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An investigation was performed to develop a process to design and manufacture a 3-D smart skin with an embedded network of distributed sensors for non-developable (or doubly curved) surfaces. A smart skin is the sensing component of a smart structure, allowing such structures to gather data from their surrounding environments to make control and maintenance decisions. Such smart skins are desired across a wide variety of domains, particularly for those devices where their surfaces require high sensitivity to external loads or environmental changes such as human-assisting robots, medical devices, wearable health components, etc. However, the fabrication and deployment of a network of distributed sensors on non-developable surfaces faces steep challenges. These challenges include the conformal coverage of a target object without causing prohibitive stresses in the sensor interconnects and ensuring positional accuracy in the skin sensor deployment positions, as well as packaging challenges resulting from the thin, flexible form factor of the skin. In this study, novel and streamlined processes for making such 3-D smart skins were developed from the initial sensor network design to the final integrated skin assembly. Specifically, the process involved the design of the network itself (for which a physical simulation-based optimization was developed), the deployment of the network to a targeted 3D surface (for which a specialized tool was designed and implemented), and the assembly of the final skin (for which a novel process based on dip coating was developed and implemented.) [ABSTRACT FROM AUTHOR]

The adoption of the Internet of Things (IoT) in the mining industry can dramatically enhance the safety of workers while simultaneously decreasing monitoring costs. By implementing an IoT solution consisting of a number of interconnected smart devices and sensors, mining industries can improve response times during emergencies and also reduce the number of accidents, resulting in an overall improvement of the social image of mines. Thus, in this paper, a robust end-to-end IoT system for supporting workers in harsh environments such as in mining industries is presented. The full IoT solution includes both edge devices worn by the workers in the field and a remote cloud IoT platform, which is responsible for storing and efficiently sharing the gathered data in accordance with regulations, ethics, and GDPR rules. Extended experiments conducted to validate the IoT components both in the laboratory and in the field proved the effectiveness of the proposed solution in monitoring the real-time status of workers in mines. [ABSTRACT FROM AUTHOR]

Through the use of Underwater Smart Sensor Networks (USSNs), Marine Observatories (MOs) provide continuous ocean monitoring. Deployed sensors may not perform as intended due to the heterogeneity of USSN devices' hardware and software when combined with the Internet. Hence, USSNs are regarded as complex distributed systems. As such, USSN designers will encounter challenges throughout the design phase related to time, complexity, sharing diverse domain experiences (viewpoints), and ensuring optimal performance for the deployed USSNs. Accordingly, during the USSN development and deployment phases, a few Underwater Environmental Constraints (UECs) should be taken into account. These constraints may include the salinity level and the operational depth of every physical component (sensor, server, etc.) that will be utilized throughout the duration of the USSN information systems' development and implementation. To this end, in this article we present how we integrated an Artificial Intelligence (AI) Database, an extended ArchiMO meta-model, and a design tool into our previously proposed Enterprise Architecture Framework. This addition proposes adding new Underwater Environmental Constraints (UECs) to the AI Database, which is accessed by USSN designers when they define models, with the goal of simplifying the USSN design activity. This serves as the basis for generating a new version of our ArchiMO design tool that includes the UECs. To illustrate our proposal, we use the newly generated ArchiMO to create a model in the MO domain. Furthermore, we use our self-developed domain-specific model compiler to produce the relevant simulation code. Throughout the design phase, our approach contributes to the handling and controling of the uncertainties and variances of the provided quality of service that may occur during the performance of the USSNs, as well as reducing the design activity's complexity and time. It provides a way to share the different viewpoints of the designers in the domain of USSNs. [ABSTRACT FROM AUTHOR]

The reliability and scalability of Linear Wireless Sensor Networks (LWSNs) are limited by the high packet loss probabilities (PLP) experienced by the packets generated at nodes far from the sink node. This is an important limitation in Smart City applications, where timely data collection is critical for decision making. Unfortunately, previous works have not addressed this problem and have only focused on improving the network's overall performance. In this work, we propose a Distance-Based Queuing (DBQ) scheme that can be incorporated into MAC protocols for LWSNs to improve reliability and scalability without requiring extra local processing or additional signaling at the nodes. The DBQ scheme prioritizes the transmission of relay packets based on their hop distance to the sink node, ensuring that all packets experience the same PLP. To evaluate the effectiveness of our proposal, we developed an analytical model and conducted extensive discrete-event simulations. Our numerical results demonstrate that the DBQ scheme significantly improves the reliability and scalability of the network by achieving the same average PLP and throughput for all nodes, regardless of traffic intensities and network sizes. [ABSTRACT FROM AUTHOR]

Decision-making is a basic component of agents' (e.g., intelligent sensors) behaviors, in which one's cognition plays a crucial role in the process and outcome. Extensive games, a class of interactive decision-making scenarios, have been studied in diverse fields. Recently, a model of extensive games was proposed in which agent cognition of the structure of the underlying game and the quality of the game situations are encoded by artificial neural networks. This model refines the classic model of extensive games, and the corresponding equilibrium concept—cognitive perfect equilibrium (CPE)—differs from the classic subgame perfect equilibrium, since CPE takes agent cognition into consideration. However, this model neglects the consideration that game-playing processes are greatly affected by agents' cognition of their opponents. To this end, in this work, we go one step further by proposing a framework in which agents' cognition of their opponents is incorporated. A method is presented for evaluating opponents' cognition about the game being played, and thus, an algorithm designed for playing such games is analyzed. The resulting equilibrium concept is defined as adversarial cognition equilibrium (ACE). By means of a running example, we demonstrate that the ACE is more realistic than the CPE, since it involves learning about opponents' cognition. Further results are presented regarding the computational complexity, soundness, and completeness of the game-solving algorithm and the existence of the equilibrium solution. This model suggests the possibility of enhancing an agent's strategic ability by evaluating opponents' cognition. [ABSTRACT FROM AUTHOR]

