energy consumption rate; therefore, this study also formulates an adaptive recharging request threshold for the sensor nodes, which is suitable for real applications. The experimental study shows that the proposed algorithm outperforms the literature in terms of data accuracy and recharged sensor’s spatial quality contributions. [ABSTRACT FROM AUTHOR]

The data produced by millions of connected devices and smart sensors in the Industrial Internet of Things (IIoT) is highly dynamic, large-scale, heterogeneous, and time-stamped. These time-stamped data are the core of IIoT automation and have the potential to affect industrial processes intensely. It poses significant challenges to effectively detect anomalies from time-series data and deliver actionable insights in real time to drive improvements to industrial processes. In most practical applications, where data are used to make automated decisions, real-time anomaly detection is critical. With this focus, in this article, we advise a hybrid end-to-end deep anomaly detection (DAD) framework to accurately detect anomalies and extremely rare events on sensitive, Internet of Things (IoT) streaming data in real time or near real time. The proposed framework is based on a convolutional neural network (CNN) and a two-stage long short-term memory (LSTM)-based Autoencoder (AE). We exploit a two-stage LSTM AE in parallel to detect anomalies and extremely rare events hidden in massive sensor data by identifying short- and long-term variations in actual sensor values from the predicted values. We design and train a hybrid model using the Keras/TensorFlow framework as the backend. The experimental results on one simulation and two real datasets demonstrate that the proposed framework achieved better performance and outperforms other state-of-the-art competitive models. Moreover, to prove that the proposed model can be designed for the network edge, we train, optimize, and quantize the model to run-on resource-constrained (i.e., edge) devices. Further evaluation indicates that the training and inference time for each sample is short enough to carry out anomaly detection on edge. [ABSTRACT FROM AUTHOR]

Flexible pressure sensors with high sensitivity, low cost, lightweight, and convenience are of significance in the field of human movement monitoring and sensing. This article demonstrated a piezoresistive pressure sensor prepared by layer-by-layer (LbL) self-assembly of MXene and carbon black (CB) on a polyurethane (PU) sponge. The electronegativity of MXene and the electropositive of CB enable them to combine more stably. The assembled sensor achieves reliable repeatability (over 3700 cycle testing), high sensitivity (−5.26 kPa $^{-{1}}$), wide sensing range (0–240 kPa), acceptable response time (300/300 ms), and good reproducibility under various pressures. MXene and CB greatly increase the electrical conductivity and measurement range of the piezoresistive sensors. The existence of microcracks allows the sensor to sensitively capture micropressure signals, such as human radial artery pulse and respiration. In addition, the large-area conductive framework permits the sensor to maintain high responsiveness over a huge strain range and to distinguish various degrees of human movement. Furthermore, the sensor has shown excellent performance in controlling smart devices through external pressure as a human–machine interface. The pressure sensor based on LbL self-assembled MXene/CB on a PU sponge has potential applications in motion signal detection, artificial intelligence, and other fields. [ABSTRACT FROM AUTHOR]

Machine olfactory perception (MOP) is a new type of intelligent perception technology that senses and describes odors. The difference between MOP and machine olfaction is that MOP describes odors by using some semantic labels. One of the challenges in realizing MOP is an appropriate multilabel classifier from the high-dimensional sensor responses. Here, we propose a novel aroma-type (AT) oriented MOP method, which is an MOP method of multiple AT. Wherein, the method designed an intelligent perception of multiple ATs based on a convolutional neural network (IPMAT-CNN), in which an olfactory information-shared mechanism was introduced to enhance the feature extraction of the network. In addition, by analyzing the molecular correlation between multiple ATs, a matrix addition method was designed in IPMAT-CNN to strengthen the information on adjacent ATs. These designs effectively improved the accuracy of model detection. At last, IPMAT-CNN successfully sensed and recognized seven ATs of 38 monomer flavors. The effectiveness of IPMAT-CNN was evaluated by four baseline models [classifier chain based on random forest classifier (CC-RF), label powerset based on random forest classifier (LP-RF), multilabel decision tree (ML-DT), and multilabel ${k}$ -nearest neighbor (ML-kNN)]. The results showed that the IPMAT-CNN had the best performance with a 0.0636 hamming loss, a 93.65% accuracy, and a 0.8149 mean average precision (mAP) value for classifying the seven ATs. It reveals that the IPMAT-CNN had a decent prediction performance and can be used as an effective reference method for the ATs classification. [ABSTRACT FROM AUTHOR]

Localization of radio-frequency (RF) transmitters using time difference of arrival (TDOA)-based methods is one of the conventional passive techniques that admits noncooperative source position finding, but suffers from challenging requirements such as precise intersensor synchronization and high-throughput transmission data links. A tradeoff governing the TDOA systems is in the sensor placement configuration. The more distance the sensors are placed, the more accurate localization is carried out, while the cost for the synchronization and data link increases, at the same time. In this work, a novel reconfigurable intelligent surface (RIS)-aided localization system is proposed that enjoys precise location accuracy while reducing the required expenses. The study reveals that the new setup, utilizing the beam-scanning property of the RIS sensors, improves the localization algorithm and outperforms the conventional approach. Also, comparisons with Cramér Rao lower bound (CRLB) are provided, which confirms the efficiency of the proposed method. Several numerical examples indicate the effectiveness of the proposed method, especially in a low signal-to-noise ratio (SNR) regime. [ABSTRACT FROM AUTHOR]

