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In this paper, the design and the manufacture of an inkjet printed resistive type humidity sensor on paper are reported. After having been exposed to humidity above a given threshold level, the resistance of the sensor decreases substantially and remains at that level even when the humidity is reduced. It is possible to deduce the humidity level by monitoring the resistance. The main benefit of the printed sensor presented in this case is in relation to its very low production costs. It has also been shown that both the ink type and this paper combination used prove to be crucial in order to obtain the desired sensor effect. More research is required in order to fully understand the humidity sintering effect on the nano particle ink and the role of the substrate. However, the observed effect can be put to use in printed humidity sensors which possess a memory function. The sensor can be used in various applications for environmental monitoring, for example, in situations where a large number of inexpensive and disposable humidity sensors are required which are able to detect whether they have been subjected to high humidity. This could be the checking of transportation conditions of goods or monitoring humidity within buildings. [ABSTRACT FROM PUBLISHER]

Nowadays, the monitoring of plant water stress is of high importance in smart agriculture. Instead of the traditional ground soil-moisture measurement, the leaf sensing is a new technology, which is used for the detection of plants needing water. In this paper, a novel, low-cost, and low-power system for leaf sensing using a new plant backscatter sensor node/tag is presented. The latter, can result in the prevention of water waste (water-use efficiency), when is connected to an irrigation system. Specifically, the sensor measures the temperature differential between the leaf and the air, which is directly related to the plant water stress. Next, the tag collects the information from the leaf sensor through an analog-to-digital converter, and then, communicates remotely with a low-cost software-defined radio reader using monostatic backscatter architecture. The tag consists of the sensor board, a microcontroller, an external timer, and an RF front-end for communication. The timer produces a subcarrier frequency for simultaneous access of multiple tags. The proposed work could be scaled and be a part of a large backscatter wireless sensor network. The communication protocol exploits the low-complexity Morse code modulation on a 868 MHz carrier signal. The presented novel proof-of-consent prototype is batteryless and was powered by a flexible solar panel consuming power around $20~\mu \text{W}$. The performance was validated in an indoors environment where wireless communication was successfully achieved up to 2 m distance. [ABSTRACT FROM AUTHOR]

This paper concerns the deployment problem of wireless sensor networks (WSNs) with mobile robotic sensor nodes for spatiotemporal monitoring. The proposed approach, deep reinforced learning tree (DRLT), utilizes deep reinforcement learning (DRL) to improve the efficiency of searching the most informative sampling locations. The parameterized sampling locations in an infinite horizon space are modeled according to their spatiotemporal correlations and subject to various constraints, including field estimation error and information gain. And the model-based information gain can be calculated efficiently over an infinite horizon. In this manner, the effectiveness of the sampling locations is learned through DRLT during the exploration by the robotic sensors. Then DRLT can instruct the robotic sensors to avoid unnecessary sampling locations in future iterations. Also, it is proved in this paper that the proposed algorithm is capable of searching for the near-optimal sampling locations effectively and guaranteeing a minimum field estimation error. Simulation based on national oceanic and atmospheric administration (NOAA) datasets is presented, which demonstrates the significant enhancements made by the proposed algorithm. Compared with the traditional approaches, such as the information theory-based greedy approach or random sampling, the proposed method shows a superior performance with regard to both estimation error and planning efficiency. [ABSTRACT FROM AUTHOR]

Environmental pollution is a major issue for public health and safety. Measurement of fine-grain spatiotemporal distribution of pollution for a micro-environment is necessary to explore the correlations among environment, health, and behavior. The continuous advancement of miniature environmental sensing devices, microelectronics, and communication technologies propelled the development of wearable devices for environmental monitoring applications. This type of devices are able to acquire high-resolution objective geo-tagged data. The aim of this review is to summarize the contemporary research and developments on wearable devices intended for environmental Internet of Things (IoT) applications. This paper provides comprehensive descriptions of the state-of-the-art wearable environmental monitoring systems (WEMS) and compares them in terms of sensing and communication technologies. The reviewed WEMS are mainly classified into two groups based on whether they used commercial off-the-shelf (COTS) sensors or self-developed sensors. The first group is further categorized based on their wearability. In line with this, a consolidated description is provided by presenting the main features of the available wearable environmental monitoring devices from the industry. In addition, several issues are identified for WEMS and future research directions are provided. Thus, this review paper examines the current research on WEMS and can serve as a reference for the researchers and developers to provide perspectives toward future research improvements. [ABSTRACT FROM AUTHOR]

