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Various key pre‐distribution schemes (KPSs) for distributed sensor networks (DSNs) were obtained from different combinatorial designs. A block design D(v,b,r,k)$$ D\left(v,b,r,k\right) $$ may be mapped to a KPS where b$$ b $$ is the number of nodes or network size and k$$ k $$ is the number of keys (points) per node. A KPS is corresponding to a symmetric balanced incomplete block design has full connectivity where all nodes are connected to each other. But it is desirable that all nodes do not share common keys as the compromise of any node will destroy the whole network (see Lee & Stinson, 2005; Martin, 2009). To meet the situation, Lee and Stinson (2005) introduced μ—common intersection design which has disjoint blocks and these disjoint blocks can establish a secure communication through certain common intersecting blocks. Here we have proposed optimal KPSs from affine resolvable and partially affine resolvable balanced incomplete block designs, group divisible designs and L2—type designs. It is shown that these designs are important classes of μ—common intersection designs. The KPSs obtained here are optimal in the sense of local connectivity. Mathematics Subject Classification (2010): 94C30; 68R05; 05B05; 62K10. [ABSTRACT FROM AUTHOR]

There is a need to develop an unambiguous digital version of the International System of Units (SI), as required for information systems and distributed sensor networks. This leads to a reconsideration of the status of the non-SI units accepted for use with the SI. Here, the case of the non-SI units dalton (Da), neper (Np), bel (B) and decibel (dB) is considered. [ABSTRACT FROM AUTHOR]

A fundamental challenge in the practical implementation of multistatic radar systems (MSRS) is the requirement for precise time and frequency synchronisation between the spatially separated radar nodes. The authors evaluate the performance of different classes of commercially available Global Navigation Satellite Systems (GNSS) timing receivers, Local Oscillators (LO) and GNSS Disciplined Oscillators (GNSSDOs) to determine the limitations of using one‐way GNSS Time and Frequency Transfer (TFT) in this application. From evaluating the performance of three pairs of GNSSDOs, it is concluded that one‐way GNSS TFT will likely be suitable only for the synchronisation of fully spatially coherent MSRS with carrier frequencies up to 100 MHz and waveform bandwidths up to 20 MHz. Whereas, in the case of short‐term spatially coherent MSRS, synchronisation of systems with carrier frequencies up to a few GHz and waveform bandwidths of over 100 MHz will likely be possible. The performance of the different classes of GNSSDOs during GNSS denial (holdover) are evaluated, where it is concluded that frequency offsets between LOs at the point of GNSS denial will often significantly contribute, or even dominate, the holdover performance. Analysis of two practical multistatic radar measurements verifies the function of using the GNSSDOs for wireless synchronisation of the ARESTOR MSRS. [ABSTRACT FROM AUTHOR]

AUC (Area Under ROC curve) is a widely used metric for measuring classification performance. Recently, due to the efficiency and scalability of online learning in handling large-scale problems, it attracts lots of interests to maximize AUC in online setting. Traditional online learning algorithms require a computation node to process all the data sequentially. However, due to the explosion in volume and velocity of modern data generated from distributed sources such as sensor network and Internet, it is not efficient to send all the data to one node for processing and updating model sequentially. To address this challenge, we explore the distributed learning techniques for maximizing AUC in online setting with a linear model. Specifically, we propose two different algorithms for solving the distributed online AUC maximization problem: (i) the Centralized Distributed One-Pass Online AUC Maximization (C-DOPOAM) algorithm in which the server updates the global model with the gradients computed from workers; and (ii) the Decentralized Distributed One-Pass Online AUC Maximization (D-DOPOAM) algorithm in which each worker updates its local model and exchanges information with its neighbors to achieve consensus. Moreover, we establish the regret bounds of the two proposed algorithms. They achieve the similar convergence rate with the state-of-the-art non-distributed online AUC maximization algorithms. Finally, we conduct extensive experiments to validate our theoretical result on different computation platforms and network configurations. We also make a comparison between C-DOPOAM and D-DOPOAM in poor network condition. The results show D-DOPOAM is more robust than C-DOPOAM against low bandwidth or high latency. [ABSTRACT FROM AUTHOR]

This paper presents a detailed design of an inexpensive, simple, and scalable Distributed Sensor Network (DSN). Each sensor's hardware consists of a Raspberry Pi 3 Model B microprocessor, a specifically designed and factory-made Printed Circuit Board (PCB), an Uninterruptible Power Supply (UPS) Hat based on a High-Capacity Lithium Polymer battery (LiPo), a Power over Ethernet Splitter, a GPS receiver, and a LoRaWAN module. Each sensor is built to capture GPS, Wi-Fi, and Bluetooth signals and sends this information to a network controller implementing a LoRaWAN gateway. Each sensor's software is developed so that all applications run on top of a Linux operating system. The layer above it includes system daemon applications, such as Air-mon, HCI tools, GPSd, and networking support. An SQLite database sits on top of the daemon applications and records the captured information. After the DSN was successfully tested, it was deployed in a research study. The novelty of this study is that this was the first time that a DSN was used in high schools to detect leakage from IoT devices to educate students about cyber safety. [ABSTRACT FROM AUTHOR]

Wind-energy-harvesting generators based on inverted flag architecture are an attractive option to replace batteries in low-power wireless electronic devices and deploy-and-forget distributed sensors. This study examines two important aspects that have been overlooked in previous research: the interaction between an inverted flag and a neighboring solid boundary and the interaction among multiple contiguous inverted flags arranged in a vertical row. Systematic tests have been carried out with metal-only 'baseline' flags as well as a 'harvester' variant, i.e., the baseline metal flag covered with PVDF (polyvinylidene difluoride) piezoelectric polymer elements. In each case, dynamic response and power generation were measured and assessed. For baseline metal flags, the same qualitative trend is observed when the flag approaches an obstacle, whether this is a wall or another flag. As the gap distance reduces, the wind speed range at which flapping occurs gradually shrinks and shifts towards lower velocities. The increased damping introduced by attaching PVDF elements to the baseline metal flags led to a considerable narrowing of the flapping wind speed range, and the wall-to-flag or flag-to-flag interaction led to a power reduction of up to one order of magnitude compared to single flags. The present findings highlight the strong dependence of the power output on the flapping frequency, which decreases when the flag approaches a wall or other flags mounted onto the same pole. Minimum flag-to-flag and flag-to-wall spacing values are suggested for practical applications to avoid power reduction in multi-flag arrangements (2-3H and 1-2H respectively, where H is flag height). [ABSTRACT FROM AUTHOR]