As one of the most crucial scenarios of the Internet of Things (IoT), wireless multimedia sensor networks (WMSNs) pay more attention to the information-intensive data (e.g., audio, video, image) for remote environments. The area coverage reflects the perception of WMSNs to the surrounding environment, where a good coverage effect can ensure effective data collection. Given the harsh and complex physical environment of WMSNs, which easily form the sensing overlapping regions and coverage holes by random deployment. The intention of our research is to deal with the optimization problem of maximizing the coverage rate in WMSNs. By proving the NP-hard of the coverage enhancement of WMSNs, inspired by the predation behavior of army ants, this article proposes a novel swarm intelligence (SI) technology army ant search optimizer (AASO) to solve the above problem, which is implemented by five operators: army ant and prey initialization, recruited by prey, attack prey, update prey, and build ant bridge. The simulation results demonstrate that the optimizer shows good performance in terms of exploration and exploitation on benchmark suites when compared to other representative SI algorithms. More importantly, coverage enhancement AASO-based in WMSNs has better merits in terms of coverage effect when compared to existing approaches. [ABSTRACT FROM AUTHOR]

As natural habitats protection has become a global priority, smart sensing-nets are ever-increasingly needed for effective environmental observation. In a practical monitoring network, it is critical to deploy sensors with sufficient automated intelligence and motion flexibility. Recent advances in robotics and sensors technology have enabled automated mobile sensors deployment in a smart sensing-net. Existing deployment algorithms can be employed to calculate adequate destinations (goals) for sensors to perform respective monitoring tasks. However, given the calculated goal positions, the problem of how to actually coordinate a fleet of robots and schedule moving paths from random initials to reach their goals safely, without collisions, remains largely unaddressed in the wireless sensor networking (WSN) literature. In this paper, we investigate this problem and propose polynomial-time collision-free motion algorithms based on batched movements to ensure all the mobile sensors reach their goals successfully without incurring collisions. We observe that the grouping (batching) strategy is similar to the coloring procedure in graph theory. By constructing a conflict graph, we model the collision-free path scheduling as the well-known k-coloring problem, from which we reduce to our k-batching problem (determining the minimum number of required batches for a successful deployment) and prove its NP completeness. Since the k-batching problem is intractable, we develop CFMA (collision-free motion algorithm), a simple yet effective batching (coloring) heuristic mechanism, to approximate the optimal solution. Performance results show that our motion algorithms outperform other existing path-scheduling mechanisms by producing 100% sensors reachability (success probability of goals reaching), time-bounded deployment latency with low computation complexity, and reduced energy consumption. Note to Practitioners— This research was originally motivated by an oceanography project, which studied marine microbes by sending a team of tiny robots (sensors) randomly scattered on the ocean floor. For hard-to-access habitats like deserts or oceans, where manual placement of sensors is costly or impossible, automatically scheduling robots movements to calculated positions from random initials is essential for an effective monitoring. Our contribution is unique in two ways. First, traditional path-planning research focuses more on independent robots navigating the environment, whereas we target on the network automation problem, coordinating a fleet of robots working together to perform an environmental sensing task. Second, we observe that existing movement methods are too complicated and energy-consuming due to the calculation on-the-go nature. To provide a practical solution, we suggest and design our motion algorithms from a new perspective: pre-scheduled batched movements. Our approach requires a central server for grouping (batching) calculations, but can effectively reduce computation complexity and save significant energy expenditure exerted on resource-limited sensors. The proposed collision-free motion algorithm (CFMA) is a suboptimal yet efficient batching solution, which can be easily applied in real-life open-space monitoring networks. Insights from this foundational research will facilitate future opportunities to implement and verify our method in operational monitoring testbeds. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT), which describes the equipment of physical objects with sensors, actuators, computing logic, and connectivity, has attracted much attention. Although many facets of the IoT have already been explored, existing works either treat smart things as black box or focus on their technical characteristics. From an engineering management perspective, however, also a profound understanding of nontechnical smart thing characteristics is key. Hence, in this article, we develop and evaluate a corresponding multilayer taxonomy based on the latest IoT literature and a deliberately broad sample of 200 smart things, which covers the diversity of smart things available on the market. Based on 11 dimensions, the taxonomy enables classifying smart things according to the layers of established IoT architectures. Based on our sample, we infer five clusters, each covering a typical combination of nontechnical smart thing characteristics occurring in practice. These results extend our understanding of the IoT by structuring nontechnical characteristics of smart things and by abstracting the diversity of smart things into artefacts with manageable complexity. Our results inspire future research on the adoption, affordances, and design of smart things. Moreover, engineering managers can use our results in early phases of product development and process reengineering projects. [ABSTRACT FROM AUTHOR]

Shoe-mounted wearable sensors may serve a variety of important purposes (activity recognition, energy expenditure estimation, and so on) by providing rich information about human locomotion. However, reliably attaching such sensors to shoes still remains a challenge. Furthermore, pressure-sensing elements in such shoe sensor systems often suffer from poor durability due to the large dynamic load. This work presents a novel shoe-mounted sensor named Smart Lacelock, which takes the form of a common shoelace tensioning device (shoelace lock). With its unique form factor, the Smart Lacelock can be securely attached to the top of a shoe with minimal effort, enabling its embedded inertia measurement unit (IMU) to provide reliable 3-D motion measurement of the foot. Furthermore, the Smart Lacelock incorporates a load cell to measure the force applied by the shoelace, providing valuable information related to ankle movement, foot/shoe shape change, and ground force. Design details of the device are presented, including the mechanical structure and electronic circuitry. We also conducted a ten-participant human study, in which signals were recorded during free ankle swing, body weight (BW) shifting, sit-to-stand (STS), and overground walking. The results demonstrated the Smart Lacelock’s capability of providing consistent and observable responses to single contributing factors (ankle movement and foot loading) as well as complex movements with clearly defined events of interest (STS and walking). Such unique capability suggests that the Smart Lacelock may serve as an important source of human movement information to support related applications such as activity recognition and gait event detection. [ABSTRACT FROM AUTHOR]