The power requirement of Smart Building-Wireless Sensor Network (SB-WSN) is achieved using the piezoelectric energy harvester. This article proposes a novel high-performance bimorph W-shaped Piezoelectric Energy Harvester (W-PEH) using different ceramic piezoelectric materials and proof mass material combinations [PZT-5A & Copper, PZT-5H & Copper, PZT-5J & Copper, PZT-5A & Platinum, PZT-5H & Platinum and PZT-5J & Platinum]. Three cases of W-PEH without proof mass, with single proof mass and with split proof mass are developed to improve the output performance. The electrical performance results under open and short circuit conditions are examined. The simulation is performed using COMSOL Multiphysics software. [ABSTRACT FROM AUTHOR]

The paper presents the results of developing and creating wireless sensor networks for agriculture and medicine, which the authors have obtained during the past five years. The paper considers wireless sensor networks for estimating the state of plants by the method of chlorophyll fluorescence induction on large areas of agricultural land. The above networks include original smart sensors, built according to flexible architecture and containing elements of artificial intelligence based on neural networks. This construction scheme increases their performance in determining the effect of stress factors of different origins. The authors have constructed a smart remote medical monitoring system based on the wireless sensor network, which employs several wireless data exchange protocols. They also have created a diagnostic system to assess the quality of life and the symptoms associated with the diseases and (or) treatment. It determines the patient's health state using international and national questionnaires. The operation of proposed smart measuring devices and systems is based on the knowledge-based principles of processing and analyzing the measuring data for making managerial decisions and providing recommendations. Most of the obtained results have been put into mass production. [ABSTRACT FROM AUTHOR]

In today's world, merging sensor-based security systems with contemporary principles has become crucial. As we witness the ever-growing number of interconnected devices in the Internet of Things (IoT), it is imperative to have robust and trustworthy security measures in place. In this paper, we examine the idea of virtualizing the communication infrastructure for smart farming in the context of IoT. Our approach utilizes a metaverse-based framework that mimics natural processes such as mycelium network growth communication with a security-concept-based srtificial immune system (AIS) and transaction models of a multi-agent system (MAS). The mycelium, a bridge that transfers nutrients from one plant to another, is an underground network (IoT below ground) that can interconnect multiple plants. Our objective is to study and simulate the mycelium's behavior, which serves as an underground IoT, and we anticipate that the simulation results, supported by diverse aspects, can be a reference for future IoT network development. A proof of concept is presented, demonstrating the capabilities of such a virtualized network for dedicated sensor communication and easy reconfiguration for various needs. [ABSTRACT FROM AUTHOR]

Recent developments in networked and smart sensors have significantly changed the way Structural Health Monitoring (SHM) and asset management are being carried out. Since the sensor networks continuously provide real-time data from the structure being monitored, they constitute a more realistic image of the actual status of the structure where the maintenance or repair work can be scheduled based on real requirements. This review is aimed at providing a wealth of knowledge from the working principles of sensors commonly used in SHM, to artificial-intelligence-based digital twin systems used in SHM and proposes a new asset management framework. The way this paper is structured suits researchers and practicing experts both in the fields of sensors as well as in asset management equally. [ABSTRACT FROM AUTHOR]

One of the major challenge in Wireless Sensor Networks (WSN's) deployment is efficient energy consumption. Critical distance, proper routing algorithm and error control coding techniques can be used for energy optimization. As WSN contains a large number of power constrained sensors, the sensed data from the environment should be transmitted in a cooperative way to the base station (BS). The pattern of the clustering structure can extend the sensor network life time, reduce the total consumed energy and regulate the data transmission. Clustering concept combines group of sensors which are located in the same communication range. Some of the routing protocol like, SEED, LEACH, SEP, Z-SEP etc., suffers from idle listening problem, which cannot cope with an environment with sensor nodes. It leads to energy wastage across the network. To manage energy efficiency and traffic heterogeneity issues, a new routing protocol called enhanced energy efficient sleep awake aware intelligent sensor network (EEESAA) is proposed. Here, one sensor in each group will be in active mode whereas other sensors entered in sleep mode. Based on the nodes energy, sleep and awake node pairs will be altered. In the proposed method, one slot is allotted for group of pairs. The proposed approach is evaluated and compared against LEACH, SEP and Z-SEP protocols. Simulation results show that EEESAA protocol performs better than LEACH, SEP, Z-SEP in terms of cluster head selection, throughput, number of alive & dead nodes and network lifetime. [ABSTRACT FROM AUTHOR]

Driven by technological advances from Industry 4.0, Healthcare 4.0 synthesizes medical sensors, artificial intelligence (AI), big data, the Internet of things (IoT), machine learning, and augmented reality (AR) to transform the healthcare sector. Healthcare 4.0 creates a smart health network by connecting patients, medical devices, hospitals, clinics, medical suppliers, and other healthcare-related components. Body chemical sensor and biosensor networks (BSNs) provide the necessary platform for Healthcare 4.0 to collect various medical data from patients. BSN is the foundation of Healthcare 4.0 in raw data detection and information collecting. This paper proposes a BSN architecture with chemical sensors and biosensors to detect and communicate physiological measurements of human bodies. These measurement data help healthcare professionals to monitor patient vital signs and other medical conditions. The collected data facilitates disease diagnosis and injury detection at an early stage. Our work further formulates the problem of sensor deployment in BSNs as a mathematical model. This model includes parameter and constraint sets to describe patient body characteristics, BSN sensor features, as well as biomedical readout requirements. The proposed model's performance is evaluated by multiple sets of simulations on different parts of the human body. Simulations are designed to represent typical BSN applications in Healthcare 4.0. Simulation results demonstrate the impact of various biofactors and measurement time on sensor selections and readout performance. [ABSTRACT FROM AUTHOR]