Real-time multi-axis distributed tactile sensing is a critical capability if robots are to perform stable gripping and dexterous manipulation, as it provides crucial information about the sensor–object interface. In this paper, we present an optical-based six-axis tactile sensor designed in a fingertip shape for robotic dexterous manipulation. The distributed sensor can precisely estimate the local XYZ force and displacement at ten distinct locations and provide the global XYZ force and torque measurements. Its compact size, comparable to that of a human thumb, and minimal thickness allow seamless integration onto existing robotic fingers, eliminating the need for complex modifications to the gripper. The proposed sensor design uses a simple, low-cost fabrication method. Moreover, the optical transduction approach uses light angle and intensity sensing to infer force and displacement from deformations of the individual sensing units that form the overall sensor, providing distributed six-axis sensing. The local force precision at each sensing unit in the X, Y, and Z axes is 20.89 mN, 19.19 mN, and 43.22 mN, respectively, over a local force range of approximately ±1.5 N in X and Y and 0 to −2 N in Z. The local displacement precision in the X, Y, and Z axes is 56.70 μm, 50.18 μm, and 13.83 μm, respectively, over a local displacement range of ±2 mm in the XY directions and 0 to −1.5 mm in Z (i.e., compression). Additionally, the sensor can measure global torques, T x , T y , and T z , with a precision of of 1.90 N-mm, 1.54 N-mm, and 1.26 N-mm, respectively. The fabricated design is showcased by integrating it with an OnRobot RG2 gripper and illustrating real-time measurements during in simple demonstration task, which generated changing global forces and torques. [ABSTRACT FROM AUTHOR]

Microsupercapacitors (µSCs) have received a lot of interest for their possible use in miniaturized electronics and in the field of the Internet of Things (IoT) to power distributed sensors. µSCs fill the gap between batteries and traditional capacitors, providing high power densities and acceptable energy densities to fulfill onboard power supply requirements, coupled with quick charge/discharge rates and extended lifetime. Charge balancing of µSCs is not a standard practice, although the optimization of electrodes can provide beneficial effects on the electrochemical performance and stability of the device. In this work, a charge‐balanced double‐layer µSC based on laser‐induced graphene (LIG) with [PYR14][TFSI] as an ionic liquid electrolyte is presented for the first time. The optimized device shows an improvement in terms of the increased lifetime of a factor of four and its energy efficiency is raised above 80%. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to analyzing the robust stability of interconnected embedded systems. The paper starts by discussing the challenges associated with designing stable and robust embedded systems, particularly in the context of interconnected systems. The proposed approach combines the H∞ control theory with a new model for interconnected embedded systems, which takes into account the effects of communication delays and data losses. The paper provides a detailed mathematical analysis of the new model and presents several theorems and proofs related to its stability. The effectiveness of the proposed approach is demonstrated through several practical examples, including a networked control system and a distributed sensor network. The paper also discusses the limitations of the proposed approach and suggests several directions for future research. The proposed filter design method establishes a sufficient condition for the asymptotic stability of the error system and the satisfaction of a predefined H∞ performance index for time-invariant bounded uncertain parameters. This is achieved through the use of the strict linear matrix inequalities (LMI) approach and projection lemma. The design is formulated in terms of linear matrix inequalities (LMI). Numerical examples are provided to demonstrate the effectiveness of the proposed filter design methods. [ABSTRACT FROM AUTHOR]

Robots are now working outside of industrial fences more and more closely with humans. Safety is the primary requirement for intimate human–robot interactions. Contacts could happen at any and multipoints of robot. Tactile sensors have great potentials for contact sensing. However, their implementation for the whole-body compliance of robots upon unknown contacts is still challenging. In this work, a systematic solution is proposed. A dual-arm humanoid platform is constructed with distributed tactile sensors on its arms and body. Cheap and easy accessed resistive flexible tactile sensors are used. A data collection and signal processing system is developed for the sensing system with scalable capabilities. External contacts on the robot can be monitored by a visualized system. The multipoint contact force is calculated with the sensor positions taken into consideration. A PD controller-based compliant force control algorithm is proposed in the joint space of the robot. Particularly, a geometry-based force propagation method is introduced in order to achieve overall whole-body compliance of the robot. Experiments verified the whole-body compliance of the robot arms. Safety could then be maintained for frequent human–robot interactions. [ABSTRACT FROM AUTHOR]

The use of wearable solutions and sensors has reached the point of modern machine learning (ML) techniques in recent years. It contains the different programs that help to develop personalized opportunities for the betterment of the individuals. The research shed light upon the functions of ML and distributed sensor networks (DSN) in promoting healthcare among users. It can be said that the functions of the ML help to develop good fitness among the users, which results in good health. This dissertation consists of different kinds of ML algorithms and the usage of DSNs for the development of physical exercise programs. It has been found that most individuals use the sensors for their benefit in developing good health. The use of machine learning techniques not only helps to record an individual's health data but also sends emergency information to the nearest medical center for the user's benefit. The use of ML helps to the identification of the location of an individual with the help of GPS measures of real-time distance and users' health activities. [ABSTRACT FROM AUTHOR]