Knee osteoarthritis (Knee OA) is a degenerative disease that often perplexes the elderly and the whole society, and its timely recognition receives interest worldwide. However, traditional imaging examinations cannot reflect dynamic function nor implement long-term monitoring. To address this issue, this article suggests a piezoresistive-based gait monitoring method to recognize patients with Knee OA by assessing the plantar pressure signals during the subjects’ walking, which is mobile, wearable, low-cost, and convenient. Eighteen subjects diagnosed with Knee OA and twenty-two control subjects participated in the experiment. Considering the asymmetric pressure distribution in feet and the landing habits of Knee OA patients, the plantar surface was split into eight areas, calculating the contact time and maximum force of each area in a gait cycle. Using these characteristics to train, the support vector machine (SVM) reached an accuracy of 93.15%, a precision of 92.39%, and a recall of 92.79%. Furthermore, a prediction model was proposed for the application that aggregates all the results in one test and gives a more accurate result, and the classification accuracy for individuals in the ensemble model is 90.90%. Our technique fills the vacancy of the recognition of patients with Knee OA based on wearable instruments. It provides ideas for intelligent healthcare, which benefits potential Knee OA patients’ early diagnosis and treatment.

On-line detection of anomalies in time series is a key technique used in various event-sensitive scenarios such as robotic system monitoring, smart sensor networks and data center security. However, the increasing diversity of data sources and the variety of demands make this task more challenging than ever. First, the rapid increase in unlabeled data means supervised learning is becoming less suitable in many cases. Second, a large portion of time series data have complex seasonality features. Third, on-line anomaly detection needs to be fast and reliable. In light of this, we have developed a prediction-driven, unsupervised anomaly detection scheme, which adopts a backbone model combining the decomposition and the inference of time series data. Further, we propose a novel metric, Local Trend Inconsistency (LTI), and an efficient detection algorithm that computes LTI in a real-time manner and scores each data point robustly in terms of its probability of being anomalous. We have conducted extensive experimentation to evaluate our algorithm with several datasets from both public repositories and production environments. The experimental results show that our scheme outperforms existing representative anomaly detection algorithms in terms of the commonly used metric, Area Under Curve (AUC), while achieving the desired efficiency. [ABSTRACT FROM AUTHOR]

Gas-liquid two-phase flow non-separation measurement plays a crucial role in industrial production and two-phase flow theory research. In this paper, an intelligent multi-sensing system is designed for horizontal gas-liquid flow measurement, which integrates near-infrared (NIR), differential pressure (DP) and acoustic emission (AE) technology. The interaction and disturbance information of the gas-liquid phase are detection by the AE and NIR sensors, and then transformed into the form and margin factor which reflect the void fraction and flow rate of gas liquid flow respectively. Both of them are input as feature variables into the least absolute shrinkage and selection operator (LASSO) machine learning model for predicting the liquid phase volume fraction, and the best fusion solution is proposed. The Analysis and evaluation have been conducted in several classic correlations, and the Collins correlation is selected to optimize by LASSO optimization algorithm. The prediction accuracy is improved by 7.14% compared to the Collins correlation, and the MAPE for liquid mass flow rate of slug flow was 8.75% and 89.47% of the predictions were within 20% relative error. [ABSTRACT FROM AUTHOR]

Healthy sleep is an important indicator of good physical and mental health. The current technology employed for monitoring sleep quality is typically expensive and invasive, which demands innovations in respiration monitoring. In addition, recent effort in developing wearable respiration sensors has faced great challenges in concurrently obtaining low cost, high stretchability, and high sensitivity. Herein, we demonstrate a simple fabrication strategy to construct low-cost, stretchable, skin-breathable and ultrasensitive respiration sensors using pencil drawing, electrode pasting, and laser cutting methods. With smart designs, the sensors exhibited high sensitivity, good stability, decent skin-breathability and capability to detect respiration rate and depth in real-time. The sensors fabricated by the simple low-cost method in this study can be utilized to measure sleep quality, providing warning signals for apnea and sleep disorders. The proposed technology holds promise for developing cost-effective wearable ultrasensitive sensors for sleep quality monitoring. [ABSTRACT FROM AUTHOR]

Public transportation is expensive to operate and maintain and is often unsatisfactory. The attractiveness of public transportation can be enhanced by making it more seamless, which, in turn, would reduce financial constraints and inefficiencies. The adoption of mobile devices for ticketing solutions is promising. However, current solutions are often inflexible and require manual interactions that produce evanescent data. Therefore, using leading-edge technologies and infrastructure, it is desirable to develop a solution to fully automate fare collection. In this paper, we provide a comprehensive literature review to understand the state of public transportation and to facilitate the development and implementation of automated fare collection solutions. First, we discuss existing mobile technologies and their common ticketing implementations. Second, we provide a predictive behavior model with sensor analytics to better understand customer needs. Finally, we highlight how machine learning can harness transactional ticketing data to create valuable business intelligence. Overall, developing and implementing automated fare collection solutions in urban transportation is expected to have a significant positive impact on customer experiences, the emergence of new business models and the reduction of pollutant emissions. [ABSTRACT FROM AUTHOR]

Due to the growing awareness of driving safety and the development of sophisticated technologies, advanced driving/driver assistance system (ADAS) has been equipped in more and more vehicles with higher accuracy and lower price. The latest progress in this field has called for a review to sum up the conventional knowledge of ADAS, the state-of-the-art researches, novel applications and standards in real world. With the help of this kind of review, newcomers in this field can get basic knowledge easier and other researchers may be inspired with potential future development possibility. This paper makes a general introduction about ADAS by analyzing its hardware support, computation algorithms and current development state. Different types of perception sensors are introduced from their interior feature classifications, installation positions, supporting ADAS functions, and pros and cons. The comparisons between different sensors are concluded and illustrated from their inherent characters and specific usages serving for each ADAS function. The current algorithms for ADAS functions are also collected and briefly presented in this paper from both traditional methods and novel ideas. Additionally, discussions about current regulations and market state of ADAS in China are reviewed in this paper, and other open issues related to ADAS are also introduced in particular. [ABSTRACT FROM AUTHOR]

