Search

Real-time structural health monitoring (SHM) and accurate diagnosis of imminent damage are critical to ensure the structural safety of conventional reinforced concrete (RC) and fiber-reinforced concrete (FRC) structures. Implementations of a piezoelectric lead zirconate titanate (PZT) sensor network in the critical areas of structural members can identify the damage level. This study uses a recently developed PZT-enabled Electro-Mechanical Impedance (EMI)-based, real-time, wireless, and portable SHM and damage detection system in prismatic specimens subjected to flexural repeated loading plain concrete (PC) and FRC. Furthermore, this research examined the efficacy of the proposed SHM methodology for FRC cracking identification of the specimens at various loading levels with different sensor layouts. Additionally, damage quantification using values of statistical damage indices is included. For this reason, the well-known conventional static metric of the Root Mean Square Deviation (RMSD) and the Mean Absolute Percentage Deviation (MAPD) were used and compared. This paper addresses a reliable monitoring experimental methodology in FRC to diagnose damage and predict the forthcoming flexural failure at early damage stages, such as at the onset of cracking. Test results indicated that damage assessment is successfully achieved using RMSD and MAPD indices of a strategically placed network of PZT sensors. Furthermore, the Upper Control Limit (UCL) index was adopted as a threshold for further sifting the scalar damage indices. Additionally, the proposed PZT-enable SHM method for prompt damage level is first established, providing the relationship between the voltage frequency response of the 32 PZT sensors and the crack propagation of the FRC prisms due to the step-by-step increased imposed load. In conclusion, damage diagnosis through continuous monitoring of PZTs responses of FRC due to flexural loading is a quantitative, reliable, and promising application. [ABSTRACT FROM AUTHOR]

The proliferation of solar panel installations presents significant societal and environmental advantages. However, many panels are situated in remote or inaccessible locations, like rooftops or vast desert expanses. Moreover, monitoring individual panel performance in large-scale systems poses a logistical challenge. Addressing this issue necessitates an efficient surveillance system leveraging wide area networks. This paper introduces an Internet of Sensing Things (IoST)-based monitoring system integrated with sun-tracking capabilities for solar panels. Cutting-edge sensors and microcontrollers collect real-time data and securely store it in a cloud-based server infrastructure, enabling global accessibility and comprehensive analysis for future optimization. Innovative techniques are proposed to maximize power generation from sunlight radiation, achieved through continuous panel alignment with the sun's position throughout the day. A solar tracking mechanism, utilizing light-dependent sensors and servo motors, dynamically adjusts panel orientation based on the sun's angle of elevation and direction. This research contributes to the advancement of efficient and sustainable solar energy systems. Integrating state-of-the-art technologies ensures reliability and effectiveness, paving the way for enhanced performance and the widespread adoption of solar energy. Additionally, the paper explores anomaly prediction using Rayleigh distribution, offering insights into potential irregularities in solar panel performance. [ABSTRACT FROM AUTHOR]

The traditional Control Shed Poultry Farm (CSPF) systems are operated manually or in a semi-automatic culture. The existing CSPF system is still not free from human intervention and needs 24/7 monitoring. The existing approach is laborious, prone to caretakers bias and human error, and repeatability is difficult. The Intelligent CSPF system is based on the Internet of Things (IoT) and Machine Learning (ML). The proposed approach comprises the learning and mastering phase. In the learning phase, the system will observe or learn from user behaviour patterns regarding poultry activity operations for a specific time duration. Upon completion of the learning phase, the mastering phase will eventually automate the system and control the poultry farm’s environmental parameters, such as temperature, humidity, water level, light, and hazardous gases based on the historical data. The proposed system will replicate the operator’s past business control behavior. For this learning task, we have used Supervised ML techniques to analyze the performance of ML algorithms such as Random Forest (RF), Decision Tree (DT), K-nearest Neighbors (k-NN), Support Vector Machine (SVM), and Naïve Bayes (NB). DT is the successful ML classifier with the highest accuracy performed in the Intelligent CSPF System; this leads to the development of a smart CSPF culture where successful business experiences, i.e. models, will be exchanged among the community to get a collective benefit in terms of revenue.