Detection and monitoring of airborne hazards using e-noses has been lifesaving and prevented accidents in real-world scenarios. E-noses generate unique signature patterns for various volatile organic compounds (VOCs) and, by leveraging artificial intelligence, detect the presence of various VOCs, gases, and smokes onsite. Widespread monitoring of airborne hazards across many remote locations is possible by creating a network of gas sensors using Internet connectivity, which consumes significant power. Long-range (LoRa)-based wireless networks do not require Internet connectivity while operating independently. Therefore, we propose a networked intelligent gas sensor system (N-IGSS) which uses a LoRa low-power wide-area networking protocol for real-time airborne pollution hazard detection and monitoring. We developed a gas sensor node by using an array of seven cross-selective tin-oxide-based metal-oxide semiconductor (MOX) gas sensor elements interfaced with a low-power microcontroller and a LoRa module. Experimentally, we exposed the sensor node to six classes i.e., five VOCs plus ambient air and as released by burning samples of tobacco, paints, carpets, alcohol, and incense sticks. Using the proposed two-stage analysis space transformation approach, the captured dataset was first preprocessed using the standardized linear discriminant analysis (SLDA) method. Four different classifiers, namely AdaBoost, XGBoost, Random Forest (RF), and Multi-Layer Perceptron (MLP), were then trained and tested in the SLDA transformation space. The proposed N-IGSS achieved "all correct" identification of 30 unknown test samples with a low mean squared error (MSE) of 1.42 × 10−4 over a distance of 590 m. [ABSTRACT FROM AUTHOR]

A variety of low-cost sensors have recently appeared to measure air quality, making it feasible to face the challenge of monitoring the air of large urban conglomerates at high spatial resolution. However, these sensors require a careful calibration process to ensure the quality of the data they provide, which frequently involves expensive and time-consuming field data collection campaigns with high-end instruments. In this paper, we propose machine-learning-based approaches to generate calibration models for new Particulate Matter (PM) sensors, leveraging available field data and models from existing sensors to facilitate rapid incorporation of the candidate sensor into the network and ensure the quality of its data. In a series of experiments with two sets of well-known PM sensor manufacturers, we found that one of our approaches can produce calibration models for new candidate PM sensors with as few as four days of field data, but with a performance close to the best calibration model adjusted with field data from periods ten times longer. [ABSTRACT FROM AUTHOR]

This paper reports a novel monitoring technique of bearings' bidirectional load (axial and radial) based on a smart sensor coating, which is screen printed onto the surface of a cross-shaped steel substrate. To ensure the accuracy and stability of measurement as well as the durability of the printed coating, the developed prototype is built according to design rules commonly used in electronic circuits. The finite element model (FEM) is used to predict the mechanical property of the tested substrate under either unidirectional or bidirectional loads. Regarding the output voltage of the piezoelectric sensor, experimental results are revealed to be well-corelated to the numerical simulation. It is pointed out that the output signal generated from the sensor (electrode) could be particularly affected due to the capacitive parasite coming from the conductive tracks (CTs). Such a phenomenon might be reduced by printing them on the dielectric layer rather than on the piezocomposite layer. The study also investigates a highly anisotropic shape of electrodes (rectangular instead of circle), indicating that the orientation of such electrodes (axial or radial) does affect the output measurement. To sum up, the high performance of a sensor network coating depends not only on the ultimate characteristics of its own materials, but also on its structural design. Such an issue has been rarely reported on in the literature, but is nonetheless crucial to achieving reliable condition monitoring of bearings, especially for multidirectional loads—a key signature of early failure detection. [ABSTRACT FROM AUTHOR]

Health monitoring of critical structures, that form parts of serial operating objects, is a pressing task. The Operational Modal Analysis (OMA) techniques could be the optimal solution. An inexpensive measurement system, such as the OMA, uses a lot of sensors for structural response assessment. The health monitoring of serial structures has to also consider possible deviations between samples. A solution providing the OMA application includes the compact measurement system based on piezoelectric film sensors and modal passport (MP) techniques. For validation of the proposed approach, a series of five similar composite cylinders, with a network of piezoelectric film sensors, was used. Applying modal tests on the specimens, and using OMA with MP methods, the set of typical modal parameters was determined and analyzed. The results of the study confirmed the feasibility of the sensor network and its applicability for structural health monitoring of serial samples using OMA methods. The proven effectiveness of OMA/MP techniques, combined with a sensor network, provides a prototype of intelligent sensor technology, which can be used for health monitoring of structures, including those that are part of an operating facility. [ABSTRACT FROM AUTHOR]

The current autonomous driving vehicles cannot collaborate with each other due to the disconnection of sensors between vehicles, which leads to poor performance in traffic efficiency and safety. Recently, with the development of wireless intelligent sensors, vehicular sensor network (VSN) is emerging to provide information sharing between vehicles, so that cooperative autonomous driving (CAD) can be enabled. This paper especially investigates the VSN-assisted straight-going CAD at urban intersections under the rule of traffic signals. First, the function of VSN is presented, and the information sharing mechanism is designed. Then distributed stop-and-go strategies with maintaining the optimal safety spacing between adjacent vehicles are proposed for multiple traffic cases. With the proposed strategies, every vehicle can get an exact time to decelerate or accelerate at signalized intersections. Finally, we use the model predictive control (MPC) scheme to actuate our strategies, and it is demonstrated that vehicles perform well in both passage safety and efficiency as expected. [ABSTRACT FROM AUTHOR]

In this manuscript, we describe the soft- and hardware architecture as well as the implementation of a modern Internet of Medical Things (IoMT) system for sensor-assisted telepsychotherapy. It enables telepsychotherapy sessions in which the patient exercises therapy-relevant behaviors in their home environment under the remote supervision of the therapist. Wearable sensor information (electrocardiogram (ECG), movement sensors, and eye tracking) is streamed in real time to the therapist to deliver objective information about specific behavior-triggering situations and the stress level of the patients. We describe the IT infrastructure of the system which uses open standards such as WebRTC and OpenID Connect (OIDC). We also describe the system's security concept, its container-based deployment, and demonstrate performance analyses. The system is used in the ongoing study SSTeP-KiZ (smart sensor technology in telepsychotherapy for children and adolescents with obsessive-compulsive disorder) and shows sufficient technical performance. [ABSTRACT FROM AUTHOR]