Sensing and communication are the two major concerns in vehicle-road collaboration system, whose function realization needs the support of RSE (roadside equipment). A misleading deployment incurs in waste of valuable resources and degradation of the network performance. Based on the combination strategy, this study proposed a hybrid allocation method to deploy RSE on a highway by considering the sensing capability of traditional ITS sensors and the communication feature of popular networking devices. Two sorts of RSE, the one integrated ITS sensor and networking device, named sensing & communication-RSE (scRSE), and the other belongs to networking devices, named communication-RSE (cRSE), were involved. A hybrid RSE allocation framework was proposed, in which an optimal method was adopted to deploy scRSE on key points for data acquisition and a connectivity probability-based uniform method was used to deploy cRSE for communication. The effectiveness of these methods was verified by case analysis. The results indicated that the proposed hybrid method was very effective and outperformed the conventional uniform deployment method. The key parameters, including the coverage range, RSE spacing, and vehicle density, had an important impact on network connectivity probability. To obtain high connectivity probability, a lower density of connected vehicles (CVs) needs the support of wide coverage of RSE, and vice versa. When the density of CVs reaches to a certain value, vehicular networks could be realized without the help of RSE. Benefitting from the mobility of CVs, increasing their transmission range was better than increasing RSE coverage radius in improving connectivity probability. [ABSTRACT FROM AUTHOR]

Cooperative Intelligent Transport Systems (C-ITS) are emerging in the field of transportation systems, which can be used to provide safety, sustainability, efficiency, communication and cooperation between vehicles, roadside units, and traffic command centres. With improved network structure and traffic mobility, a large amount of trajectory-based data is generated. Trajectory-based knowledge graphs help to give semantic and interconnection capabilities for intelligent transport systems. Prior works consider trajectory as the single point of deviation for the individual outliers. However, in real-world transportation systems, trajectory outliers can be seen in the groups, e.g., a group of vehicles that deviates from a single point based on the maintenance of streets in the vicinity of the intelligent transportation system. In this paper, we propose a trajectory deviation point embedding and deep clustering method for outlier detection. We first initiate network structure and nodes’ neighbours to construct a structural embedding by preserving nodes relationships. We then implement a method to learn the latent representation of deviation points in road network structures. A hierarchy multilayer graph is designed with a biased random walk to generate a set of sequences. This sequence is implemented to tune the node embeddings. After that, embedding values of the node were averaged to get the trip embedding. Finally, LSTM-based pairwise classification method is initiated to cluster the embedding with similarity-based measures. The results obtained from the experiments indicate that the proposed learning trajectory embedding captured structural identity and increased F-measure by 5.06% and 2.4% while compared with generic Node2Vec and Struct2Vec methods. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) that introduces Internet of Things (IoT) technology into industrial environments is beneficial to construct smart factories. It utilizes various sensors to collect the data of industrial devices. These data are analyzed to improve the manufacturing efficiency and product quality. Cloud storage provides a solution for storing data outsourced, especially for sensors that have limited local storage and computational capacity. To ensure the privacy preserving of devices, the collected data should be stored in the formal ciphertext. Therefore, encrypted data sharing should be implemented to analyze the devices’ data. In this article, the cloud storage solution for sensors is considered. To achieve a secure and efficient data storage and sharing, a novel group signature scheme, which has less computation overhead and communication overhead, is designed to realize anonymous authentication first. And then, a novel blockchain-based cloud storage protocol for sensors in IIoT is constructed on basis of the proposed group signature scheme. Smart contract and proxy re-encryption are utilized in this protocol to realize secure data sharing with a less computational overhead. Furthermore, security proofs and performance evaluations demonstrate that this protocol is secure, privacy-preserving, and has at least 40% and 20% performance improvement in data storage and sharing phase, respectively. [ABSTRACT FROM AUTHOR]

The itemized product identification, sensing and data integration are critical building blocks for the efficiency, flexibility, and potential intelligence of the Industrial Internet of Things (IIoT). The product sensing and data integration solutions usually suffer from the sophisticated sensor devices and their inflexible integration with the industrial processes, which may restrict the performance of industrial systems. To maintain an information link for convenient product data integration, this investigation presents a surface mountable dual-mode Radio Frequency Identification (RFID) sensor as a wirelessly powered information interface for simultaneous product identification, sensing, and data integration. The focus of this investigation includes: (1) the theory and hardware/software scheme for sensor-augmented RFID integrating Ultra-High Frequency (UHF) and High Frequency (HF) with Commercial-off-the-Shelf (COTS) devices; (2) integration of the UHF and HF energy and data interfaces to support both UHF and HF powering and sensing; (3) a data integration method supporting sensor data transmission using common UHF and HF readers without extra hardware or software cost; (4) proposal of a model for industrial integration with the proposed dual-mode RFID sensor. The experimental studies and exemplar application demonstrate the feasibility of the presented techniques for industrial integration. [ABSTRACT FROM AUTHOR]