Processing large very high-resolution remote sensing images on resource-constrained devices is a challenging task because of the large size of these data sets. For applications such as environmental monitoring or natural resources management, complex algorithms have to be used to extract information from the images. The memory required to store the images and the data structures of such algorithms may be very high (hundreds of gigabytes) and therefore leads to unfeasibility on commonly available computers. Segmentation algorithms constitute an essential step for the extraction of objects of interest in a scene and will be the topic of the investigation in this paper. The objective of the present work is to adapt image segmentation algorithms for large amounts of data. To overcome the memory issue, large images are usually divided into smaller image tiles, which are processed independently. Region-merging algorithms do not cope well with image tiling since artifacts are present on the tile edges in the final result due to the incoherencies of the regions across the tiles. In this paper, we propose a scalable tile-based framework for region-merging algorithms to segment large images, while ensuring identical results, with respect to processing the whole image at once. We introduce the original concept of the stability margin for a tile. It allows ensuring identical results to those obtained if the whole image had been segmented without tiling. Finally, we discuss the benefits of this framework and demonstrate the scalability of this approach by applying it to real large images. [ABSTRACT FROM PUBLISHER]

With the rapid development of modern industries, more and more risk factors exist in industrial operations, leading to a gradual increase in the frequency of industrial failures. The industrial enterprises have taken a variety of measures to analyze the risk factors in industrial operations, but establishing an effective monitoring and evaluation system remains a technical challenge. To overcome this challenge, a practical industrial risk monitoring system, based on the wireless sensor network (WSN), is established in this paper to improve resilience of the respective industrial operations. In the proposed system, the existing sensor nodes in the industrial environment are organized into WSN to transmit real-time data. A special inspection robot is used to monitor comprehensively the environmental and safety risk factors in the industrial environment. To obtain the location of the robot, the partial nodes of the WSN are organized dynamically as the anchor nodes of the localization system, and two grid cumulative probability localization (GCPL) algorithms are proposed based on the GCPL-circle intersection and GCPL-path loss methods, respectively. The GCPL algorithm determines the grid cumulative probability using prior location information of the target node and the received signal strength information between the anchor nodes and target node to locate the target node. Experimental results show that the two GCPL algorithms significantly improve the localization accuracy and stability, and hence can be implemented as a risk monitoring system for real-world applications. Note to Practitioners—In industrial operations, a lot of risk factors exist which result in many frequent operational disruptions and failures. It is hence critical to develop an effective risk monitoring and evaluation system. Motivated by the industrial needs, we develop in this paper a practical wireless sensor network (WSN)-based industrial risk monitoring system. In the system, the existing sensor nodes in the industrial environment are organized into WSN to transmit real-time data. A special inspection robot is used to monitor comprehensively the environmental and safety risk factors in the industrial environment. To obtain the location of the robot, the partial nodes of the WSN are organized dynamically. We further propose two grid cumulative probability localization algorithms to establish the system. We show via computational experiments that the proposed risk monitoring system performs well and can be implemented as a real application which helps to improve risk management of industrial operations. [ABSTRACT FROM AUTHOR]