Automated bioacoustics classification has received increasing attention from the research community in recent years due its cross-disciplinary nature and its diverse application. Applications in bioacoustics classification range from smart acoustic sensor networks that investigate the effects of acoustic vocalizations on species to context-aware edge devices that anticipate changes in their environment adapt their sensing and processing accordingly. The research described here is an in-depth survey of the current state of bioacoustics classification and monitoring. The survey examines bioacoustics classification alongside general acoustics to provide a representative picture of the research landscape. The survey reviewed 124 studies spanning eight years of research. The survey identifies the key application areas in bioacoustics research and the techniques used in audio transformation and feature extraction. The survey also examines the classification algorithms used in bioacoustics systems. Lastly, the survey examines current challenges, possible opportunities, and future directions in bioacoustics. [ABSTRACT FROM AUTHOR]

Objective. In order to control the vibration of the beam structure more effectively and improve the safety and availability of the beam structure, an application study of the Lagrange equation for vibration control of smart sensors for power grid monitoring is proposed. The vibration of the beam structure, the displacement of the beam structure under the excitation of seismic acceleration, the response analytical electrical formula, and the displacement response formula of the beam structure under the action of the kinematic force are deduced. The optimal parameters of the beam-TMDI system are given, and the parameter sensitivity analysis is carried out. Then, the control effect of the TMDI system is studied by numerical analysis, and the vibration reduction effect of the TMDI system and the tuned mass damper (TMD) system is compared. Experimental results show that when the mass ratio μ of the TMDI system and the TMD system are both set to a fixed value of 0.005, and the parameter β of the TMDI system is set to 0, namely β = b = 0 , at this time, the TMDI system degenerates into a TMD system. The TMD natural frequency is 14.179 rad/s and the damping ratio is 0.0432 by the DH optimization method, while the TMD natural frequency is 14.1812 rad/s, and the damping ratio is 0.0436 by the augmented Lagrangian optimization algorithm. Conclusion. The vibration displacement response spectrum of a beam structure obtained by the frequency domain method can effectively reflect electricity in the displacement response of a beam structure. The parameters that minimize the vibration response of the beam structure can be accurately obtained by using the augmented Lagrangian parameter optimization method. The sensitivity of the TMDI system is controlled by the inertial device, and the inertial device has a significant impact on its robustness. The vibration reduction performance of the TMDI system is obviously better than in the conventional TMD systems. [ABSTRACT FROM AUTHOR]

Judging from the current online teaching practice of higher physical education basic theory courses, there are many problems such as lagging theory, unclear models, confusing mechanisms, outdated methods, and lack of resources, resulting in the low interest of students. Basic theory courses are studied. The phenomenon of skipping class and being late is common, which seriously affects the improvement of the comprehensive quality of physical education students. Therefore, it has become an urgent problem for the majority of higher sports workers to develop learning resources for basic physical education courses and realize the innovation and application of the teaching mode of higher sports online courses. Compared with the traditional teaching method, the students' physical education teaching performance is improved by about 30%, and their personality is fully developed. Linking teacher performance to changes in student performance parameters can effectively improve teacher motivation. This shows that under the background of intelligent sensor network, the physical education network course can effectively improve the teaching efficiency. [ABSTRACT FROM AUTHOR]

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]

The Internet of things (IoT) provides an infrastructure to constructing smart cities. Through the installed IoT sensors in the city, a large amount of information of the city is detected and collected in a cloud center, which provides residents to have immediate and convenient services. To achieve the goal of smart cities, the fundamental challenge has been to minimize the energy consumption of the IoT sensor network, which is involved with the deployment and sleep scheduling of IoT sensors. Most of the previous related works proposed two-stage approaches to address deployment and sleep scheduling problems of IoT sensors separately. However, the deployment at the first stage influences the optimality of the sleep scheduling at the second stage significantly. Consequently, this work jointly considers deployment and sleep scheduling of IoT sensors in a smart city. To improve the performance of sleep scheduling of IoT sensors, the deployment at the first stage concurrently considers a part of the optimality of later sleep scheduling, and it is optimized by the proposed improved geometric selective harmony search algorithm that incorporates crossover and dynamic schemes. The crossover scheme can effectively solve the problem of different decision variables in the solution representation; and the dynamic scheme can increase stability and diversity of searching for solutions. The performance of the proposed algorithm is evaluated by the simulation under various combinations of covering requirements, the number of sensors, and practical parameter settings. [ABSTRACT FROM AUTHOR]

The intelligent recognition of human behavior and action in massive video data is the key application direction in the field of artificial intelligence. With the development of intelligent communication network, multimedia communication has become a hot spot in the field of video analysis. 3D convolution is an efficient deep learning model. It can learn the temporal and spatial features of target images at the same time. A 3D max residual feature map convolution network (3D-MRCNN) is proposed in this paper. Problems can be solved by the proposed model that the deficiencies of the network degradation and gradient disappearance caused by convolution calculation. The proposed model is preprocessed by 2D convolution firstly. A learning network including 3D-max feature map (3D-MFM) and residual structure is established after the convolution splitting is completed. Finally, the output vectors corresponding to the two different inputs are connected and fused into the support vector machine (SVM) classification. The accuracy of 3D-MRCNN can achieve 85.7% by experimenting on the representative UCF101 data set. And it has higher accuracy and operating efficiency compared with the models which have strong correlation with 3D-MRCNN. [ABSTRACT FROM AUTHOR]