Neonatal infections are prevalent as newborn children are prone to a range of infections because of their absence of immunity. Being susceptible to various diseases, the immune system of infants is not adequately developed to fight against protozoa bacteria, viruses and parasites. Pathogenic pneumonia is one of the most common infections identified within the neonatal group. It is a lung infection occurring in the neonates, which can start after a few hours of delivery or even after a week. The infection can even occur due to the normal flora found in the genital tract of the mother, and the respiratory distress caused by pathogenic pneumonia can even lead to the infant's death. The study examined the clinical indicator for assessing the occurrence of pathogenic pneumonia in infants. With the help of sensor networking in data analytics, the prediction of such a disorder has been assessed in-depth in the article. [ABSTRACT FROM AUTHOR]

Recently, sentiment analysis has been a major business practice for an organization that helps analyze online reviews of different products. An organization can understand the customer's perception of their organizational products by analyzing reviews. On the other hand, this analysis process also helps to understand the customer's emotions about the organizational products. In this process, deep learning and distributed sensor networks play crucial roles in analyzing emotions. Five different steps of this analysis will provide accurate information about the customers' emotions on products. This analysis will help increase the product's value by understanding the customer's perception of where negative feedback improves the products. Through the help of this analysis, an organization will get several benefits that enrich its organizational image. On the other hand, this analysis process will face polarity issues, tone issues, comparative sentence analysis issues, and understanding idioms and emojis issues. The implementation of live API and proper sentiment analysis tools help to increase the effectiveness of this analysis. [ABSTRACT FROM AUTHOR]

Distributed fiber-optic sensing continues to gain widespread adoption in the energy industry because of the numerous benefits it offers for real-time surface and subsurface monitoring of pipelines, wellbores, reservoirs, and storage infrastructure. In this study, we introduce a novel workflow to analyze optical fiber-based distributed acoustic sensor (DAS) data, which takes into account the speed of sound for a certain phase to filter the acoustic energy or signal contributed by that phase. This information is then utilized for the characterization of multiphase flow. The application of the proposed velocity band energy (VBE) workflow is demonstrated using a dataset acquired in a 5163-ft-deep wellbore, for estimating gas void fraction and real-time gas–liquid interface tracking across the length of the well. The workflow utilizes a series of signal processing and conditioning steps that aim to reduce noise and enhance the signals of interest. The insights from the new methodology will further assist in validating DAS-based flow monitoring algorithms, leak detection and quantification, and reservoir characterization. [ABSTRACT FROM AUTHOR]

Communication distances of wireless sensor networks (WSNs) are greatly limited in settings where vegetation coverage is moderate or dense, and power consumption can be an issue in remote environmental settings. A newer innovative technology called "Low Power Wide Area Sensor Networks" (LPWAN) is capable of greater communication distances while consuming less power than traditional WSNs. This research evaluates the design and in-field performance of a LPWAN configuration in headwater catchments to measure environmental variables. The performance of the Beta LPWAN deployment indicate reduced signal strength in topographic valleys, but better actual than modelled data transmission performance. System performance during extreme cold temperatures (below – 15 ºC) resulted in increased sensor down time. The configuration of antennae combinations provides the greatest improvement in signal strength and system performance. This technology facilitates remote collection of physically-based, spatially-distributed information within regions with limited accessibility, ultimately advancing data collection capabilities into areas that are not feasible to visit regularly. [ABSTRACT FROM AUTHOR]

To develop implementation research on distributed optical fiber sensing technology, field tests were conducted on municipal roads and railways using a distributed acoustic sensor (DAS). Data were collected by the DAS during a field test for a long time period (more than 20 min), and we conducted short-term (<10 s) and long-term (≥10 s) analyses on these data separately. In the short-term data analysis, the vehicle type, vehicle length, and working status of the vehicle engine or the compressor were identified. In the long-term data analysis, the traffic flow was monitored, and the running distance, acceleration, speed, and braking distance of the vehicle were obtained. The characteristics of the vehicle operation data obtained in these field tests are important in developing the data processing method of DASs, which will help to promote the implementation of DASs. [ABSTRACT FROM AUTHOR]

This paper considers model-based fault detection of large-scale (possibly rank-deficient) dynamic systems. Assuming only global (and not local) observability over a sensor network, we introduce a single time-scale networked estimator/observer. Sensors take local outputs/measurements of system states with partial observability and share their information (including estimation and/or output) over a communication network, and gain distributed observability. We define the conditions on the network structure ensuring distributed observability and stabilising the error dynamics. However, system outputs are prone to faults and uncertainties, which affect the state estimation of all sensors as a consequence of communicating (possibly) faulty data. From the cyber-physical-systems (CPS) perspective, such faults add bias to the data transferred from the physical layer (dynamic system) to the cyber layer (sensor network). In this work, we propose a localised fault detection and isolation (FDI) mechanism at sensors to secure distributed estimation. This protocol enables every sensor to locally identify the possible fault at the sensor measurement, and, via local detection and isolation, to prevent the spread of biased/faulty information over the network. This distributed isolation and localisation of fault follows from our partial observability assumption instead of full observability at every sensor. Then, other sensors can estimate/track the system by using observationally-equivalent output information to recover for possible loss of observability. In particular, we study rank-deficient systems as they are known to demand more information-sharing, and thus, are more vulnerable to the spread of possible faults over the network. One challenge is the detection of faults in the presence of system/output noise without making (simplifying and unrealistic) upper-bound assumptions on the noise support. We resolve this by adopting probabilistic threshold designs on the residuals. Further, we show that additive faults at rank-deficiency-related outputs affect the residuals at all sensors, a consequence that mandates more constraints on the (distributed) FDI strategy. We address this problem by constrained LMI design of the feedback gain matrix. Finally, we design q-redundant distributed estimators, resilient to isolation/removal of up to q number of faulty sensors, and further, we consider thresholding residual history over a sliding time-window, known as the stateful FDI. [ABSTRACT FROM AUTHOR]