Real-time structural health monitoring (SHM) and accurate diagnosis of imminent damage are critical to ensure the structural safety of conventional reinforced concrete (RC) and fiber-reinforced concrete (FRC) structures. Implementations of a piezoelectric lead zirconate titanate (PZT) sensor network in the critical areas of structural members can identify the damage level. This study uses a recently developed PZT-enabled Electro-Mechanical Impedance (EMI)-based, real-time, wireless, and portable SHM and damage detection system in prismatic specimens subjected to flexural repeated loading plain concrete (PC) and FRC. Furthermore, this research examined the efficacy of the proposed SHM methodology for FRC cracking identification of the specimens at various loading levels with different sensor layouts. Additionally, damage quantification using values of statistical damage indices is included. For this reason, the well-known conventional static metric of the Root Mean Square Deviation (RMSD) and the Mean Absolute Percentage Deviation (MAPD) were used and compared. This paper addresses a reliable monitoring experimental methodology in FRC to diagnose damage and predict the forthcoming flexural failure at early damage stages, such as at the onset of cracking. Test results indicated that damage assessment is successfully achieved using RMSD and MAPD indices of a strategically placed network of PZT sensors. Furthermore, the Upper Control Limit (UCL) index was adopted as a threshold for further sifting the scalar damage indices. Additionally, the proposed PZT-enable SHM method for prompt damage level is first established, providing the relationship between the voltage frequency response of the 32 PZT sensors and the crack propagation of the FRC prisms due to the step-by-step increased imposed load. In conclusion, damage diagnosis through continuous monitoring of PZTs responses of FRC due to flexural loading is a quantitative, reliable, and promising application.

The proliferation of solar panel installations presents significant societal and environmental advantages. However, many panels are situated in remote or inaccessible locations, like rooftops or vast desert expanses. Moreover, monitoring individual panel performance in large-scale systems poses a logistical challenge. Addressing this issue necessitates an efficient surveillance system leveraging wide area networks. This paper introduces an Internet of Sensing Things (IoST)-based monitoring system integrated with sun-tracking capabilities for solar panels. Cutting-edge sensors and microcontrollers collect real-time data and securely store it in a cloud-based server infrastructure, enabling global accessibility and comprehensive analysis for future optimization. Innovative techniques are proposed to maximize power generation from sunlight radiation, achieved through continuous panel alignment with the sun’s position throughout the day. A solar tracking mechanism, utilizing light-dependent sensors and servo motors, dynamically adjusts panel orientation based on the sun’s angle of elevation and direction. This research contributes to the advancement of efficient and sustainable solar energy systems. Integrating state-of-the-art technologies ensures reliability and effectiveness, paving the way for enhanced performance and the widespread adoption of solar energy. Additionally, the paper explores anomaly prediction using Rayleigh distribution, offering insights into potential irregularities in solar panel performance.

The massive Internet of Things (IoT) comprises different gateways (GW) covering a given region of a massive number of connected devices with sensors. In IoT networks, transmission interference is observed when different sensor devices (SD) try to send information to a single GW. This is mitigated by allotting various channels to adjoining GWs. Furthermore, SDs are permitted to associate with anyGWin a network, naturally choosing the one with a higher received signal strength indicator (RSSI), regardless of whether it is the ideal choice for network execution. Finding an appropriate GW to optimize the performance of IoT systems is a difficult task given the complicated conditions among GWs and SDs. Recently, in remote IoT networks, the utilization of machine learning (ML) strategies has arisen as a viable answer to determine the effect of various models in the system, and reinforcement learning (RL) is one of these ML techniques. Therefore, this paper proposes the use of an RL algorithm for GW determination and association in IoT networks. For this purpose, this study allows GWs and SDs with intelligence, through executing the multi-armed bandit (MAB) calculation, to investigate and determine the optimal GW with which to associate. In this paper, rigorous mathematical calculations are performed for this purpose and evaluate our proposed mechanism over randomly generated situations, which include different IoT network topologies. The evaluation results indicate that our intelligentMAB-based mechanism enhances the association as compared to state-of-the-art (RSSI-based) and related research approaches. [ABSTRACT FROM AUTHOR]