Climate change has been linked to the occurrence of rainfall events with higher frequencies and intensities in various urban areas around the world. Existing stormwater systems in many areas are more frequently experiencing overloading beyond their design capacities, leading to more frequent flooding events. Recent technological advances surrounding the Internet of Things (IoT) have made it possible to retrofit existing stormwater infrastructure systems with cyber-physical technologies at lower costs. This paper presents the design and prototype implementation of an automated stormwater management system that uses inexpensive microcontroller units coupled with smart sensors and valves to minimize flooding events. The system design comprises a sensing layer to collect, control, and transfer real-time data; a gateway layer to receive, store, and process data; and an application layer to define interactions between system components. As such, the system aims to go beyond simple monitoring of stormwater levels and allow actions to be taken automatically. This paper demonstrates the testing of the system at a small lab scale experiment. It is anticipated that future versions of the proposed system will offer the potential to provide an automated and cost-effective solution to managing entire urban stormwater systems. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) paradigm represents a significant leap forward for sensor networks, potentially enabling wide-area and innovative measurement systems. In this scenario, smart sensors might be equipped with novel low-power and long range communication technologies to realize a so-called low-power wide-area network (LPWAN). One of the most popular representative cases is the LoRaWAN (Long Range WAN) network, where nodes are based on the widespread LoRa physical layer, generally optimized to minimize energy consumption, while guaranteeing long-range coverage and low-cost deployment. Additive manufacturing is a further pillar of the IIoT paradigm, and advanced measurement capabilities may be required to monitor significant parameters during the production of artifacts, as well as to evaluate environmental indicators in the deployment site. To this end, this study addresses some specific LoRa-based smart sensors embedded within artifacts during the early stage of the production phase, as well as their behavior once they have been deployed in the final location. An experimental evaluation was carried out considering two different LoRa end-nodes, namely, the Microchip RN2483 LoRa Mote and the Tinovi PM-IO-5-SM LoRaWAN IO Module. The final goal of this research was to assess the effectiveness of the LoRa-based sensor network design, both in terms of suitability for the aforementioned application and, specifically, in terms of energy consumption and long-range operation capabilities. Energy optimization, battery life prediction, and connectivity range evaluation are key aspects in this application context, since, once the sensors are embedded into artifacts, they will no longer be accessible. [ABSTRACT FROM AUTHOR]

Centrifugal pumps find extensive applications in various industries, being important to monitor their healthy operation. In this regard, the utilization of machine learning techniques for fault diagnosis, particularly in the domain of Intelligent Fault Diagnosis, assumes a pivotal role. This paper aims to contribute to this field by introducing an edge-based soft sensor approach for anomaly detection. The proposed strategy, grounded in machine learning principles, aims to diagnose the operational states of centrifugal pumps, distinguishing between healthy operation and various fault conditions. The study specifically investigates two types of faults: cavitation and dry running. The proposed Intelligent Fault Diagnosis only considers information available in industrial communication networks, which are usually provided at a high sampling rate, and underutilized by automation and supervisory systems, not requiring dedicated sensors. An experimental test bench was used and the centrifugal pump was operated under different conditions. Features were extracted, creating a dataset that represents system behavior. A Support Vector Machine-based model was applied for supervised data, and a One-Class Support Vector Machine-based model for unsupervised data, especially for users unable to operate the equipment under faulty conditions in the training phase. An easy feature selection strategy was employed to drastically reduce computational effort and improve the system performance, especially in the unsupervised data approach. The results achieve an accuracy of 100% for the supervised data approach and reach up to 96.4% for cavitation and 99.9% for dry running for the unsupervised data approach. [Display omitted] • A soft sensor methodology for Intelligent fault Detection for centrifugal pumps. • The use of supervised and unsupervised classification algorithms. • The proposal's performance is verified using a developed test bench. • An edge-based infrastructure for the proposed methodology. [ABSTRACT FROM AUTHOR]

A smart city is an intelligent space, in which large amounts of data are collected and analyzed using low-cost sensors and automatic algorithms. The application of artificial intelligence and Internet of Things (IoT) technologies in electronic health (E-health) can efficiently promote the development of sustainable and smart cities. The IoT sensors and intelligent algorithms enable the remote monitoring and analyzing of the healthcare data of patients, which reduces the medical and travel expenses in cities. Existing deep learning-based methods for healthcare sensor data classification have made great achievements. However, these methods take much time and storage space for model training and inference. They are difficult to be deployed in small devices to classify the physiological signal of patients in real time. To solve the above problems, this paper proposes a micro time series classification model called the micro neural network (MicroNN). The proposed model is micro enough to be deployed on tiny edge devices. MicroNN can be applied to long-term physiological signal monitoring based on edge computing devices. We conduct comprehensive experiments to evaluate the classification accuracy and computation complexity of MicroNN. Experiment results show that MicroNN performs better than the state-of-the-art methods. The accuracies on the two datasets (MIT-BIH-AR and INCART) are 98.4% and 98.1%, respectively. Finally, we present an application to show how MicroNN can improve the development of sustainable and smart cities. [ABSTRACT FROM AUTHOR]

Football is the most popular sports in the World, with an estimated global following of 4.0 billion fans worldwide. Football draws attention from people of various age groups. The result of the game only decides the performance of the team and individual players. The player has to train smarter to avoid a career-ending injury. Sports have also entered into the new era of artificial intelligence as any industry. Artificial intelligence (AI) in football acts like a teammate to the players and also plays the role of an assistant coach. The coach uses artificial intelligence and incorporates it into the traditional way of training. The Football Associations have already implemented sensors to collect data in the form of technologies such as Video Assistant Referee and Goal Line Technology. Additionally, the quality of the players and the coaches is improved with smart technological implementation. This technology itself incorporates the utilization of smart technologies for data acquisition using sensor networks and an intelligent data analysis. The proposed algorithm is compared with the fuzzy logic model (FLM) and found that it is 7.2% of higher risk predication by the proposed model than the existing. [ABSTRACT FROM AUTHOR]