Over the next 30 years, it is anticipated that the world will need to source 70% more food to provide for the growing population, and it is likely that a significant amount of this will have to come from irrigated land. However, the quality of irrigation water is also important, and measuring the quality of this water will allow management decisions to be made. Soil salinity is an important parameter in crop yield, and in this paper, we describe a chloride sensor system based on a low-cost robust screen-printed chloride ion sensor, suitable for use in distributed sensor networks. Previously, this sensor has been used in controlled laboratory-based experiments, but here we provide evidence that the sensor will find application outside of the laboratory in field deployments. We report on three experiments using this sensor; one with a soil column, one using a fluvarium, and finally on an experiment in a greenhouse. All these give an insight into the movement of chloride over small distances with high temporal resolution. These initial experiments illustrate that the new sensors are viable and usable with relatively simple electronics, and although subject to ongoing development, they are currently capable of providing new scientific data at high spatial and temporal resolutions. Therefore, we conclude that such chloride sensors, coupled with a distributed wireless network, offer a new paradigm in hydrological monitoring and will enable new applications, such as irrigation using mixtures of potable and brackish water, with significant cost and resource saving. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), target surveillance has been an important application, particularly in harsh environments. With limited energy of battery-driven sensors, a challenging problem is how to schedule each sensor between active and sleep modes to maximize the network lifetime while meeting surveillance requirements. This problem is often referred to as the wake-up scheduling problem. In this paper, we consider the wake-up scheduling problem for a roadside sensor network that is deployed along a road to monitor the activities on it, such as car accidents, traffic loads and patterns, or enemy activities on battle fields. Previous research has studied a simple case of this problem in which sensors were scheduled so that the entire road could be covered by just one set of active sensors. However, to ensure the reliability of the surveillance tasks, multiple sets of active sensors are often required so that each set can independently cover the entire road. The number of such sets is referred as survivability. This paper studies the wake-up scheduling with survivability k for the road coverage problem. When k >\mbox{1}, the scheduling problem becomes more practical and interesting. We present efficient polynomial algorithms that produce an optimal schedule to satisfy the survivability requirement with the maximum lifetime for both cases k=\mbox{1}$ and k >\mbox{1}. Theoretical proof for the optimality of our algorithms is given, and simulation results further validate the correctness of our algorithms. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of efficiently deploying sensors in spatial environments, e.g., buildings, for the purposes of monitoring spatio-temporal environmental phenomena. By modeling the environmental fields using spatio-temporal Gaussian processes, a new and efficient optimality-cost function of minimizing prediction uncertainties is proposed to find the best sensor locations. Though the environmental processes spatially and temporally vary, the proposed approach of choosing sensor positions is proven not to be affected by time variations, which significantly reduces computational complexity of the optimization problem. The sensor deployment optimization problem is then solved by a practical and feasible polynomial algorithm, where its solutions are theoretically proven to be guaranteed. The proposed method is also theoretically and experimentally compared with the existing works. The effectiveness of the proposed algorithm is demonstrated by implementation in a real tested space in a university building, where the obtained results are highly promising. [ABSTRACT FROM AUTHOR]

This paper compares two radar configurations operating in the W-band with different antenna systems for measuring particle streams in the near-field range. A complex source beam is employed to describe the field characteristics. This new approach is used to derive the associated antenna gain and power patterns and applied in the radar equation for particle streams. We demonstrate how indoor measurements benefit from a bistatic radar configuration and provide measurement results for streams with particles in the micrometer size range. Owing to the bistatic configuration of the radar, the particle stream flowing through a plastic pipe has been detected showing the low interference caused by pipe walls, which enables nondestructive monitoring of particle streams through the dielectric pipe section. [ABSTRACT FROM PUBLISHER]

The capability to detect changes in an underwater scene has many applications, including environmental monitoring, surveillance of strategic maritime waterways, and naval mine countermeasures. This paper examines the automated detection of changes in repeat-pass synthetic aperture sonar (SAS) imagery by exploiting the loss of phase coherence caused by differences in the scene. A renavigation method based on track registration is developed for performing the coregistration of images. The method first determines the corrections to be applied to the repeat-pass navigation data by optimizing a linear track model using the estimated across-track pixel displacements. A second optimization is then performed using the along-track pixel displacements to determine a velocity correction for each ping location of the repeat-pass image to compensate for residual motion errors. The corrected navigation solution is then used to rebeamform the repeat-pass image onto the same focal points as the reference image. The method is demonstrated on a pair of repeat-pass SAS images acquired using the AquaPix INSAS2 sonar, where it is shown that it is not necessary to have access to raw element data to coregister images accurately enough for coherent change detection requirements. This technique is then applied to the second data set obtained using the HISAS 1030 sonar for the purpose of comparing both coherent and noncoherent (amplitude-only) approaches to change detection in scenes with low and high clutters. False alarms caused by the acoustic shadows of objects in high-clutter areas are mitigated using the single-pass interferometric coherence as a reference mask. The results show the successful use of the repeat-pass coherence to detect changes between SAS images with temporal baselines of several days on scenes consisting of mud, gravel, and sand, and demonstrate that a coherent approach is capable of detecting large-scale differences (caused by the introduction or removal of targets) as well as subtle scene changes that are not detectable using noncoherent methods. [ABSTRACT FROM AUTHOR]