Millimeter-wave radar and machine vision are both important means for intelligent vehicles to perceive the surrounding environment. Aiming at the problem of multi-sensor fusion, this paper proposes the object detection method of millimeter-wave radar and vision fusion. Radar and camera complement each other, and radar data fusion in machine vision network can effectively reduce the rate of missed detection under insufficient light conditions, and improve the accuracy of remote small object detection. The radar information is processed by mapping transformation neural network to obtain the mask map, so that radar information and visual information in the same scale. A multi-data source deep learning object detection network (MS-YOLO) based on millimeter-wave radar and vision fusion was proposed. Homemade datasets were used for training and testing. This maximized the use of sensor information and improved the detection accuracy under the premise of ensuring the detection speed. Compared with the original YOLOv5 (the fifth version of the You Only Look Once) network, the results show that the MS-YOLO network meets the accuracy requirements better. Among the models, the large model of MS-YOLO has the highest accuracy with an mAP reaching 0.888. The small model of MS-YOLO has good accuracy and speed, and the mAP reaches 0.841 while maintaining a high frame rate of 65 fps. [ABSTRACT FROM AUTHOR]

Existing surveys look at parking solutions from the perspective of sensors, communication protocols, and the hardware-software interface. While this is a worthwhile approach, it suffers from three obvious shortcomings, namely that present-day sensors are likely to become obsolete in a few years, communication protocols get discontinued, and present-day software will almost certainly not run on tomorrow’s platforms. Consequently, these approaches are not promising for the Smart Cities of the near future. Unlike previous surveys, we look at parking in Smart Cities through the lens of market-based allocation of goods and services. In competitive markets prices act as signals used to allocate goods to those who value them most. In the case of parking spots, some drivers are willing to pay higher prices for the use of those parking spots that offer them the highest utility. What makes our survey unique is that we are looking at the recent literature with an eye for market-oriented solutions including pricing as an instrument for shaping traffic and for incentivizing socially-desirable driver behavior. We believe that one of the important contributions of any survey paper, over and above being a compendium of known art, is to suggest new lines of research. With this in mind, we have peppered the manuscript with slightly unorthodox perspectives. These perspectives are intended to be thought-provoking and to open new avenues for possible investigations. [ABSTRACT FROM AUTHOR]

Nowadays, a large number of sensors are equipped on mobile or stationary platforms, which continuously generate geo-tagged and time-stamped readings (i.e., geo-sensory data) that contain rich information about the surrounding environment. These data have irregular space and time coordinates. To represent geo-sensory data, there have been extensive research efforts using time sequences, grid-like images, and graph signals. However, there still lacks a proper representation that can describe both the mobile and stationary geo-sensory data without the information-losing discretization in spatial and temporal dimensions. In this paper, we propose to represent massive geo-sensory data as spatio-temporal point clouds (STPC), and present STPC-Net, a novel deep neural network for processing STPC. STPC leverages the original irregular space-time coordinates, and STPC-Net captures intra-sensor and inter-sensor correlations from STPC. In this way, STPC-Net learns the key information of STPC, and overcomes challenges in data irregularity. Experiments using real-world datasets show that STPC-Net achieves state-of-the-art performance in different tasks on both mobile and stationary geo-sensory data. The source code is available at https://github.com/zhengchuanpan/STPC-Net. [ABSTRACT FROM AUTHOR]

Low-Power Wide-Area Network (LPWAN) is an emerging communication standard for Internet of Things (IoT) that has strong potential to support connectivity of a large number of roadside sensors with an extremely long communication range. However, the high operation cost to manage such a large-scale roadside sensor network remains as a significant challenge. In this article, we propose Low Operation-Cost LPWAN (LOC-LPWAN), a novel optimization framework that is designed to reduce the operation cost using the cross-technology communication (CTC). LOC-LPWAN allows roadside sensors to offload sensor data to passing vehicles that in turn forward the data to a LPWAN server using CTC aiming to reduce the data subscription cost. LOC-LPWAN finds the optimal communication schedule between sensors and vehicles to maximize the throughput given an available budget. Furthermore, LOC-LPWAN optimizes the fairness among sensors by preventing certain sensors from dominating the channel for data transmission. LOC-LPWAN can also be configured to ensure that data packets are received within a specific time bound. Extensive numerical analysis performed with real-world taxi data consisting of 40 vehicles with 24-hour trajectories demonstrate that LOC-LPWAN reduces the cost by 50% compared with the baseline approach where no vehicle is used to relay packets. The results also show that LOC-LPWAN improves the throughput by 72.6%, enhances the fairness by 65.7%, and reduces the delay by 28.8% compared with a greedy algorithm given the same amount of budget. [ABSTRACT FROM AUTHOR]

The widespread proliferation of smart sensors has led to hardware that enable edge intelligence (EI) with extreme energy efficiencies. This decreases the volume of data that is transmitted to the cloud, thus reducing: 1) processing latency; 2) communication energy; and 3) network congestion. However, this comes with an added cost of computation at the edge node. The cost (energy/latency) of edge computation and the cost of communication to the cloud vary widely depending on operating conditions, which include: 1) information content in the data; 2) algorithm selection; 3) channel conditions (noise, path-loss, etc.); 4) network size, available bandwidth; and 5) resources at the cloud. This article presents a 65 nm wireless image processing SoC for real-time computation-communication trade-off on resource-constrained edge devices. The test-chip includes: 1) an all-digital, near-memory, reconfigurable, and programmable neural-network (NN)-based systolic image processor; 2) a digitally adaptive radio-frequency digital-to-analog converter (RF-DAC)-based transceiver; and 3) a mixed-signal, time-based, actor-critic (AC) neuro-controller with compute-in-memory (CIM) and in-place weight updates that provide online learning and adaptation for efficiently controlling the computation, communication blocks separately as well as jointly. The major contributions of the proposed SoC are threefold: 1) a wireless Internet of Things (IoT) SoC architecture enabling a generic computation-communication trade-off scheme; 2) a novel CIM circuit design enabling effective AC control and online learning (0.59 pJ/MAC, 0.4 pJ/update); 3) integration of programmable deep NN (DNN) accelerator (1.05 TOPS/W) and reconfigurable transceiver (184 pJ/b @ –15 dBm) supporting versatile cloud-edge collaborations; and 4) significant system-level energy efficiency improvement (5.7 $\times $) with real-time on-chip smart control enabled by seamless chip integration and AI-enabled decision-making. Furthermore, this SoC serves as a system-level IoT prototype for next-generation context-aware EI. [ABSTRACT FROM AUTHOR]