Thermoelectric power generation in the low temperature region has attracted considerable attention as a means of the effective use of distributed energy and for sensor applications. However, it is difficult to theoretically predict the exact thermoelectric transport properties owing to the relatively narrow bandgap of low temperature thermoelectric materials. In this study, a high-purity α-SrSi2 crystal was synthesized by the vertical Bridgman (VB) method. The carrier density of the VB-grown α-SrSi2 was investigated, and, from the temperature dependence of the carrier density, it was estimated that the bandgap was 13.1 meV. First-principles calculations using the Heyd–Scuseria–Ernzerhof screened hybrid functional for α-SrSi2 predicted the bandgap to be very close to this value (13.27 meV) when assuming the mixing parameter of the Hartree–Fock contribution to the exact exchange is 18.7%. Using the calculated electronic structure and the measured carrier concentration, the predicted temperature dependence of the Seebeck coefficient was in good agreement with the experimental results. [ABSTRACT FROM AUTHOR]

Piezoelectric energy harvesting from ambient vibrations offers an environmentally friendly approach to powering distributed sensors for the Internet of Things. This paper gives a direct comparison of Pb(Zr,Ti)O3 (PZT)- and (K,Na)NbO3 (KNN)-based harvesters using a compliant mechanism harvester design for resonant frequencies of 20, 40, and 70 Hz. At 70 Hz, the measured power densities for PZT- and KNN-based devices are 1139 and 31 μW/mm3, respectively, for unimorph structures on nickel foils of 25 and 50 μm in thickness. The power density ratios scale proportionally to the material energy harvesting figures of merit. Energy harvesting with the compliant mechanism design is twice as efficient when compared to harvesting with a simple cantilever beam. [ABSTRACT FROM AUTHOR]

Abstract Light spectrum analysis of leaves plays an essential role in measuring the contents of carbohydrates and other important compound like proteins. And it is also useful in evaluating the status of vegetation by remote sensing. Here, an approximation method considering the optical response characteristics of multispectral silicon (Si) sensor for the reflection or absorption spectra of leaves is proposed. The light spectrum is analysed by Gaussian mixture models (GMM) with fixed bandwidth firstly, and then the optimal model parameters are derived and validated to balance the complexity of Si sensor and the 2‐norm deviation of spectra, and next a low‐cost framework for the leaf spectroscopy with wireless node is illustrated according to the specific centre‐wavelengths and bandwidths of the GMM, and finally, an experimental prototype with 18‐channel Si sensor node and the developed mobile APP is demonstrated. The simplified strategy and its realization for leaf spectroscopy cast light on large‐scale applications in future agriculture.

Patras Gulf pockmark field (Western Greece) is a tectonically controlled field that has been activated at least twice by strong earthquakes (M5.4, 14 July 1993 and M6.4, 8 June 2008), and episodic gas seepages have been recorded in the past using geophysical means. A distributed temperature sensor (DTS) system was deployed inside a shallow pockmark and along an active fault at the northern end of the field. This ongoing experiment represents the first long-term monitoring ever conducted on gas-bearing pockmarks and active faults by the DTS system. For now, we have acquired and analyzed data regarding about 1.56 years. One of the primary objectives of this study is to establish methodological queues for data processing and analysis, including spectral analysis and incomplete data treatment techniques, to be standardized for use in further stages of the experiment. Spectral analysis was proven capable of separating the temperature footprint of background environmental components, such as sea-atmosphere heat flux, tides, and winds/waves, from high-frequency temperature residuals. Those residuals represent unusual events that might be correlated to seismicity. Monitoring the causal relationship between seismic activity and seabed water temperature changes in the field was thus attempted. No significant local earthquakes occurred during the monitoring period. Although the relation between seismicity and irregular seabed water temperature events was not systematic, we postulate that four thermal events have a causative link with the local seismicity. The DTS system constitutes a low-cost monitoring system, and the promising preliminary results of this experiment suggest that it is worth testing for a longer period. [ABSTRACT FROM AUTHOR]

In this paper, a method that estimates the real-time behavior of subsea line structures based on sequential data assimilation with distributed strain sensors is proposed. A finite element method is used to represent the behavior of subsea line structures and generates ensemble forecasts regarding unknown parameters. A merging particle filter technique is applied to integrate the observation data with the numerical models to calculate the posterior probability density function. The effectiveness of the proposed method is examined through twin experiments. The presented results validate the proposed method's capability to estimate the current state as well as unknown parameters of subsea line structures. The results suggest the advantage of distributed sensors against pointwise sensing when applied to line structures. [ABSTRACT FROM AUTHOR]

In this paper, a geometrically exact hybrid-mixed four-node laminated solid-shell element with piezoelectric patches with temperature-dependent material properties through the sampling surfaces (SaS) formulation is developed. The SaS formulation is based on the choice of SaS, parallel to the middle surface and located at Chebyshev polynomial nodes within the layers, to introduce the temperatures, displacements and electric potentials of these surfaces as basic shell unknowns. The outer surfaces and interfaces are also included into a set of SaS. Such a choice of unknowns with the use of Lagrange polynomials in the through-thickness approximations of temperatures, temperature gradient, displacements, strains, electric potential and electric field leads to a very compact higher-order thermopiezoelectric shell formulation. To implement efficient analytical integration throughout the solid-shell element, the extended assumed natural strain method is employed for all components of the temperature gradient, strain tensor and electric field vector. The developed hybrid-mixed four-node laminated solid-shell element with piezoelectric sensors and actuators is based on the extended Hu-Washizu variational principle and shows superior performance in the case of coarse meshes. This can be useful for the three-dimensional temperature-dependent response of laminated composite shells with piezoelectric patches, since the SaS formulation allows one to obtain the numerical solutions with a prescribed accuracy, which asymptotically approach the exact solutions of thermopiezoelectricity as a number of SaS tends to infinity. [ABSTRACT FROM AUTHOR]