Mobile crowdsensing has found a variety of applications (e.g., spectrum sensing, environmental monitoring) by leveraging the “wisdom” of a potentially large crowd of mobile users. An important metric of a crowdsensing task is data accuracy, which relies on the data quality of the participating users’ data (e.g., users’ received SNRs for measuring a transmitter’s transmit signal strength). However, the quality of a user can be its private information (which, e.g., may depend on the user’s location) that it can manipulate to its own advantage, which can mislead the crowdsensing requester about the knowledge of the data’s accuracy. This issue is exacerbated by the fact that the user can also manipulate its effort made in the crowdsensing task, which is a hidden action that could result in the requester having incorrect knowledge of the data’s accuracy. In this paper, we devise truthful crowdsensing mechanisms for Quality and Effort Elicitation (QEE), which incentivize strategic users to truthfully reveal their private quality and truthfully make efforts as desired by the requester. The QEE mechanisms achieve the truthful design by overcoming the intricate dependency of a user’s data on its private quality and hidden effort. Under the QEE mechanisms, we show that the crowdsensing requester’s optimal (RO) effort assignment assigns effort only to the best user that has the smallest “virtual valuation”, which depends on the user’s quality and the quality’s distribution. We also show that, as the number of users increases, the performance gap between the RO effort assignment and the socially optimal effort assignment decreases, and converges to 0 asymptotically. We further discuss some extensions of the QEE mechanisms. Simulation results demonstrate the truthfulness of the QEE mechanisms and the system efficiency of the RO effort assignment. [ABSTRACT FROM AUTHOR]

In recent years, the Internet of Things (IoT) has promoted many battery-powered emerging applications, such as smart home, environmental monitoring, and human healthcare monitoring, where energy management is of particular importance. Meanwhile, there is an accelerated tendency toward mobility of IoT devices, either being transported by humans or being mobile by itself. Existing energy management mechanisms for battery-powered IoT fail to consider the two significant characteristics of IoT: 1) the approximate real-time computation and 2) the mobility of IoT devices, resulting in unnecessary energy waste and network lifetime decay. In this paper, we explore mobility-aware network lifetime maximization for battery-powered IoT applications that perform approximate real-time computation under the quality-of-service (QoS) constraint. The proposed scheme is composed of offline and online stages. At offline stage, an optimal mobility-aware task schedule that maximizes network lifetime is derived by using mixed-integer linear programming technique. Redundant executions due to mobility-incurred overlapping of a single task on different IoT devices are avoided for energy savings. At online stage, a performance-guaranteed and time-efficient QoS-adaptive heuristic based on cross-entropy method is developed to adapt task execution to the fluctuating QoS requirements. Extensive simulations based on synthetic applications and real-life benchmarks have been implemented to validate the effectiveness of our proposed scheme. Experimental results demonstrate that the proposed technique can achieve up to 169.52% network lifetime improvement compared to benchmarking solutions. [ABSTRACT FROM AUTHOR]

With the development of high-resolution radar satellite for global comprehensive environmental monitoring, day-and-night and all-weather surveillance has become an active and growing research field. However, in all cases, these applications require radar to have a high-efficiency radar module (e.g., T/R module), and high system transmitting power. These requirements may put an important limitation on the performance of a radar satellite with a high-power configuration. In this paper, we report a novel waveform optimization framework. Through this framework, an advanced nonlinear frequency modulation (NLFM) waveform with lower sidelobes and a smaller main lobe, which can significantly relieve the restriction of very limited satellite power, is constructed. In addition, we apply it in a real synthetic aperture radar (SAR) system with a bandwidth of 100 MHz at 9.6-GHz carrier frequency and the whole process of the NLFM waveform for radar imaging is discussed in detail, including the system architecture and configuration, a system error compensation method, and a modified chirp scaling algorithm (CSA). The imaging results demonstrate the excellent performance of the advanced NLFM waveform. Moreover, we observe that the SAR system with the advanced waveform has a higher signal-to-noise ratio (SNR) of 1.29 dB compared with the conventional linear frequency modulation (LFM) waveform. The improvement of 1.29-dB SNR means that the real radar system can reduce transmitting power with a ratio of 25%. This effect is likely to be a potential feature of NLFM waveform, which can reduce the transmitting power requirement, especially for radar satellite. [ABSTRACT FROM AUTHOR]