Summary: Software‐defined networking (SDN) has emerged as an evolving technique in wireless sensor networks (WSNs). SDN enables WSNs with programmable control to manage network functions dynamically and efficiently. In Internet of Things (IoT) applications, smart sensors suffer from the low battery issue, generally deployed in harsh network environments where regular recharge is not feasible. Moreover, integrating SDN with IoT‐enabled sensor network puts forward several challenges, for example, control nodes' selection, load balancing, and energy cost optimization while aggregating the collected data, focusing on heterogeneous traffic data. Thus, an energy‐efficient data collection technique via definite sensing control in two‐level IoT‐enabled software‐defined heterogeneous WSN (2SD‐HWSN) is formulated as an optimization problem, with transmission distance from smart sensors, residual energy of sensors, and load based on node density. The proposed algorithm is divided into two: set‐up and transmission phases. In the set‐up phase, the control server (CS) elects the best‐suited control nodes (CNs) and sets up a schedule for coordinating data transmission. Further, normal nodes join appropriate CNs based on distance and residual energy. This way, CNs form clusters and route sensed data during the transmission phase. Therefore, an alternative nature‐inspired algorithm, that is, grey wolf optimization (GWO), is hybridized with particle swarm optimization using a low‐level co‐evolutionary technique to improve its overall performance. This hybrid variant of GWO, known as HGWO‐BC, offers balanced clustering (BC) via novel fitness function design. An exhaustive simulation study is performed in different scenarios considering homogeneous and heterogeneous sensors. Comparative results show that the HGWO‐BC outperforms state‐of‐the‐arts concerning network lifetime, instability period, residual energy, throughput, and computational efforts. [ABSTRACT FROM AUTHOR]

Intelligent transportation infrastructure has gained significant research attention recently. In this paper, an innovative sensor network of smart road stud (SRS) is developed to enhance traffic detection infrastructure characterized by its functionality in traffic data collection, long/short range wireless data transmission, self-sustained power supply, and remote custom controlled lighting-based traffic guidance. Compared to the traditional traffic detectors and road studs, SRS nodes are installed on lane lines instead of lane center to enable the additional applications besides the detection function, such as traffic monitoring, congestion warning, routing guidance, and so on. SRS detects vehicles based on three-axis geomagnetic sensors. A vehicle detection algorithm is proposed correspondingly under different operation scenarios to count vehicles in two adjacent lanes. Its detecting accuracy can be further improved by a sensor network of multiple SRSs working cooperatively. Field test results show that the vehicle detection accuracy based on the SRS network is about 98% per lane, which is the same level as the commercial detector installed in center of lane, even under the non-standard driving behaviors such as crossing lane line. The high performance, value-added service, and low cost enable wide-range applications of SRS networks as part of intelligent traffic detection infrastructure. [ABSTRACT FROM AUTHOR]

Federated Learning (FL) is a privacy-preserving way to utilize the sensitive data generated by smart sensors of user devices, where a central parameter server (PS) coordinates multiple user devices to train a global model. However, relying on centralized topology poses challenges when applying FL in a sensors network, including imbalanced communication congestion and possible single point of failure, especially on the PS. To alleviate these problems, we devise a Dynamic Average Consensus-based Federated Learning (DACFL) for implementing FL in a decentralized sensors network. Different from existing studies that replace the model aggregation roughly with neighbors' average, we first transform the FL model aggregation, which is the most intractable in a decentralized topology, into the dynamic average consensus problem by treating a local training procedure as a discrete-time series.We then employ the first-order dynamic average consensus (FODAC) to estimate the average model, which not only solves the model aggregation for DACFL but also ensures model consistency as much as possible. To improve the performance with non-i.i.d data, each user also takes the neighbors' average model as its next-round initialization, which prevents the possible local over-fitting. Besides, we also provide a basic theoretical analysis of DACFL on the premise of i.i.d data. The result validates the feasibility of DACFL in both time-invariant and time-varying topologies and declares that DACFL outperforms existing studies, including CDSGD and D-PSGD, in most cases. Take the result on Fashion-MNIST as a numerical example, with i.i.d data, our DACFL achieves 19∼34% and 3∼10% increases in average accuracy; with non-i.i.d data, our DACFL achieves 30∼50% and 0∼10% increases in average accuracy, compared to CDSGD and D-PSGD. [ABSTRACT FROM AUTHOR]

Earthquakes are one of the most frequent and inevitable natural disasters that occur on Earth. The most important seismic isolation device used in the isolation technology is the isolation bearing, but the limited structure of the isolation bearing is not suitable for the seismic isolation and shock absorption of smart transportation buildings. Therefore, it is very important to improve the new generation of seismic isolation structures with good seismic isolation effect, stable performance, and economical performance. It is very important to study the structural composition of the isolation bearing and the rigidity of the isolation structure. Isolation bearings are designed to resist and absorb the energy of seismic shocks by installing substructures in the structure. Active control involves installing sensors on the structure and its foundation to determine how the structure responds to seismic action. In this paper, based on the intelligent transportation of the sensor network, the reticulated shell structure of the isolation bearing is analyzed. By introducing the architecture and network layout of the sensor network, it is beneficial to obtain more accurate seismic data in complex and difficult terrain. This paper analyzes the technical principle of seismic isolation technology, which can effectively avoid the upward transmission of ground vibration by increasing the flexibility and proper damping of the system. From the experimental data of the seismic response of the ground-isolated structure to the near-field pulsation and far-field vibration, the total energy of the ground-isolated structure under the near-field pulsed ground motion is the largest. The seismic isolation effect of the reticulated shell structure of the seismic isolation bearing prevents more than 80% of the seismic energy from being transmitted to the superstructure. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a new strategy for vibration‐based structural health monitoring is proposed, specifically designed for smart sensors with edge computing capabilities organized in a line topology. This solution is aimed at maximizing resource optimization and enables the identification of modal parameters even for large or densely instrumented structures, where star‐topology monitoring systems are typically unsuitable. In particular, an efficient data management procedure is proposed to reduce data transmission, thus improving efficiency and minimizing maintenance interventions for battery replacement in wireless applications. The maximum volume of transmitted data can be selected by the user, based on the specific requirements of the network. Although the considerable reduction of data size, the proposed approach enables accurate estimation of the structural parameters in challenging scenarios where other techniques generally fail. Modal parameters are identified in an online fashion, enabling near real‐time detection and localization of early damage. Applications to a real case study instrumented with a dense sensor network show the effectiveness of the proposed approach and the possibility of localizing structural defects in slightly damaged civil structures. [ABSTRACT FROM AUTHOR]