The present work originates from a previous study in which a low-cost Real-Time Locating System (RTLS) based on Ultra-Wideband signals was developed and tested both in a laboratory and in a real industrial environment for assessing its performance and determining the best configuration, according to some selected KPIs. Starting from the future research directions depicted, the evolution herein presented is twofold. First, tests performed in the laboratory are refined and deepened in terms of (i) different anchors' arrangements and orientation; (ii) the increased number of tested tags; and (iii) the tags' battery capacity test. Second, the development and deployment of the industrial solution as well is improved by means of a case for hosting tags to be positioned on the asset to be tracked, realized through 3D printing, in line with the industrial context requirements. Finally, an economic analysis is performed so as to demonstrate the convenience of the investment and the feasibility of the solution. Results are positive and promising in terms of both economic sustainability and implementation of the system in a real industrial environment and may constitute guidelines for practitioners and managers. [ABSTRACT FROM AUTHOR]

Within the scope of the Aveiro STEAM City project, an air quality monitoring network was installed in the city of Aveiro (Portugal), to evaluate the potential of sensors to characterize spatial and temporal patterns of air quality in the city. The network consists of nine sensors stations with air quality sensors (PM10, PM2.5, NO2, O3 and CO) and two meteorological stations, distributed within selected locations in the city of Aveiro. The analysis of the data was done for a one-year measurement period, from June 2020 to May 2021, using temporal profiles, statistical comparisons with reference stations and Air Quality Indexes (AQI). The analysis of sensors data indicated that air quality variability exists for all pollutants and stations. The majority of the study area is characterized by good air quality, but specific areas—associated with hotspot traffic zones—exhibit medium, poor and bad air quality more frequently. The daily patterns registered are significantly different between the affected and non-affected road traffic sites, mainly for PM and NO2 pollutants. The weekly profile, significative deltas are found between week and weekend: NO2 is reduced on the weekends at traffic sites, but PM10 is higher in specific areas during winter weekends, which is explained by residential combustion sources. [ABSTRACT FROM AUTHOR]

Human–robot interaction is becoming an integral part of practice. There is a greater emphasis on safety in workplaces where a robot may bump into a worker. In practice, there are solutions that control the robot based on the potential energy in a collision or a robot re-planning the straight-line trajectory. However, a sensor system must be designed to detect obstacles across the human–robot shared workspace. So far, there is no procedure that engineers can follow in practice to deploy sensors ideally. We come up with the idea of classifying the space as an importance index, which determines what part of the workspace sensors should sense to ensure ideal obstacle sensing. Then, the ideal camera positions can be automatically found according to this classified map. Based on the experiment, the coverage of the important volume by the calculated camera position in the workspace was found to be on average 37% greater compared to a camera placed intuitively by test subjects. Using two cameras at the workplace, the calculated positions were 27% more effective than the subjects' camera positions. Furthermore, for three cameras, the calculated positions were 13% better than the subjects' camera positions, with a total coverage of more than 99% of the classified map. [ABSTRACT FROM AUTHOR]

The present work originates from a previous study in which a low-cost Real-Time Locating System (RTLS) based on Ultra-Wideband signals was developed and tested both in a laboratory and in a real industrial environment for assessing its performance and determining the best configuration, according to some selected KPIs. Starting from the future research directions depicted, the evolution herein presented is twofold. First, tests performed in the laboratory are refined and deepened in terms of (i) different anchors’ arrangements and orientation; (ii) the increased number of tested tags; and (iii) the tags’ battery capacity test. Second, the development and deployment of the industrial solution as well is improved by means of a case for hosting tags to be positioned on the asset to be tracked, realized through 3D printing, in line with the industrial context requirements. Finally, an economic analysis is performed so as to demonstrate the convenience of the investment and the feasibility of the solution. Results are positive and promising in terms of both economic sustainability and implementation of the system in a real industrial environment and may constitute guidelines for practitioners and managers.