Accurate rainfall monitoring is important, and previously it has been shown that microwave backhaul link between adjacent base station towers can be used for rainfall estimation. However, with the deployment of 5G using millimeter technology, there is opportunity for dense rainfall monitoring network using both backhaul and data transmission links. This paper presents our millimeter-wave measurement results during rainy days in Beijing, China. Our measurement design is an exemplary line-of-sight transmission link. We show that it is possible to retrieve the path-averaged rain rate from the measurement and to use both millimeter-wave transmission link and backhaul link to assist rainfall monitoring. Compared to the local rain measurement from rain gauge and disdrometer, we show that the correlation value of millimeter-wave link retrieved average rain rate varies between 0.6 and 0.9 for different rainfall events. There is room for improvement, and we find that if we can monitor the received signal for a sufficiently long period of time, we can quantify the bias due to fading and work out a better estimate of the rain retrieval model's reference level. [ABSTRACT FROM AUTHOR]

The radiation environment of the Galileo spacecraft is severe and poorly characterized. The Galileo orbit takes the spacecraft through the heart of the outer radiation belt, while the low levels of geomagnetic shielding throughout the orbit expose the spacecraft to intermittent intense fluxes of protons during solar energetic particle events. In the Galileo constellation, two environmental monitoring units (EMUs) are currently flying in two different orbital planes. These units monitor the radiation environment and provide critical information related to hazards for the host spacecraft and its payload. In this paper, we present the results from the analysis of the surface charge collecting plates and the proton telescope sensors. The performed numerical calibration of the EMU sensors and the application of novel unfolding and in-flight cross-calibration techniques allow the calculation of high-quality proton and electron differential fluxes. The creation of a high-quality, long-term EMU electron flux data set, is a step forward toward the improved characterization of medium earth orbit (MEO) environment through the update of the existing or the development of new radiation environment models. [ABSTRACT FROM AUTHOR]

Distributed and decentralized sensing offers a new and promising paradigm for surveillance, reconnaissance, and situation awareness. This paper introduces a new fully decentralized decision-making platform referred to as environmental monitoring station (EMS) for sensor-level detection, classification, and tracking of acoustic airborne sources in national parks. This custom-designed field-programmable gate array-based platform allows for near real-time transient source detection and classification using the 1/3 octave spectral data extracted locally from the streaming acoustic data. Source tracking through successive angle-of-arrival (AoA) estimation is also implemented locally on the EMS system using an array of microphones. The general headings of the sources generated using the AoA history and the source labels are transmitted to a park station via two possible wireless links. Field test results are provided to evaluate the performance of the overall system. [ABSTRACT FROM AUTHOR]

A multistatic dual frequency continuous-wave radar sensor with separated transmitters and receivers operating at 91.5 and 150.3 GHz is developed and used to monitor particle streams remotely through a dielectric wall. The streams consist of low concentrated multidisperse particles with sizes in the micrometer range. The physical relation between the particle stream’s mass flow and the received signal power is derived and used for calibration. Also, scattering effects are investigated and used to develop a routine for the determination of particle dimensions. In a numerical study, it is shown how a double-bistatic antenna configuration can extend the limits of double-monostatic setups. Also, it is demonstrated how the antennas need to be adapted to the measurement environment to maximize the sensitivity of the radar sensor. Finally, measurement results of the mass loading, velocity, and radius of particles from a multidisperse stream are provided and discussed. [ABSTRACT FROM AUTHOR]

This paper presents the EcoChip, a new system based on the state-of-the-art electro-chemical impedance (EIS) technologies allowing the growth of single strain organisms isolated from northern habitats. This portable system is a complete and autonomous wireless platform designed to monitor and cultivate microorganisms directly sampled from their natural environment, particularly from harsh northern environments. Using 96-well plates, the EcoChip can be used in the field for real-time monitoring of bacterial growth. Manufactured with high-quality electronic components, this new EIS monitoring system is designed to function at a low excitation voltage signal to avoid damaging the cultured cells. The high-precision calibration network leads to high-precision results, even in the most limiting contexts. Luminosity, humidity, and temperature can also be monitored with the addition of appropriate sensors. Access to robust data storage systems and power supplies is an obvious limitation for northern research. That is why the EcoChip is equipped with a flash memory that can store data over long periods of time. To resolve the power issue, a low-power micro-controller and a power management unit control and supply all electronic building blocks. Data stored in the EcoChip's flash memory can be transmitted through a transceiver whenever a receiver is located within the functional transmission range. In this paper, we present the measured performance of the system, along with results from laboratory tests in vitro and from two field tests. The EcoChip has been utilized to collect bio-environemental data in the field from the northern soils and ecosystems of Kuujjuarapik and Puvirnituq, during two expeditions, in 2017 and 2018, respectively. We show that the EcoChip can effectively carry out EIS analyses over an excitation frequency ranging from 750 Hz to 10 kHz with an accuracy of 2.35%. The overall power consumption of the system was 140.4 mW in normal operating mode and 81 μW in sleep mode. The proper development of the isolated bacteria was confirmed through deoxyribonucleic acid sequencing, indicating that bacteria thrive in the EcoChip's culture wells while the growing conditions are successfully gathered and stored. [ABSTRACT FROM AUTHOR]