This work demonstrates the design, implementation, and experimental results of a low-cost disposable flexible sensor system capable of both impact localization and measurement. The proposed flexible sensor structure utilizes a special series of Bristol paper as the main fabrication material, which is coated with electric paint graphite paste and silver paste. The implemented sensor system uses a planar absolute encoder-like sensing topology to locate the impact and has a low-cost and quick manufacturing process. The size of the structure is ${210}\times {18}.{56}$ mm with a thickness of approximately 340 $\mu \text{m}$. It has an electronic read-out consisting of three identical Wheatstone bridge circuits and instrumentation amplifiers for each bit. It can detect the external forces in the range of 0.6N to 12N with a spatial resolution of 2.4 cm and 0.55 cm in horizontal and vertical axes, respectively. The proposed sensor structure is tested in a series of experiments using a robotic setup consisted of a pantograph mechanism and a direct drive linear motor. The experiments illustrate the results with measurement sensitivity as small as 1N and proper fatigue resilience against repetitive loads. [ABSTRACT FROM AUTHOR]

The shearer drum height measurement plays a crucial role in the fully mechanized mining face. This paper proposes a measurement method of drum height, taking the ranging arm swing-angle and fuselage attitude angle into consideration; therefore, compared with the existing drum height measurement methods, our approach adheres more to the actual operating conditions of the shearer. Based on our approach, we further developed a shearer drum height sensor. Firstly, TLE5012B (MEMS magnetic angle chip) and MPU6050 (MEMS inertial navigation chip) are selected as the sensing elements of swing-angle and attitude angle, respectively. Secondly, a “rocker-rotating shaft” is designed to convert the swing-angle into the rotation of TLE5012B’s external magnetic field. Thirdly, an intelligent drum height sensor based on DSP-TMS320F28335 is developed, including hardware circuits and software that measure and process the angle data, calculate the drum height, store and display the data, and transmit the data by CAN or WIFI. Fourthly, the magnetic field distribution and vibration source of the shearer are simulated and analyzed, and the magnetic shielding shell and anti-vibration device are designed to reduce the influence of external factors on the sensor. Furthermore, a sensor prototype was developed and experimentally tested. The maximum absolute errors of sensor swing-angle, pitch angle, roll angle, and drum height were 0.16°, 0.07°, 0.06°, and 6.7mm, respectively. Finally, we establish a measurement error model of shearer drum height and calculate the maximum measurement error is 2.24cm. The results show that our approach and sensor meet the requirements of shearer drum height measurement. [ABSTRACT FROM AUTHOR]

In the last fifteen years, there has been a widespread diffusion of wearable sensorized devices for a plethora of applications in heterogeneous domains. Wearable technology provides fundamental capabilities such as smart sensing, monitoring, data recording, and multi-modal interaction, in a seamless, pervasive, and easy-to-use way. An emerging research trend is the definition of situation-aware wearable computing systems, i.e., wearable devices able to perceive and understand what is happening in the environment in order to adapt their behavior and anticipate users’ needs, a capability known as situation awareness. Although the increasing interest of the research community in situation-aware wearable devices, there is a lack of studies, formal models, methodological approaches, and theoretical groundings on which these systems can be grounded. As a result, a very limited number of smart sensors (physical or virtual) capable of effectively and efficiently supporting Situation Awareness have been proposed so far. In this article, we provide a survey and a classification of state-of-the-art situation-aware wearable systems, outlining current research trends, shortcomings, and challenges, with an emphasis on the models, approaches, and computational techniques of situation awareness and wearable computing on which they are based. The survey has been performed using the PRISMA methodology for systematic reviews. The analysis has been conducted with respect to a reference architecture, namely SA-WCS, of a generic situation-aware wearable computing system that we propose in this article, grounded on Endsley’s model of Situation Awareness. Such reference architecture not only provides a systematic framework for the comparison and categorization of the works, it also aims to promote the development of the next generation WCS. [ABSTRACT FROM AUTHOR]

In this paper, a sensor-fusion based smart hydroponic system has been proposed. The system aims to monitor and regulate environmental conditions to boost the yield of plants grown using hydroponics. The system employs multiple sensors synchronized to work and communicate to a common server giving shape to sensor fusion and the Internet of Things. The proposed system reduces human intervention to a minimum, by operating autonomously. The Random Forest algorithm has been used to help the system identify the ambient parameter to be regulated based on priority, thus making the system intelligent. By using the randomized priority-based selection of operations, the algorithm determines the parameter to be altered, based on the input from the sensors, reducing the sensor crowding problem. The ability to regulate nutrient concentration is a salient feature of the proposed system. Sensor fusion along with the Random Forest algorithm has been implemented in a hardware prototype of the proposed system. The system was able to save 20.4% of the peak power consumption while temperature and water level regulation and 82.1 % of peak power while light regulation, thus making the system smarter and more energy-efficient for a hydroponic system. [ABSTRACT FROM AUTHOR]