The operating range of a distributed acoustic sensor based on a phase-sensitive optical time-domain reflectometer has been increased using an erbium-doped fiber amplifier with remote forward pumping. It is shown that by incorporating a single segment of erbium-doped fiber at a distance of 70 km and pumping it from the front end by a 500-mW laser at a wavelength of 1480 nm over the sensing fiber, it is possible to increase the operating range of the reflectometer by 45 km and, thereby, obtain the total operating range as large as 120 km along a standard single-mode fiber. [ABSTRACT FROM AUTHOR]

A brief review of the applications of distributed acoustic sensors for solving geophysical and seismometric problems is given. Theoretical estimates and experimental measurements of the level of self-noise in distributed acoustic sensors, including those at subhertz frequencies, are obtained. Numerical modeling has been carried out, which makes it possible to quantify the power of low-frequency noise (associated with a slow temperature change of the probed fiber) in the signal of distributed acoustic sensors. The obtained results are supplemented by theoretical estimates of the power spectrum of a signal from a distant earthquake; they demonstrate the importance of taking into account temperature effects in the fiber when planning experiments related to the registration of weak seismic events. [ABSTRACT FROM AUTHOR]

An experiment on seismic survey of the geological structure of the Black Sea seabed using a fiber-optic cable laid on the sea bottom (in shallow water) and a distributed acoustic sensor (DAS) is described. Based on the experimental results, it is shown that the proposed technology is promising. [ABSTRACT FROM AUTHOR]

A fiber phase-sensitive optical time-domain reflectometer (a distributed acoustic sensor) with a low-noise output signal in the frequency range of 0.01−1 Hz is proposed for seismology applications. The sensor architecture is based on an unbalanced Mach−Zehnder interferometer, which is used to form a dual-pulse probe signal with required phases of its components and to stabilize the frequency of the laser source in the feedback circuit. The low noise level of the output signal is achieved in the proposed circuit by compensating for the difference in the optical paths of the dual-pulse probe signal fields scattered by different fiber sections. The applicability of the proposed circuit has been experimentally demonstrated by detecting a remote earthquake using a fiber-optic cable located at the bottom of the Black Sea. [ABSTRACT FROM AUTHOR]

The range of applications for distributed fiber-optic sensors is constantly expanding due to both the growing needs of industry and the development of the measurement capabilities of the sensors themselves. In connection with the need to develop methods for interpreting sensor signals, it is extremely important to form sets of test signals for distributed fiber-optic sensors obtained under known conditions and effects on the fiber. In the presence of reliable analytical models of signals from distributed fiber-optic sensors, it is extremely convenient to obtain test signals in the course of numerical experiments. The paper will consider the processes of formation of backscattering signals in Rayleigh reflectometric systems and describe physical and mathematical models that allow calculations of signals under different operating conditions. Two approaches for calculating the resulting backscatter signal are proposed: (1) based on the temporal representation of the probing signal and the impulse response of the sensitive fiber and (2) an alternative approach based on the spectral representation of the probing signal and the transfer function of the fiber. The presented results can be used both for direct simulation of the operation of reflectometric systems using Rayleigh scattering and for the analysis of existing limitations and the specifics of their operation. [ABSTRACT FROM AUTHOR]

This paper analyses the spectral, spatial and temporal probing pulse characteristics of the fiber-optic distributed acoustic sensor prototype. The signal spectral distribution and mean value, the interference pattern contrast dependencies on the light source pulse duration and the optical amplifier pump current are obtained. Directions for further research in this area are proposed. The collected data made it possible to draw conclusions about such devices cost reduction possibilities and the proposed design operation suitability. [ABSTRACT FROM AUTHOR]

The sensitivity of the Sagnac interferometer for various acoustic influence coordinates was studied. The principles of the formation of a dead zone in an acoustic distributed fiber-optic sensor based on the Sagnac interferometer have been obtained and experimentally confirmed. The response of the interferometer was studied for different types of acoustic impact on the circuit: in the form of a rectangular pulse, sinusoidal, and in the form of a periodic triangular function. The nature of the change in the phase difference at the output of the Sagnac interferometer for each of them was studied. With the found value of the typical frequency = 10.8 kHz and a loop length of 20 km, numerical simulation and experimental study of the amplitude of the phase difference under acoustic impact through each 1 km of the loop in the range from 0 to 10 km were carried out. A dead zone elimination method is proposed for integrating the Sagnac interferometer into a complex monitoring system using a phase-sensitive optical time domain reflectometer. [ABSTRACT FROM AUTHOR]

An improved method for extracting the Brillouin frequency shift in postprocessing of a given Brillouin gain spectrum is presented. Modification of the method made it possible to expand the boundaries of its applicability to the region of noisy spectra with a signal-to-noise ratio (SNR) below 0 dB. The modified method can be successfully used in distributed fiber-optic sensors operating on the Brillouin scattering principle, especially in long-distance sensing lines. [ABSTRACT FROM AUTHOR]

The presented literature review was prepared by a team of authors united by the Program and Organizing Committees of the "Optical Reflectometry, Metrology, and Sensing" (ORMS) conference in 2023. It is intended to assess the state and prospects in this area for the coming years. The review covers the following topics: distributed acoustic sensors, fiber-optic measurement systems based on Brillouin scattering, research methods based on the principles of optical reflectometry in the frequency domain, and low-coherence approaches to distributed temperature and strain monitoring. [ABSTRACT FROM AUTHOR]