High spatial resolution (HSR) imagery scene classification, which involves labeling an HSR image with a specific semantic class according to the geographical properties, has received increased attention, and many algorithms have been proposed for this task. The employment of the probabilistic topic model to acquire latent topics and the convolutional neural networks (CNNs) to capture deep features for representing HSR images has been an effective ways to bridge the semantic gap. However, the midlevel topic features are usually local and significant, whereas the high-level deep features convey more global and detailed information. In this paper, to discover more discriminative semantics for HSR images, the adaptive deep sparse semantic modeling (ADSSM) framework combining sparse topics and deep features is proposed for HSR image scene classification. In ADSSM, the fully sparse topic model and a CNN are integrated. To exploit the multilevel semantics for HSR scenes, the sparse topic features and deep features are effectively fused at the semantic level. Based on the difference between the sparse topic features and the deep features, an adaptive feature normalization strategy is proposed to improve the fusion of the different features. The experimental results obtained with four HSR image classification data sets confirm that the proposed method significantly improves the performance when compared with the other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Tropical forests are a key component of the global carbon cycle. Yet, there are still high uncertainties in forest carbon stock and flux estimates, notably because of their spatial and temporal variability across the tropics. Several upcoming spaceborne missions have been designed to address this gap. High-quality ground data are essential for accurate calibration/validation so that spaceborne biomass missions can reach their full potential in reducing uncertainties regarding forest carbon stocks and fluxes. The BIOMASS mission, a P-band SAR satellite from the European Space Agency (ESA), aims at improving carbon stock mapping and reducing uncertainty in the carbon fluxes from deforestation, forest degradation, and regrowth. In situ activities in support of the BIOMASS mission were carried out in French Guiana and Gabon during the TropiSAR and AfriSAR campaigns. During these campaigns, airborne P-band SAR, forest inventory, and lidar data were collected over six study sites. This paper describes the methods used for forest inventory and lidar data collection and analysis, and presents resulting plot estimates and aboveground biomass maps. These reference datasets along with intermediate products (e.g., canopy height models) can be accessed through ESA's Forest Observation System and the Dryad data repository and will be useful for BIOMASS but also to other spaceborne biomass missions such as GEDI, NISAR, and Tandem-L for calibration/validation purposes. During data quality control and analysis, prospects for reducing uncertainties have been identified, and this paper finishes with a series of recommendations for future tropical forest field campaigns to better serve the remote sensing community. [ABSTRACT FROM AUTHOR]

China’s railway is experiencing a restructuring from taking fossil fuels as leading resources to electrical energy with renewable energy sources (RESs). As a major consumer of energy, the railway system with RESs integration in China is discussed in this paper, and the relevant applications have been summarized and analyzed. At the same time, the development situations have not yet reached the expected target at present, and China is selecting the route for technical, investment, managerial, market and incentive policy approaches to exploit RESs potential for intelligent railway applications. Furthermore, the next-generation intelligent electrified railway with RESs is proposed and investigated, and the relevant core technologies, systems and incentive policies are summarized and elaborated. Referring to advanced experiences from other countries, the characteristics and objectives of railway energy management system based RESs are also obtained. Based on high-proportion of RESs integration, a win-win situation for railway system and RESs is expected in both economy and environment in China in the future. [ABSTRACT FROM AUTHOR]

This paper presents the development of a cyber-physical system that monitors the environmental conditions or the ambient conditions in indoor spaces at remote locations. The communication between the system’s components is performed using the existent wireless infrastructure based on the IEEE 802.11 b/g standards. The resulted solution provides the possibility of logging measurements from locations all over the world and of visualizing and analyzing the gathered data from any device connected to the Internet. This work encompasses the complete solution, a cyber-physical system, starting from the physical level, consisting of sensors and the communication protocol, and reaching data management and storage at the cyber level. The experimental results show that the proposed system represents a viable and straightforward solution for environmental and ambient monitoring applications. [ABSTRACT FROM PUBLISHER]