This work focuses on improving a camera system for sensing a workspace in which dynamic obstacles need to be detected. The currently available state-of-the-art solution (MoveIt!) processes data in a centralized manner from cameras that have to be registered before the system starts. Our solution enables distributed data processing and dynamic change in the number of sensors at runtime. The distributed camera data processing is implemented using a dedicated control unit on which the filtering is performed by comparing the real and expected depth images. Measurements of the processing speed of all sensor data into a global voxel map were compared between the centralized system (MoveIt!) and the new distributed system as part of a performance benchmark. The distributed system is more flexible in terms of sensitivity to a number of cameras, better framerate stability and the possibility of changing the camera number on the go. The effects of voxel grid size and camera resolution were also compared during the benchmark, where the distributed system showed better results. Finally, the overhead of data transmission in the network was discussed where the distributed system is considerably more efficient. The decentralized system proves to be faster by 38.7% with one camera and 71.5% with four cameras. [ABSTRACT FROM AUTHOR]

Distributed measurement often relies on sensor networks. In this paper, we present the construction of low-coherence fiber-optic Fabry–Pérot sensors connected into a quasi-distributed network. We discuss the mechanism of spectrum modulation in this type of sensor and the constraints of assembly of such sensors in the network. Particular attention was paid to separate the signals from individual sensors which can be achieved by cavity length-based addressing. We designed and built a laboratory model of a temperature sensors network. The employed sensors are low-coherence Fabry–Pérot interferometric sensors in a fiberoptics configuration. The extrinsic sensor cavity utilizes the thermal expansion of ceramics, and the sensors are addressed by the different lengths of the cavities. The obtained test results showthat the signal components from each sensor can be successfully separated, and the number of sensors could be expanded depending on the FWHM of the light source.

The urban environment is often responsible for negative impacts on the climate and environmental comfort, due to the common climatic interference in highly built and waterproofed environments. One of the ways of mitigating negative environmental and microclimate impacts is the use of green areas in the urban environment. Thus, aiming at the analysis of environmental quality parameters of urban regions, it is essential to monitor the influence of green areas on different meteorological variables. In this sense, the use of information acquisition instruments and methodologies can positively contribute to this analysis, which can support decision-making related to urban planning. Thus, this work aims to develop a sensor network with sensor nodes to assess meteorological variables in urban green areas, aiming to analyze and discuss the influence of green areas on the behavior of CO¬2 concentration, temperature and air relative humidity parameters in an urban environment. The proposed and developed sensor nodes enabled the adequate collection of these variables and, based on the results obtained, it was possible to observe that the green area (permanent preservation area - PPA) contributed positively to these variables in the portion of the study area located in Campinas (SP), presenting lower temperature and CO2 concentration and increased air humidity. However, for the portion of Paulínia (SP), it was possible to observe little influence from the PPA. Influences on these variables, exerted by rural areas and parks, were also identified, contributing to the reduction of meteorological parameters, but different interactions were identified with the CO2 concentration, which may present a possible increase or assist in the reduction of CO2 in the air. [ABSTRACT FROM AUTHOR]

Abstract With the advent of the era of network information, the amount of data in network information is getting larger and larger, and the classification of data becomes particularly important. Current semi-supervised multi-map classification methods cannot quickly and accurately perform automatic classification and calculation of information. Therefore, this paper proposes an MR-MGSSL algorithm and applies it to the classification of semi-supervised multi-graph. By determining the basic idea and calculation framework of MR-MGSSL algorithm, the mining of optimal feature subsets in multi-graphs and the multi-graph vectorization performance time are taken as examples, and the proposed algorithm is compared with other semi-supervised multi-graph classification methods. The performance evaluation results show that compared with other classification calculation methods, MR-MGSSL algorithm has the advantages of low sensitivity to feature subgraph and short vectorization time. The method is used to extract and detect clouds in remote sensing images (GF-1 and GF-2).