Summary: Recently, even the road industry has been involved in an evolutionary process, inspired by the novel 'Industry 4.0'. This transition moves the attention towards big‐data and Internet of Things concepts, determining novel data flows and relevant management efforts. In smart roads, modern instrumented vehicles and networks of sensors will provide frequent helpful measures (traffic, weather, condition, accidents, mechanical performance, etc.), with reduced efforts and costs. However, this process is currently at the preliminary phases and several issues raise. Since the management and elaboration of huge amounts of data represent a relevant novel issue, in this study, an original web platform for collection and analysis of road performance data is proposed. This platform can acquire and process several data classes and support maintenance activity planning. This paper focuses on pavement maintenance, to provide a reliable decision support tool for road agencies, alternatively feedable by modern survey equipment and, in future, widespread sensors (when effective smart road sensors are installed on the main highways). The platform has been tested, in a preliminary form, on an existing motorway, considering high‐performance survey systems data, with interesting and positive results. [ABSTRACT FROM AUTHOR]

The advances and convergence in sensor, information processing, and communication technologies have shaped the Internet of Things of today. The rapid increase of data and service requirements brings new challenges for Internet of Thing. Emerging technologies and intelligent techniques can play a compelling role in prompting the development of intelligent architectures and services in Internet of Things to form the artificial intelligence Internet of Things. In this article, we give an introduction and review recent developments of artificial intelligence Internet of Things, the various artificial intelligence Internet of Things computational frameworks and highlight the challenges and opportunities for effective deployment of artificial intelligence Internet of Things technology to address complex problems for various applications. This article surveys the recent developments and discusses the convergence of artificial intelligence and Internet of Things from four aspects: (1) architectures, techniques, and hardware platforms for artificial intelligence Internet of Things; (2) sensors, devices, and energy approaches for artificial intelligence Internet of Things; (3) communication and networking for artificial intelligence Internet of Things; and (4) applications for artificial intelligence Internet of Things. The article also discusses the combination of smart sensors, edge computing, and software-defined networks as enabling technologies for the artificial intelligence Internet of Things. [ABSTRACT FROM AUTHOR]

Wireless sensor network is a multisensor wireless network system, which consists of multiple sensors and is configured independently. Because the network generates a large amount of data, the frequency, performance, and computing power of sensor nodes are limited, and they are particularly vulnerable to harsh environments and malicious attackers. This leads to the occurrence of malicious nodes, emergencies, and abnormal data in the sensor network system. Failures can also have a significant impact on sensor network services. The two main functions of wireless sensor network security are abnormal node detection and data anomaly detection. These two directions are mutually independent and complementary. Therefore, under the promotion of the rural revitalization strategy and the precision poverty alleviation strategy, China has increased its agricultural efforts. At this stage, all localities focus on the construction of rural financial systems to ensure that scattered farmers and rural small and micro-enterprises receive comprehensive financial services. The establishment of a rural financial system based on "intelligent forecasting" can improve financial development theories and build new ideas for rural financial development. And, the balance between realization and profitability, and then, through the use of Internet technology to make traditional financial institutions more effective in providing financial services, new online financial platforms can use them to make up for the existing shortcomings of traditional financial institutions as much as possible. In this article, through the research on the intelligent prediction of sensor data anomaly detection, it is applied to the development of rural finance and promotes the development of rural finance. [ABSTRACT FROM AUTHOR]

Driven largely by the recent growth in the Internet of Things, there is a rapid surge in the demand for low-powered or self-powered sensors and devices. Here, we report a fully self-powered magnetic sensor system based on the magnetoelectric (ME) effect. This device consists of both a field-detection element and a power-generation element, which are designed using magnetostrictive Metglas amorphous ribbons and piezoelectric PMN-PT single crystal plates, respectively. The ME laminates for energy harvesting exhibit a giant resonance magnetic responsivity of 350 nC/Oe at 15 kHz. The magnetic-field sensing element shows a linear response with a high resolution up to 32 nT, and the magnetic-harvest element can reach a power of 48.68 mW/Oe at an optimum load resistance of 5 kΩ. The self-powered sensor system has shown excellent capability to convert magnetic energy into electrical energy, as demonstrated in powering a small electronic screen. The high sensitivity and power generation of our system suggest potential applications in sustainable intelligent sensor networks. [ABSTRACT FROM AUTHOR]

Along with the ever-increasing attraction of the internet of things, self-sustainable smart sensor is receiving more and more attention. However, in some industrial sensing applications, such as the monitoring system of the transmission line in power grid, there are still a lot of critical technical issues that remain to be addressed. On the one hand, the power issue has long been an obstacle of the sensors in power grid because the routine maintenance in this case is almost impossible considering its high cost. On the other hand, highly integrated current sensing techniques with large linear range for measuring the transmission current in power line are still limited. To date, in the power grid, autonomous current sensor nodes with free-standing designs are in great demand. In this article, a complete solution for addressing these two issues has been proposed. First, a magnetic energy harvester that can scavenge the ambient power-frequency magnetic field around the transmission line is proposed. The proposed electrodynamic design is based on nonlinear resonance, which is demonstrated to have a higher power density. The power density of the prototype can be as high as 3.42 mW/cm3 (tested at merely 3 Oe applied magnetic field). Second, a power management circuit is specially designed for the energy harvester. The minimum input voltage of the circuit can be as low as 0.33 V. Finally, an anisotropic magnetoresistance (AMR) sensor head with large linear range is customized for sensing the current in power grid. The largest linear range of the AMR sensor can be as large as ±170 Oe. The specially designed sensing system can be a promising solution to realize the new generation of self-sustainable current sensor network in power grid. [ABSTRACT FROM AUTHOR]