The seasonal cycle of landscape freeze/thaw (FT) state across mid- to high latitudes influences critical processes such as the land surface energy balance, carbon cycle dynamics related to vegetation growth, and hydrological partitioning between surface runoff and infiltration. In this paper, we produce the first daily FT classification for the 2011–2014 period based on L-band radar measurements from Aquarius. The radar FT algorithm used in this paper is based on a seasonal threshold approach, which is also the baseline algorithm applied to higher-resolution (3 km) radar measurements from NASA’s Soil Moisture Active/Passive (SMAP) mission (Launched January 31, 2015). The lower frequency (L-band) radar backscatter measurements from Aquarius provide enhanced sensitivity to FT conditions in vegetation canopy, snow and surface soil layers, although the relative radar penetration depth and sensitivity of the FT signal to these landscape elements will vary according to surface moisture and vegetation biomass conditions, and underlying land cover and terrain heterogeneity [1], [2]. Evaluation of the seasonal threshold FT algorithm using Aquarius was performed using surface air and soil temperatures from selected stations in the Snow Telemetry (SnoTel) network. Analysis identified good agreement during the fall freeze-up period with flag agreement exceeding the 80% SMAP accuracy target when summarized on a monthly basis. Disagreement was greater during the spring thaw transition due in part to uncertainty in characterizing thaw from in situ measurements. Unlike the fall season, stronger agreement in the spring was identified when the reference state was characterized with air temperature compared to soil temperature. [ABSTRACT FROM PUBLISHER]

In recent years, automated driving has become one of the most important research fields in the automotive industry. A key component for a successful substitution of human driving by vehicles is a real-time model of the current environment including the traffic situation, the guide-way, and the road itself. We propose a solution for measuring road conditions within the spring-damper system of the vehicle. In this paper, we utilize a Vehicle Level Sensor (VLS) and an Acceleration Sensor (AS), both of which are standard components in modern vehicles, for road condition monitoring. Our model-based approach therefore consists purely of additional software. We present a calculation of the Quarter Vehicle Model (QVM) for road elevation measurements to enable each connected vehicle to provide valid data for a potential crowd-sensing approach, where every vehicle contributes past data and consumes data for upcoming segments. The generated data are capable of providing the environment model with real-time data. Our calculations are first validated in a laboratory setup, representing a down-scaled Quarter-Vehicle. The knowledge gained it then applied to a real vehicle. For this purpose, the measurement setup is explained, the model-based calculation and the parameters are adjusted, and the results are compared. [ABSTRACT FROM AUTHOR]

Minimizing insecticide use, preventing vector diseases and facilitating biodiversity assessments are suitable applications of recent advances in photonic insect surveillance and entomological lidar. The tools also comprise a new window into fundamental aspect of the fascinating life and ecology of insects and their predators in situ. At the same time, it is evident that lidars are subject to finite detection range given by the instrument noise and saturation levels, and therefore, intervals of the biomass spectra are sectioned at different ranges. The Scheimpflug lidar allows an interesting trade-off between high sample rate and low pulse energy for retrieving wing beat harmonics and slow sample rates with high pulse energy for detecting small species far away. In this paper, we review and revise calibration, sizing and associated deficiencies, and report count rates to 104 insects/minute up to 2 km range. We investigate if and how high dynamic range can be exploited in entomological lidar and also how fast and slow sample rates could complement each other and capture a wider span of the biomass spectrum. We demonstrate that smaller insect can be detected further away by long exposures and show consistency between the captured biomass size spectra. However, we find unexpected discrepancies between short and long exposures in the range distributions. We found that vertebrates as well as specular insects can saturate signals. Error sources and limitations are elaborated on. [ABSTRACT FROM AUTHOR]