Within the scope of the Aveiro STEAM City project, an air quality monitoring network was installed in the city of Aveiro (Portugal), to evaluate the potential of sensors to characterize spatial and temporal patterns of air quality in the city. The network consists of nine sensors stations with air quality sensors (PM10, PM2.5, NO2, O3 and CO) and two meteorological stations, distributed within selected locations in the city of Aveiro. The analysis of the data was done for a one-year measurement period, from June 2020 to May 2021, using temporal profiles, statistical comparisons with reference stations and Air Quality Indexes (AQI). The analysis of sensors data indicated that air quality variability exists for all pollutants and stations. The majority of the study area is characterized by good air quality, but specific areas—associated with hotspot traffic zones—exhibit medium, poor and bad air quality more frequently. The daily patterns registered are significantly different between the affected and non-affected road traffic sites, mainly for PM and NO2 pollutants. The weekly profile, significative deltas are found between week and weekend: NO2 is reduced on the weekends at traffic sites, but PM10 is higher in specific areas during winter weekends, which is explained by residential combustion sources.

Human–robot interaction is becoming an integral part of practice. There is a greater emphasis on safety in workplaces where a robot may bump into a worker. In practice, there are solutions that control the robot based on the potential energy in a collision or a robot re-planning the straight-line trajectory. However, a sensor system must be designed to detect obstacles across the human–robot shared workspace. So far, there is no procedure that engineers can follow in practice to deploy sensors ideally. We come up with the idea of classifying the space as an importance index, which determines what part of the workspace sensors should sense to ensure ideal obstacle sensing. Then, the ideal camera positions can be automatically found according to this classified map. Based on the experiment, the coverage of the important volume by the calculated camera position in the workspace was found to be on average 37% greater compared to a camera placed intuitively by test subjects. Using two cameras at the workplace, the calculated positions were 27% more effective than the subjects’ camera positions. Furthermore, for three cameras, the calculated positions were 13% better than the subjects’ camera positions, with a total coverage of more than 99% of the classified map.

This work focuses on improving a camera system for sensing a workspace in which dynamic obstacles need to be detected. The currently available state-of-the-art solution (MoveIt!) processes data in a centralized manner from cameras that have to be registered before the system starts. Our solution enables distributed data processing and dynamic change in the number of sensors at runtime. The distributed camera data processing is implemented using a dedicated control unit on which the filtering is performed by comparing the real and expected depth images. Measurements of the processing speed of all sensor data into a global voxel map were compared between the centralized system (MoveIt!) and the new distributed system as part of a performance benchmark. The distributed system is more flexible in terms of sensitivity to a number of cameras, better framerate stability and the possibility of changing the camera number on the go. The effects of voxel grid size and camera resolution were also compared during the benchmark, where the distributed system showed better results. Finally, the overhead of data transmission in the network was discussed where the distributed system is considerably more efficient. The decentralized system proves to be faster by 38.7% with one camera and 71.5% with four cameras.

Distributed measurement often relies on sensor networks. In this paper, we present the construction of low-coherence fiber-optic Fabry-Pérot sensors connected into a quasi-distributed network. We discuss the mechanism of spectrum modulation in this type of sensor and the constraints of assembly of such sensors in the network. Particular attention was paid to separate the signals from individual sensors which can be achieved by cavity length-based addressing. We designed and built a laboratory model of a temperature sensors network. The employed sensors are low-coherence Fabry-Pérot interferometric sensors in a fiberoptics configuration. The extrinsic sensor cavity utilizes the thermal expansion of ceramics, and the sensors are addressed by the different lengths of the cavities. The obtained test results showthat the signal components from each sensor can be successfully separated, and the number of sensors could be expanded depending on the FWHM of the light source. [ABSTRACT FROM AUTHOR]