Plant phenotyping has been widely studied as an effective and powerful tool for analyzing crop status and growth. However, the traditional phenotyping (i.e., manual) is time-consuming and laborious, and the various types of growing structures and limited room for systems hinder phenotyping on a large and high-throughput scale. In this study, a low-cost high-throughput phenotyping system that can be flexibly applied to diverse structures of growing beds with reliable spatial–temporal continuities was developed. The phenotyping system was composed of a low-cost phenotype sensor network with an integrated Raspberry Pi board and camera module. With the distributed camera sensors, the system can provide crop imagery information over the entire growing bed in real time. Furthermore, the modularized image-processing architecture supports the investigation of several phenotypic indices. The feasibility of the system was evaluated for Batavia lettuce grown under different light periods in a container-type plant factory. For the growing lettuces under different light periods, crop characteristics such as fresh weight, leaf length, leaf width, and leaf number were manually measured and compared with the phenotypic indices from the system. From the results, the system showed varying phenotypic features of lettuce for the entire growing period. In addition, the varied growth curves according to the different positions and light conditions confirmed that the developed system has potential to achieve many plant phenotypic scenarios at low cost and with spatial versatility. As such, it serves as a valuable development tool for researchers and cultivators interested in phenotyping. [ABSTRACT FROM AUTHOR]

Ensuring the reliability and stability of long‐distance power transmission requires monitoring the transmission lines' and grid environment's health. For this purpose, this study proposes a triboelectric nanogenerator (TENG) with anti‐self‐excited vibration. In case of aeolian vibration in the transmission line, the anti‐self‐excited oscillation triboelectric nanogenerator (AO‐TENG) generates electricity via cohesive work between the spring and the additional weight block that drives the TENG. As a result, this prevents excessive surge voltage from damaging the back‐end circuit, and the capacitor contributes significantly to voltage drop. This solution solves the issue that arises due to the TENG's high output voltage, making it difficult to power commercial sensors. This study presents a new design solution to facilitate smart grids that guarantee stable operation of distributed sensor nodes in the grids. [ABSTRACT FROM AUTHOR]

IoT systems are becoming an essential part of our environment. Smart cities, smart manufacturing, augmented reality, and self-driving cars are just some examples of the wide range of domains, where the applicability of such systems have been increasing rapidly. These IoT use cases often require simultaneous access to geographically distributed arrays of sensors, heterogeneous remote, local as well as multi-cloud computational resources. This gives birth to the extended Cloud-to-Things computing paradigm. The emergence of this new paradigm raised the quintessential need to extend the orchestration requirements (i.e., the automated deployment and run-time management) of applications from the centralised cloud-only environment to the entire spectrum of resources in the Cloud-to-Things continuum. In order to cope with this requirement, in the last few years, there has been a lot of attention to the development of orchestration systems in both industry and academic environments. This paper is an attempt to gather the research conducted in the orchestration for the Cloud-to-Things continuum landscape and to propose a detailed taxonomy, which is then used to critically review the landscape of existing research work. We finally discuss the key challenges that require further attention and also present a conceptual framework based on the conducted analysis. [ABSTRACT FROM AUTHOR]

Barrier coverage is a fundamental application in wireless sensor networks, which are widely used for smart cities. In applications, the sensors form a barrier for the intruders and protect an area through intrusion detection. In this paper, we study a new branch of barrier coverage, namely warning barrier coverage (WBC). Different from the classic barrier coverage, WBC has the inverse protect direction, which moves the sensors surrounding a dangerous region and protects any unexpected visitors by warning them away from the dangers. WBC holds a promising prospect in many danger keep out applications for smart cities. For example, a WBC can enclose the debris area in the sea and alarm any approaching ships in order to avoid their damaging propellers. One special feature of WBC is that the target region is usually dangerous and its boundary is previously unknown. Hence, the scattered mobile nodes need to detect the boundary and form the barrier coverage themselves. It is challenging to form these distributed sensor nodes into a barrier because a node can sense only the local information and there is no global information of the unknown region or other nodes. To this end, in response to the newly proposed issue of the formation of barrier cover, we propose a novel solution AutoBar for mobile sensor nodes to automatically form a WBC for smart cities. Notably, this is the first work to trigger the coverage problem of the alarm barrier, wherein the regional information is not pre-known. To pursue the high coverage quality, we theoretically derive the optimal distribution pattern of sensor nodes using convex theory. Based on the analysis, we design a fully distributed algorithm that enables nodes to collaboratively move toward the optimal distribution pattern. In addition, AutoBar is able to reorganize the barrier even if any node is broken. To validate the feasibility of AutoBar, we develop the prototype of the specialized mobile node, which consists of two kinds of sensors: one for boundary detection and another for visitor detection. Based on the prototype, we conduct extensive real trace-driven simulations in various smart city scenarios. Performance results demonstrate that AutoBar outperforms the existing barrier coverage strategies in terms of coverage quality, formation duration, and communication overhead. [ABSTRACT FROM AUTHOR]

Distributed acoustic sensing (DAS) is a promising technology for seismic data acquisition, particularly in downhole applications. However, downhole DAS measurements can be affected by the deployment method of the fibre-optic cable. These effects were explored in a field trial in two wells (one vertical and one deviated) drilled at the Otway International Test Centre. The trial in the vertical well shows that (1) fibre-optic cables cemented behind the casing provide data of the highest quality due to the best coupling to the formation, and (2) tubing-conveyed cable shows only slightly weaker coupling, but the data quality can be severely degraded by source-generated noise. A cable loosely suspended in the deviated well provided data quality comparable to that of the cemented DAS cable. To better understand the nature of the observed effects, the field experiments were supplemented by numerical modelling with a 1.5D full wave reflectivity algorithm (3D wave propagation in a 1D model), where cement, casing and wellbore were represented by infinite vertical layers. The results show that (1) a cement layer has only a slight effect (<5%) on the DAS amplitude; (2) the vertical strain in a liquid-filled borehole is comparable to that in the formation; and (3) the strain amplitude in the cable is of the same order of magnitude both in the formation and in the fluid. The strain in the cable is zero both when the cable's Poisson's ratio is zero and when the borehole fluid is air. The results confirm the feasibility of borehole DAS measurements with fibre-optic cables suspended in a borehole liquid (but not gas!). [ABSTRACT FROM AUTHOR]