Sparse data injection attacks are data injection attacks that target a limited number of sensors in the smart grid paradigm. In this paper, sparse attack construction strategy is proposed for the information-theoretic stealth attacks, in which the attacker minimizes the amount of information acquired by the operator about the power system from the observations and the probability of being detected simultaneously. For Gaussian distributed state variables, closed-form expression is proposed for the optimal Gaussian sparse attacks when the attacker targets deterministic observations and gets access to the full system information. Based on that, two target observation selection strategies are introduced to find the vulnerable observations in the system, in which the algorithms take different tradeoff strategies between performance and computational time. Furthermore, a locally perfect stealthy attack construction strategy is proposed for the case in which the attacker only has partial system information but gets access to real-time realizations of the local state variables. The optimality of Gaussian sparse attacks and the performance of the selection strategies are validated on the IEEE 30 Bus and 118 Bus test systems. [ABSTRACT FROM AUTHOR]

This paper presents a suite of analytics that are proposed to be embedded in next-generation smart sensors in electric power grids. The proposed analytics take the electrical signals as the input and unlock the full potential in signal processing and machine learning for real-time event detection and classification. Meanwhile, a robust synchrophasor estimation mechanism is housed within the proposed sensor technology that will be triggered following a detected event and guides on the adaptive selection of the best-fit (most accurate) synchrophasor estimation algorithms at all times. Embedding such analytics within the sensor and closer to where the waveforms are captured, the proposed distributed intelligence solution technology mitigates the potential risks to communication failures and latencies as well as malicious cyber threats. Our experiments demonstrate that the introduced scheme achieves improved quality of measurements with a promising event detection and classification accuracy, collectively resulting in enhanced online situational awareness in modern power grids. [ABSTRACT FROM AUTHOR]

The advancement of wireless sensor networks (WSNs) improves various smart home automation services and home users’ living standards. However, efficiently collecting data and automating smart home services require the extensive deployment of the sensors. Thus, one of the crucial and challenging tasks is to minimize the sensors’ energy consumption for monitoring and automating various activities in a smart home. In this article, we present a solution to control the excessive energy consumption of sensors used to detect various activities of daily living (ADL) of a smart home resident. The sensors within a smart home network are divided into various groups employing the recurrent neural network (RNN) and dynamic time warping (DTW) techniques to predict the activities with high accuracy and less energy consumption. The smart home users’ future activities are forecast with bidirectional long short-term memory (BLSTM) RNN model to select those sensors that are likely to predict the upcoming activities. Similarly, to predict the home users’ unusual activities, a guard sensor is elected among sensors with high similarities with each other using DTW. The sensor’s role is evenly switched between different modes to maintain a fair tradeoff between energy and accuracy. An extensive set of simulations is performed to validate the proposed scheme’s work integrating datasets from authentic sources. Finally, the proposed system significantly reduces the sensors’ energy consumption and prolongs the battery lifetime to approximately 137 days. Note to Practitioners—This article presented an energy-efficient duty-cycling scheme for automating sensors’ operations in a smart home scenario. The traditional duty-cycling schemes mainly provide solutions based on appointing sentries or predicting smart home users’ next activity using models, such as Bayesian networks. We design a system that integrates the advantages of both sentry and prediction-based schemes to reduce the amount of energy required by sensors to detect and automate smart home users’ activities with high accuracy and precision. The active sensors are appointed using a bidirectional long short-term memory recurrent neural network. Similarly, the guard sensors are assigned to detect unusual activities using the similarities among idle sensors. This study could be used to automate the smart home sensors for detecting home user’s activities with less energy, which ultimately prolongs the battery lifetime of the sensors. [ABSTRACT FROM AUTHOR]

With the advancement of wireless communication technologies and rich embedded sensors in smart mobile devices, mobile crowdsensing has become an attractive paradigm for collecting sensing data from surrounding environments. Effective incentive mechanisms often play an important role in guaranteeing high quality of services and wide involvement of workers. Much work has been carried out in this aspect to encourage workers to strenuously and truthfully provide high quality data. However, task quality does not only depend on workers’ subjective effort or truthfulness, but also workers’ distribution and task quality growth law. In this paper, we consider a scenario where worker arrivals are unevenly distributed and the growth of task quality conforms to the law of diminishing margin of workers’ efforts. We propose a Quality-driven Online Task-Bundling-based incentive mechanism (QOTB). The design objective is to maximize the social welfare while maximally satisfying the task quality requirements. In QOTB, we introduce Mental accounting Theory to build accounts for task execution profit and bonus, respectively, which are then used to derive the participation willingness of workers. We adopt task bundling to stimulate workers to change their original travel schedules for balancing the task participations according to the popularities of task locations and also the traveling cost. We present the detailed mechanism design of QOTB. We prove that the QOTB mechanism has desired properties of willingness truthfulness, individual rationality, and computation efficiency. We conduct extensive simulations and the results show that QOTB can effectively improve the social welfare while satisfying the task quality requirements. [ABSTRACT FROM AUTHOR]

Flexible sensors with surface suitability for complex-shaped objects generally sense object motion through a contact mode, which limits their application range and shortens their service life. The development of flexible sensors with noncontact working modes can overcome these shortcomings. Herein, superhydrophobic and self-powered flexible sensors based on a triboelectric nanogenerator (TENG) were fabricated by a step-by-step swelling-permeation method to prepare natural latex films with carbon black and silica nanoparticles embedded on their inner and outer surfaces, respectively. In addition to sensing object motion through contact, the TENG-based sensor can also sense charged object motion in noncontact mode based on the principle of electrostatic induction. The TENG-based sensor can sense horizontal and vertical movements of objects above it and can even detect the movement of objects with speeds as low as 0.04 $\text{m}/\text{s}$. Interestingly, the sensor’s ability to monitor object motion in noncontact mode remains unchanged even at 70% strain. The TENG-based sensor can also be used to distinguish the type and amount of charge carried by an object and can harvest energy from moving objects in noncontact mode. Combining a single-electrode TENG with a freestanding TENG allows monitoring of the motion direction of an object loading an unknown type of charge in a noncontact mode. Based on the characteristics of our TENG-based flexible sensors to sense moving objects in both contact and noncontact modes, they have potential applications for human-machine interfaces, smart wearable devices, and flexible robots. [ABSTRACT FROM AUTHOR]