The article presents intelligent routing algorithms currently used in sensory networks, in terms of determining the possibility of their integration into systems working in potentially explosive atmospheres. Selected types of scribing algorithms were characterized. The analysis of simulation tests performed on selected types of scribing algorithms was carried out. The analysis of equipment solutions which can be used to build a network node operating in the conditions of methane and/or coal dust explosion hazard was carried out. [ABSTRACT FROM AUTHOR]

An original and attractive method of choosing sensors in a fixed network is proposed in order to identify online the density of heat flow from a mobile source. The system state (temperature) is described by a set of partial differential equations issued from an energy balance. Inverse heat conduction problems based on such valid mathematical model are ill posed and the choice of relevant sensors is crucial to enhance the identification algorithm. Strategies based on optimal sensors location are generally built on observations taken at fixed positions and considering a supposed nominal state. The proposed new approach is based on the iterative regularization of the minimization problem resulting from the inverse problem. Such an online regulation technique provides relevant information on the evolution of the sensitivity distributions and the best sensor locations can be updated. The main results are illustrated on a complex thermal situation in two-dimensional geometry and it is shown which sensors should be chosen over time when the identification of the unknown mobile heating flux is carried out online. [ABSTRACT FROM AUTHOR]

Wireless power transfer is considered as a novel solution for energy harvesting in wireless communication networks. In this paper, the system performance of the non-linear energy harvesting based power splitting relaying in the full-duplex relaying sensor network is investigated. We considered the system model network with one source, one destination, and one relay node in both the amplify-and-forward and decode-and-forward modes. The closed-form expressions of the system outage (OP) are analysed and derived for verifying system performance. Then, the correctness of the OP closed-form expression is verified by using the Monte Carlo simulation. Furthermore, the influence of the primary system parameters on the system OP is suggested and investigated. The research results indicated that the simulation curves and the analytical curves overlapped, verifying the correctness of the analytical expressions. [ABSTRACT FROM AUTHOR]

With the advent of the era of network information, the amount of data in network information is getting larger and larger, and the classification of data becomes particularly important. Current semi-supervised multi-map classification methods cannot quickly and accurately perform automatic classification and calculation of information. Therefore, this paper proposes an MR-MGSSL algorithm and applies it to the classification of semi-supervised multi-graph. By determining the basic idea and calculation framework of MR-MGSSL algorithm, the mining of optimal feature subsets in multi-graphs and the multi-graph vectorization performance time are taken as examples, and the proposed algorithm is compared with other semi-supervised multi-graph classification methods. The performance evaluation results show that compared with other classification calculation methods, MR-MGSSL algorithm has the advantages of low sensitivity to feature subgraph and short vectorization time. The method is used to extract and detect clouds in remote sensing images (GF-1 and GF-2). [ABSTRACT FROM AUTHOR]

In this article, we present data Compression mechanisms in an intelligent E-Health gateway for medical monitoring applications, to improve the collection and processing of data in the context of medical surveillance. These mechanisms, will not only optimize the data storage space required by the gateway, but also to accelerate the processing of the data flow for the fast and lossless transfer of data on the WSN (Wireless Sensor Network) and WLAN (Wireless Local Area Network) networks. In addition, all of the modern data compression mechanisms presented in this article are the following: Forecast compression, Sampling compression, Scheduling compression, distributed compression. We apply some formats of lossless data compression algorithms to these mechanisms (LZ4, LZO and Bzip2), to establish a comparative study between mechanisms using metric parameters. The implementation of these mechanisms in the smart gateway highlights an advanced Internet of Things data processing system for health and medical surveillance, based on a comprehensive and efficient study of advanced data compression techniques. [ABSTRACT FROM AUTHOR]

The paper presents the concept of installation of an electricity generator and en energy store inside a roller of belt or roller conveyor. Accordingly, the use of a generator-pulley does not require any structural changes to be made to an existing conveyor. In addition, the user will be able to power distributed sensors network for monitoring the operation of the belt conveyor anywhere on its route. [ABSTRACT FROM AUTHOR]