Aiming at the problem of distributed state estimation in sensor networks, a novel optimal distributed finite-time fusion filtering method based on dynamic communication weights has been developed. To tackle the fusion errors caused by incomplete node information in distributed sensor networks, the concept of limited iterations of global information aggregation was introduced, namely, fast finite-time convergence techniques. Firstly, a local filtering algorithm architecture was constructed to achieve fusion error convergence within a limited number of iterations. The maximum number of iterations was derived to be the diameter of the communication topology graph in the sensor network. Based on this, the matrix weight fusion was used to combine the local filtering results, thereby achieving optimal estimation in terms of minimum variance. Next, by introducing the generalized information quality (GIQ) calculation method and associating it with the local fusion result bias, the relative communication weights were obtained and embedded in the fusion algorithm. Finally, the effectiveness and feasibility of the proposed algorithm were validated through numerical simulations and experimental tests. [ABSTRACT FROM AUTHOR]

The autonomous traffic system has imposed higher requirements on the speed estimation of connected vehicles, where the speed of connected vehicles, as one of the control conditions for refined traffic management, plays a crucial role in the evaluation and optimization of network performance. In this paper, we propose a multi-source speed measurement sensor network consensus filtering (MSCF) algorithm based on information weight for the problem of optimal speed consistency estimation for connected vehicles. Specifically, we first utilize dynamic linearization techniques and data-driven parameter identification algorithms to handle the derived state equations of connected vehicles. We then establish observation models for four different types of sensors and construct distributed direct and indirect measurement models by dynamically adjusting the information weights of sensor nodes. Following this, we combine the Kalman consistency filtering algorithm to derive the speed state estimation update rate and design a new state estimator to achieve the optimal consistent convergence estimation for connected vehicles' speed. The MSCF algorithm can solve the problem of consistency filtering for noisy sensor data under observation- and communication-constrained conditions, enabling each sensor node to obtain a consistent convergence estimation value for the speed of the connected vehicle. The convergence of the algorithm is proved using the Lyapunov function. Through numerical simulation, the results are verified, indicating that compared to existing methods, this method can achieve a higher precision speed estimation effect. [ABSTRACT FROM AUTHOR]

In the last few years, fog computing has been recognized as a promising approach to support modern IoT applications based on microservices. The main characteristic of this application involve the presence of geographically distributed sensors or mobile end users acting as sources of data. Relying on a cloud computing approach may not represent the most suitable solution in these scenario due to the non-negligible latency between data sources and distant cloud data centers, which may represent an issue in cases involving real-time and latency-sensitive IoT applications. Placing certain tasks, such as preprocessing or data aggregation, in a layer of fog nodes close to sensors or end users may help to decrease the response time of IoT applications as well as the traffic towards the cloud data centers. However, the fog scenario is characterized by a much more complex and heterogeneous infrastructure compared to a cloud data center, where the computing nodes and the inter-node connecting are more homogeneous. As a consequence, the the problem of efficiently placing microservices over distributed fog nodes requires novel and efficient solutions. In this paper, we address this issue by proposing and comparing different heuristics for placing the application microservices over the nodes of a fog infrastructure. We test the performance of the proposed heuristics and their ability to minimize application response times and satisfy the Service Level Agreement across a wide set of operating conditions in order to understand which approach is performs the best depending on the IoT application scenario. [ABSTRACT FROM AUTHOR]

This work studies the application of low-cost noise reduction algorithms for the data processing of distributed acoustic sensors (DAS). It presents an improvement of the previously described methodology using the activation function of neurons, which enhances the speed of data processing and the quality of event identification, as well as reducing spatial distortions. The possibility of using a cheaper radiation source in DAS setups is demonstrated. Optimal algorithms' combinations are proposed for different types of the events recorded. The criterion for evaluating the effectiveness of algorithm performance was an increase in the signal-to-noise ratio (SNR). The finest effect achieved with a combination of algorithms provided an increase in SNR of 10.8 dB. The obtained results can significantly expand the application scope of DAS. [ABSTRACT FROM AUTHOR]

This paper presents the novel islanding detection technique that depends on the piezoelectric sensors for distributed generation sources (DGs) in the grid-integrated operation mode. The islanding detection method employs a piezoelectric sensor for the detection of the frequency variations during grid disruptions. The designed diaphragm-based sensor has a natural frequency as that of the fundamental frequency of the system frequency signal. Any further deviations from the natural frequency due to the disturbances will be sensed by the sensor and a control signal is being sent to the associated circuit breaker for suitable action. The sensor structure is modeled using the finite element method and implemented in the Phase-Locked Loop (PLL) of the grid-integrated DGs. The proposed circular diaphragm-based sensor structure will have the bandwidth in the range of natural frequency with the tolerance rate of ±1% and uses the input signal received from the PLL controller outer loop. The proposed islanding detection technique's performance and reliability for grid-integrated DG are validated using the real-time hardware in the loop test-bed platform. The HIL virtual simulated responses show that the method can be proposed effectively for islanding detection in the event of frequency variations in the grid-integrated DG systems. [ABSTRACT FROM AUTHOR]

This paper presents a fast approximation method for Multidimensional Scaling (MDS)-based dimensionality reduction on large cartography datasets. Since MDS preserves data point distances, it is useful in application domains where geolocation data are critical. Typical relevant tasks include smartphone user behavioral pattern extraction, animal motion tracking over long distances, or distributed sensor data monitoring. The input to MDS is a data distance matrix employed for reducing data point dimensionality under distance constraints. Similar procedures are crucial for analyzing and revealing the original hidden data structure, as well as for data visualization, feature extraction, or compression. For N data points, MDS has a computational complexity that exceeds O (N 2) which, may be excessive for a large N , e.g., for several hundred thousands or millions of data points. The proposed method allows fast approximate MDS calculation on million-point datasets in less than a minute on a simple laptop, by sampling a small subset of the original dataset, performing regular MDS on it and training a neural regressor to learn the desired MDS mapping. Quantitative and qualitative empirical evaluation of the proposed fast MLP-MDS algorithm on a geospatial data mapping task, i.e., on reducing 3D Earth surface points (longitude, latitude, radius) to 2D maps, has resulted in promising findings and small approximation errors. The benefits are even greater in incremental settings, where new data points are obtained and projected over time. Unlike regular MDS or competing approximations, this is trivially supported in MLP-MDS due to the latter's model-based nature. [ABSTRACT FROM AUTHOR]