This article focuses on intensifying in-vehicle biological wireless sensor networks for the persistence of monitoring the information on a precise vehicle. The wireless sensor networks will have enormous amount of nodules which are interrelated with each other. Therefore, these wireless sensor networks can be installed on a vehicle not only for monitoring perseverance but also for corroborating security with the support of a Global Positioning System expedient. In addition, the projected work focuses on reliable communiqué which is defined in terms of network reliability with discrepancy in reporting rate at each base station. To validate the efficiency of the proposed scheme, the simulation has been abetted using network simulator (NS2) and the outcomes indicate that when the sensors are installed, a robust system can be obtained with improved data transfer between the base stations. Moreover, a fortified in-vehicular sensor can be fixed in each vehicle with minimized path loss. [ABSTRACT FROM AUTHOR]

This article investigates the application of deep learning (DL) to the fusion of omnidirectional (O-D) infrared (IR) sensors and O-D visual sensors to improve the intelligent perception of autonomous robotic systems. Recent techniques primarily focus on O-D and conventional visual sensors for applications in localization, mapping, and tracking. The robotic vision systems have not sufficiently utilized the combination of O-D IR and O-D visual sensors, coupled with DL, for the extraction of vegetation material. We will be showing the contradiction between current approaches and our deep vegetation learning sensor fusion. This article introduces two architectures: 1) the application of two autoencoders feeding into a four-layer convolutional neural network (CNN) and 2) two deep CNN feature extractors feeding a deep CNN fusion network (DeepFuseNet) for the fusion of O-D IR and O-D visual sensors to better address the number of false detects inherent in indices-based spectral decomposition. We compare our DL results to our previous work with normalized difference vegetation index (NDVI) and IR region-based spectral fusion, and to traditional machine learning approaches. This work proves that the fusion of the O-D IR and O-D visual streams utilizing our DeepFuseNet DL approach outperforms both the previous NVDI fused with far-IR region segmentation and traditional machine learning approaches. Experimental results of our method validate a 92% reduction in false detects compared to traditional indices-based detection. This article contributes a novel method for the fusion of O-D visual and O-D IR sensors using two CNN feature extractors feeding into a deep CNN (DeepFuseNet). [ABSTRACT FROM AUTHOR]

In recent years, how to improve the performance of smart factories and reduce the cost of operation has been the focus of industry attention. This study proposes a new type of location-based service (LBS) to improve the accuracy of location information delivered by self-propelled robots. Traditional localization algorithms based on signal strength cannot produce accurate localization results because of the multipath effect. This study proposes a localization algorithm that combines the Kalman filter (KF) and the adaptive-network-based fuzzy inference system (ANFIS). Specifically, the KF was adopted to eliminate noise during the signal transmission process. Through the learning of the ANFIS, the environment parameter suitable for the target was generated, to overcome the deficiency of traditional localization algorithms that cannot obtain real signal strength. In this study, an experiment was conducted in a real environment to compare the proposed localization algorithm with other commonly used algorithms. The experimental results show that the proposed localization algorithm produces minimal errors and stable localization results. [ABSTRACT FROM AUTHOR]

The development of the Internet of Things (IoT) widened its definition to incorporate aquatic conditions. The submarine sensor structures and intelligent underwater linked devices have been built into the IoT environment as the Internet of Underwater Things. Energy-sensitive and accurate data collection is carried out using extremely secure communications in underwater sensor networks that face major drawbacks like timing and place-dependent connectivity. Therefore in this paper, Optimized energy planning based intelligent data analytics has been proposed to offer a programming system for distributing intelligent data analytics underwater with high energy efficiency. IDA implements two stages: the first stage is to overcome a drawback caused by secret and expose terminals by a possibility-based disputing method. The second stage investigates the possibilities for slight specificity recovery by adding a space focused on transmitter and receiver. OEP is used to capture data through an activity that uses intelligent data focused on self-learning to identify highly secure and effective route directions across communication gaps in a sensor network. By balancing data traffic loading in a vast network, the OEP transport system minimizes greater energy usage and delay issues. In a controversial approach, IDA resolves a limitation of confidentiality and reveals terminals based on choice. OEP collects data via smart self-learning information to track safety and productive paths through connectivity holes in a sensor network. The experimental findings illustrate the improved results have been built in terms of the high packet distribution rate of 97.11% and low latency, and less energy consumption. [ABSTRACT FROM AUTHOR]

Smart homes equipped with anonymous binary sensors offer a low-cost, unobtrusive solution that powers activity-aware applications, such as building automation, health monitoring, behavioral intervention, and home security. However, when multiple residents are living in a smart home, associating sensor events with the corresponding residents can pose a major challenge. Previous approaches to multi-resident tracking in smart homes rely on extra information, such as sensor layouts, floor plans, and annotated data, which may not be available or inconvenient to obtain in practice. To address those challenges in real-life deployment, we introduce the sMRT algorithm that simultaneously tracks the location of each resident and estimates the number of residents in the smart home, without relying on ground-truth annotated sensor data or other additional information. We evaluate the performance of our approach using two smart home datasets recorded in real-life settings and compare sMRT with two other methods that rely on sensor layout and ground truth-labeled sensor data. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a link is proposed for wireless body area sensor networks (WBASNs) for the transmission of electrocardiogram (ECG) signal. When the quantity of signal is very high, then it is beneficial that signal should be compressed before transmission. In that situation, the signal compression and error control schemes played an important role in the designing of link for WBASNs. In case of biological sensor network, it is very important that node consumed less power in transmission because the replacement of nodes is a very complex surgical procedure in many medical situations. A compressed sensing concept is introduced here. The main aspect of this paper is to present transmission strategy that provides fast and accurate transmission of ECG signals. In this the smart sensor nodes are utilized. These nodes having sensing, transformation and transmission capability. The nodes sense the ECG signals, and these signal are compressed by Walsh–Hadamard transform method. This compressed signal is transmitted to the module outside the human body. In this module, various error control coding and modulation operations are to be performed on signals, and after that, signals are transmitted to the final destination. The compressed signal occupied less storage space, and ECC provides high data rate. In that manner, the fast transmission of ECG data becomes possible on low data rate sensor network link. [ABSTRACT FROM AUTHOR]