Infections triggered by fungi of the genus Candida are widely known, although the high incidence and mortality factors are still unclear. The classic methods of identifying Candida species are subject to errors, requiring new techniques with faster and more accurate performance. We present a study for identifying fungi species by analyzing volatile organic compounds of cultures acquired and interpreted using Electronic Nose and Artificial Intelligence methods. The proposed approach contributes to establishing an agile and appropriate treatment, reducing the complications of the disease and the number of deaths. We perform experiments with three species of Candida obtaining accuracy above 90% in the fungi identification. Therefore, future works are encouraged to deal with more types of fungi to help create a new identification methodology faster and more reliable using artificial intelligence methods. [ABSTRACT FROM AUTHOR]

Ego-vehicle state estimation can be achieved by utilizing classic vehicle and environmental sensors. However, each type of sensor has specific strengths and limitations regarding accuracy and robustness. Using existing sensor concept cannot fulfill the requirements for both accuracy and robustness against changing vehicle parameters and environments while driving. Therefore, we propose a framework to exploit the advantages of each individual sensor type seeking high accuracy and robustness. The uncertainty of state variables is estimated by the fusion of an inertial measurement unit, a global navigation satellite system, a radar sensor, and a lidar sensor. The multimodal sensor data are processed in a distributed manner in each sensor model at a low level, and the physical quantities from the sensor models are fused centrally by a probabilistic method, namely, the synchronized error-state extended Kalman filter, at a high level. We propose an event-based approach to estimate sensor system delay and compensate lagged signals using forward prediction fusion. The concept was implemented in a test vehicle and evaluated in field tests for dynamic driving and on public roads. The algorithm represents real-time estimation with high accuracy for different driving maneuvers and robustness against different disturbances caused by the environment and changing chassis parameters. [ABSTRACT FROM AUTHOR]

Obtaining voyage data through a vessel monitoring system (VMS) and transmitting it to a remote monitoring center through satellites is an important link to realize the information perception of smart ships. However, improving data quality to ensure the integrity of information is still an open question. Taking into account the complex and harsh marine environment, a BeiDou satellite transmission link will inevitably suffer the challenge of packet loss. Therefore, this paper proposes a novel BeiDou satellite transmission framework with missing package imputation to improve the information perception ability of smart ships. In particular, we propose a two-stage missing packet imputation strategy (T-MPI). First, a bidirectional recurrent neural network (Bi-RNN) based on temporal view imputation is designed to learn the time correlation of the data. Second, considering the attributive view imputation of multivariate data, an autoencoder with a linear aggregation module (LA-AE) is proposed to capture sudden changes in the ship’s complex sailing state. An experimental platform was established to verify the developed framework, and real freighter transportation data were used to prove the effectiveness of our proposed imputation strategy under complex sailing conditions. [ABSTRACT FROM AUTHOR]

Network security issues are significant for cyber-physical systems while existing wireless networks are vulnerable to cyber-attacks. This paper proposes a novel detector for stealthy attacks in distributed consensus-based filtering, which has fewer objects to detect and thus can reduce the time consumption of the detector. Based on the proposed detector, we can analyze the estimated performance of the distributed estimator under a stealthy attack. The purpose of this study is to analyze the properties and performance of the estimator equipped with the proposed detector depending on the adversary’s available resources. First, we study the sufficient condition for distributed state estimation to be resilient based on the assumption that the adversary has sufficient resources. With limited adversary resources, the optimal Kalman gain can be obtained by minimizing state estimation error covariance. Furthermore, a sufficient condition is presented to guarantee the convergence of the estimation error covariance in the estimator equipped with the proposed detector. Finally, a simulation example is provided to demonstrate the effect of the detector on the distributed estimator. [ABSTRACT FROM AUTHOR]

We propose a periodic event-based control scheme in which, when the sensor triggers an event, also past and subsequent values of the controlled variable are transmitted. We employ this idea to obtain a stability condition independent of the used event-triggering mechanism. In turn, we exploit this independence to devise a methodology for extending existing controller (auto)tuning rules to the event-based setting and provide an example with the internal model control PID (IMC-PID) rule. We then abstract the functionalities that a wireless sensor node has to offer for implementing our scheme and finally present such a node, physically realized in a completely hardware-based form to the advantage of both energy efficiency and security. Simulations results and experiments with the so-obtained control architecture (wireless sensor node plus event-based IMC-PID) are reported. [ABSTRACT FROM AUTHOR]

Availability of the big data from Bluetooth MAC Scanners (BMS) over the network provides opportunities to trace the movement of the individual Bluetooth-equipped vehicles on the network. However, BMS might not perfectly detect all the devices within its detection zone. For dense urban networks, the scanner scanning zones can significantly overlap, which complicates the detailed reconstruction of the vehicle trajectory. Addressing the need, this paper proposes a Slit based Trajectory Reconstruction (STATER) algorithm where for each BMS, a slit is defined that considers the overlap and connectivity with other BMS, and thereafter the trajectory is reconstructed considering the shortest path over the observed sequence of slits. A numerical simulation framework is proposed to thoroughly test the proposed STATER algorithm at various levels of ambiguity and randomness in the input dataset. The testing results indicate that the reconstructed trajectories could capture more than 90% (true positive) of the actual path and an average error (false positive) of 11.3% at different randomness levels considered in the experiments. As proof of concept, STATER is applied on one-day data from the entire Brisbane network with 0.56m trips, the computational performance for which supports its practical applicability. [ABSTRACT FROM AUTHOR]