The multivariate Laplace distribution (MLD) possesses a lesser set of prior parameters compared to the Student's t-distribution, thus simplifying the modeling of measurement data subject to heavy-tailed noise corruption. This short communication introduces a novel distributed hybrid consensus fusion algorithm utilizing the MLD modeling and variational Bayesian approach. Firstly, we formulate the measurement noise associated with individual sensor nodes and introduce the hierarchical Gaussian form of MLD to enhance the feasibility of variational inference. Secondly, we opt for the inverse-Wishart (IW) distribution to serve as the conjugate prior to the positive definite matrix, enabling adaptive parameter learning within the MLD context. As a result, a novel hierarchical Gaussian state–space model (SSM) is established for multi-sensors network. Subsequently, we derive a novel local information filter to acquire higher precision information and innovation vector–matrix pairs, outperforming the Student's t distribution-based filter. These derivations are grounded in the new hierarchical SSM, and the vector–matrix pairs aid in information fusion across the network. Lastly, we present a distributed robust consensus filter for fusing local information using a hybrid consensus approach that devoid of a central fusion node. Simulation results demonstrate that the proposed consensus filter can obtain better estimation accuracy under the interference of heavy-tailed measurement outliers. • We assumes the measurement noise of each sensor follows a multivariate Laplace distribution. • We have proposed a localized information filter using the variational Bayesian methodology. • We have suggested a hybrid consensus filter designed to ensure robust distributed fusion. • Simulation results illustrate that the algorithm we have proposed improves estimation accuracy. [ABSTRACT FROM AUTHOR]

In recent years, there is an increasing attention on air quality derived services for the final users. A dense grid of measures is needed to implement services such as conditional routing, alerting on data values for personal usage, data heatmaps for Dashboards in control room for the operators, and for web and mobile applications for the city users. Therefore, the challenge consists of providing high density data and services starting from scattered data and regardless of the number of sensors and their position to a large number of users. To this aim, this paper is focused on providing an integrated solution addressing at the same time multiple aspects: To create and optimize algorithms for data interpolation (creating regular data from scattered), making it possible to cope with the scalability and providing support for on demand services to provide air quality data in any point of the city with dense data. To this end, the accuracy of different interpolation algorithms has been evaluated comparing the results with respect to real values. In addition, the trends of heatmaps interpolation errors have been exploited to detected devices’ dysfunctions. Such anomalies may often be useful to request a maintenance action. The solution proposed has been integrated as a Micro Services providing data analytics in a data flow real time process based on Node.JS Node-RED, called in the paper IoT Applications. The specific case presented in this paper refers to the data and the solution of Snap4City for Helsinki. Snap4City, which has been developed as a part of Select4Cities PCP of the European Commission, and it is presently used in a number of cities and areas in Europe.

Relay communication, in which the relay forwards the signal received by a source to a destination, has a massive consideration in research, due to its ability to expand the coverage, increase the capacity, and reduce the power consumption. In this paper, we proposed and investigated energy harvesting (EH) based two-way half-duplex (TWHD) relaying sensors network using selection combining (SC) over block Rayleigh fading channel. In this model, we proposed the direct link between two sources for improving the system performance. For the system performance analysis, we investigated and derived the closed-form of the exact and upper bound Ergodic capacity (EC) and the exact form of the symbol error ratio (SER). By using the Monte Carlo simulation, the correctness of the research results is verified in the influence of the main system parameters. From the discussions, we can see that the analytical and simulation agree well with each other.