Defect recognition in ballastless track structures, based on distributed acoustic sensors (DASs), was researched in order to improve detection efficiency and ensure the safe operation of trains on high-speed railways. A line in southern China was selected, and equipment was installed and debugged to collect the signals of trains and events along it. Track vibration signals were extracted by identifying a train track, denoising, framing and labeling to build a defect dataset. Time–frequency-domain statistical features, wavelet packet energy spectra and the MFCCs of vibration signals were extracted to form a multi-dimensional vector. An XGBoost model was trained and its accuracy reached 89.34%. A time-domain residual network (ResNet) that would expand the receptive field and test the accuracies obtained from convolution kernels of different sizes was proposed, and its accuracy reached 94.82%. In conclusion, both methods showed a good performance with the built dataset. Additionally, the ResNet delivered more effective detection of DAS signals compared to conventional feature engineering methods. [ABSTRACT FROM AUTHOR]

Energy harvesting systems are key elements for the widespread deployment of wireless sensor nodes. Although many energy harvesting systems exist, electric field energy harvesting is a promising choice because it can provide uninterrupted power regardless of external conditions and depends only on the presence of AC voltage in the grid, regardless of the magnitude of the line current, even under no-load conditions. However, it also has some disadvantages, such as low power availability, the need for storage, or reliance on capacitive coupling, which is a complex phenomenon that depends on parasitic capacitances. This paper aims to provide useful and practical information on the possibilities of electric field energy harvesting for both high- and low-voltage applications. Since the objective of this paper is to quantify the physical limit of the harvested energy, it considers only the physical harvester itself and not the electronic circuitry required to transfer the harvested energy to the load. Theoretical, simulation, and experimental results show the feasibility of this energy source for low-power applications such as wireless sensor nodes. [ABSTRACT FROM AUTHOR]

With the development of optical frequency-domain reflectometry (OFDR)-based distributed strain sensing technology and static influence line (IL) measurement concept, the special influence surface (IS) measurement, a function of sensing and force locations, has become technically feasible. This study investigated, for the first time, damage detection in long-span bridges using the special IS constructed on the basis of the OFDR-based distributed sensing. The damage detection performance of IS was validated through numerical and experimental case studies on a scaled physical Tsing Ma bridge (TMB) model. First, the special IS concept was elaborated. The scaled TMB model was then introduced as a representative long-span bridge. Subsequently, the IS characteristics of different bridge components of the scaled TMB were analyzed using the finite element model (FEM). Various IS-based indices, including strain IS (SIS) and displacement IS (DIS), were applied to hypothetical damage scenarios where the bottom and top chords were subjected to severe damage in the FEM. The extracted ISs successfully realized damage localization with relatively great sensitivities. For comparison, the modal parameters were also output for damage detection. In the experimental section, an OFDR-based distributed sensor was placed along the top chord of one middle grid in the scaled TMB model to construct the SIS. The SIS changes could accurately localize the single, double, and triple bottom-chord damages. The numerical and experimental results demonstrated the feasibility and effectiveness of the OFDR-constructed special SIS for damage detection, indicating that IS is a promising damage indicator for long-span bridges, promoting the development of distributed sensing techniques in structural health monitoring for real-bridge applications. [ABSTRACT FROM AUTHOR]

Monitoring the seepage field is essential to characterize the operation of the earth dam. However, conventional seepage monitoring typically involves the use of a piezometer tube or pressure sensor for point distribution. A mathematical model incorporating the feedback of the temperature field on the seepage field was proposed based on experimental test observations. First, a theoretical analysis of the mathematical model is conducted, which includes examining the relationship between the variation of temperature field and factors, such as hydraulic gradient, water and soil temperature difference, hydraulic conductivity, thermal conductivity, specific heat, and time. Second, a homogeneous earth dam model is constructed in the laboratory with a distributed temperature sensor system to obtain synchronous information of the seepage–temperature field. Finally, the parameters of the mathematical model are quantified using observations from experimental tests, demonstrating good agreement between calculated and measured values. The experimental results indicate the feasibility of inferring seepage field monitoring information from the temperature field and elucidation of the interaction mechanism of the coupled fields. The quantitative mathematical model of the temperature field feedback on the seepage field is validated for its applicability in earth dams. [ABSTRACT FROM AUTHOR]

In multichannel signal processing with distributed sensors, choosing the optimal subset of observed sensor signals to be exploited is crucial in order to maximize algorithmic performance and reduce computational load, ideally both at the same time. In the acoustic domain, signal cross-correlation is a natural choice to quantify the usefulness of microphone signals, i.e., microphone utility, for coherent array processing, but its estimation requires that the uncoded signals are synchronized and transmitted between nodes. In resource-constrained environments like acoustic sensor networks, low data transmission rates often make transmission of all observed signals to the centralized location infeasible, thus discouraging direct estimation of signal cross-correlation. Instead, we employ characteristic features of the recorded signals to estimate the usefulness of individual microphone signals using the Magnitude-Squared Coherence (MSC) between the source and respective microphone signal as ground-truth metric. In this contribution, we provide a comprehensive analysis of model-based microphone utility estimation approaches that use signal features and, as an alternative, also propose machine learning-based estimation methods that identify optimal sensor signal utility features. The performance of both approaches is validated experimentally using both simulated and recorded acoustic data, comprising a variety of realistic and practically relevant acoustic scenarios including moving and static sources. [ABSTRACT FROM AUTHOR]