Underground mining operations require robust and efficient communication like other industrial enterprises. Communication in the mining industry is the most critical factor to ensure the safety and security of the underground working environment. A robust IT communication infrastructure in underground mining will enhance safety and provide information in real-time which can result in a quick response to some fatal situation. Communication technologies that are capable of bearing the rough terrain and extreme environment inside a mine and between the surface and underground workstations is a critical factor for the monitoring of various underground mining operations remotely. Internet of Things (IoT) can play a massive role in the mining operations to improve the safety of the workers with enhanced productivity. Wireless communication, which is a part of IoT setup, is already playing a vital role in underground mine communication (UMC). Recently, IoT with its building blocks has been introduced to deploy and automate the operation of ‘self-advancing goaf edge support (SAGES)’ systems in an attempt to explore the possibility of the technology for real-time performance monitoring of support system including the behaviour of overlying roof strata in underground coal mines. The machine helps in supporting the roof of the mines during mining the coal. This paper presents a conceptual framework for implementation of IoT to make this underground support system smarter i.e. Smart-SAGES. The taxonomy of security challenges in UMC IoT combination has been described in detail in order to identify clear security goals. The blockchain based system seems to be a promising technology for the mining industry as it may help in curbing the penetration and disruption of cyber-attacks due to heterogeneous devices and distributed network. Various open issues in UMC have also been outlined. Vulnerabilities like information disclosure and Denial-of-Service attack (DoS attack) were discovered during the threat and risk analysis on the SAGES data logger. The SAGES falls into information generation block of IoT ecosystem. Therefore, it is advisable to incorporate the security by design approach from the initial phase of the development. Thus, the data transmission and storage can be made secure. [ABSTRACT FROM AUTHOR]

Tracking people even being partially or fully occluded in the group situation is studied using a Perception Sensor Network (PSN) system which is composed of multiple Kinects used to detect human 3D locations, and pan tilt zoom (PTZ) cameras used to identify human faces. A method is proposed to fuse multiple detection of human in the PSN system. After associating detected human with corresponding names, the novel grouping and ungrouping algorithms are proposed. When a group of multiple human staying close together is formed, viewpoint and illumination invariant features of group members including human 3D location, height, color and binary robust invariant scalable keypoint (BRISK), retrieved from region of interest (ROI) of both depth and color images, are then stored and updated into the group database. Based on the distance between a group location at previous frame and each member location in the group at current frame, the PSN system decides whether to keep the members in the group or to ungroup them then reassign the right name among the group database by minimizing multiple criterions. The experimental results demonstrate the outperforming of the proposed method on tracking people in group than conventional methods. [ABSTRACT FROM AUTHOR]

Large-scale ultrasonic positioning systems have recently shown substantial improvement, according to the current great interest concerning large-scale metrology applications in many different fields of manufacturing industry. However, the sound velocity is greatly influenced by the ambient temperature especially in large-scale applications. The traditional sound velocity compensation method mainly uses the average temperature in measurement space based on an assumption that the temperature field is uniform and stable. As the assumption is invalid in most cases, especially in industrial measurement environments, the traditional compensation method will bring obvious errors. To reduce these errors caused by sound velocity, this article proposes a novel compensation method by constructing a three-dimensional temperature field of measurement space through heat transfer theory as well as the finite element method, and then estimating the distance accurately using a couple iterating algorithm. Verification experiments demonstrate that the ranging deviation estimated through the proposed method may keep within 0.5 mm in 5.4×2 m measurement space, whereas the average distance measurement uncertainty is about 0.25 mm. [ABSTRACT FROM AUTHOR]

This paper presents an innovative electronically controlled suspension system installed on a real car and used as a test bench. The proposed setup relies on a sensor network that acquires a large real-time dataset collecting the car vibrations and the car trim and, through a new controller based on a recently proposed theory developed by the authors, makes use of adjustable semi-active magneto-rheological dampers. A BMW series 1 is equipped with such an integrated sensors-controller-actuators device and an extensive test campaign, in real driving conditions, is carried out to evaluate its performance. Thanks to its strategy, the new plant enhances, at once, both comfort and drivability of the car, as field experiments show. A benchmark analysis is performed, comparing the performance of the new control system with the ones of traditional semi-active suspensions, such as skyhook devices: the comparison shows very good results for the proposed solution.