Geophysical phenomena like volcanic eruptions, tectonic plate movements, and earthquakes directly influence the gravitational pull in the region of occurrence. Measurement of anomalies in the gravitational pull has been a useful technique in analyzing such activities quantitatively. Gravitational variations also carry indications of the presence of the earth’s minerals and other such useful resources. In this paper, a novel on-chip micro-cantilever-based optical gravimeter has been proposed for the precise measurement of gravitational changes. Simulations comprising both three-dimensional full-vector finite element and finite difference time domain methods have been carried out for analyses, demonstrating an extensive reading range of 6000 mGal with a resolution of $1.17~\mu $ Gal and sensitivity of $1~\mu $ Gal/ $\sqrt {Hz}$ at 1 Hz. [ABSTRACT FROM AUTHOR]

This paper introduces a weighted total least squares (WTLS)-based estimator into image registration to deal with the coordinates of control points (CPs) that are of unequal accuracy. The performance of the estimator is investigated by means of simulation experiments using different coordinate errors. Comparisons with ordinary least squares (LS), total LS (TLS), scaled TLS, and weighted LS estimators are made. A novel adaptive weight determination scheme is applied to experiments with remotely sensed images. These illustrate the practicability and effectiveness of the proposed registration method by collecting CPs with different-sized errors from multiple reference images with different spatial resolutions. This paper concludes that the WTLS-based iteratively reweighted TLS method achieves a more robust estimation of model parameters and higher registration accuracy if heteroscedastic errors occur in both the coordinates of reference CPs and target CPs. [ABSTRACT FROM PUBLISHER]

Scalar field mapping has many applications including environmental monitoring, search and rescue, etc. In such applications, there is a need to achieve a certain level of confidence regarding the estimates of the scalar field. In this paper, a cooperative and active sensing framework is developed to enable scalar field mapping using multiple mobile sensor nodes. The cooperative and active controller is designed via the real-time feedback of the sensing performance to steer the mobile sensors to new locations in order to improve the sensing quality. During the movement of the mobile sensors, the measurements from each sensor node and its neighbors are fused with the corresponding confidences using distributed consensus filters. As a result, an online map of the scalar field is built while achieving a certain level of confidence of the estimates. We conducted computer simulations to validate and evaluate our proposed algorithms. [ABSTRACT FROM AUTHOR]

Upon tightening new regulations, the demand for using smart wireless sensing for health, safety, and surveillance applications of nuclear installations is growing rapidly. To help with the developments, this paper describes the design of a practical small-scaled wireless sensor network (WSN) that allows a smart real-time monitoring of radiation levels at nuclear facilities. A wireless system compiled with a radiation sensor and associated peripherals been developed and implemented upon ZigBee technology using TI CC2530 chip. The radiation sensor uses a Geiger Muller tube as a reliable detector for the radioactive particulates in the gaseous effluent vented from nuclear facilities. The WSN allows the operators to record and control the radiation levels emitted to the environment and it is supported with a warning system, for the early detection of radiation release. However, building a reliable wireless sensing system with an effective coverage, especially for indoor applications, requires professional planning and proper investigation. In this paper, a procedure to investigate the wireless sensing coverage is reported where a ray-tracing simulator is adopted to enable the wireless node placement prior to the deployment. A real test scenario has been implemented based on the built wireless node to examine network coverage inside a 60-m hallway and results have been compared with simulations showing a 100% packet reception ratio. [ABSTRACT FROM PUBLISHER]

The sensors in water surface sensor networks are sparsely deployed, which means the distance between sensors is generally larger than the sensors’ communication radius. So, it is difficult for sensors to aggregate their data by multihop routing. If water surface mobile sink is to be used to collect the sensors’ data, its path forming method is a key issue. In this paper, the Voronoi diagram and dominating set theory are introduced, and a path forming method called path forming Voronoi diagram dominating set (PFVD) for water surface mobile sink is proposed. First, the water surface sensor network structure is converted into a Voronoi diagram and the adjacent sensors’ data can be collected at the generated Voronoi vertexes position. Second, the dominating set theory is used to compute the minimum set of Voronoi vertexes that can gather all the sensors’ data. Finally, based on the minimum set, the final path for mobile sink is formed. Theoretical analysis and extensive experiments demonstrate that PFVD has the advantages of short path and balanced energy consumption in different scale networks. [ABSTRACT FROM AUTHOR]

Over the past few years, regional air pollution has frequently occurred in Mid-Eastern China, especially in Beijing. As the primary pollutant in urban air, atmospheric particulate matter (PM) not only leads to the decrease of atmospheric visibility, but also increases the mortality and morbidity of respiratory system diseases. By analyzing aerosol volume size distribution data downloaded from the AERONET official website, we find that the size distribution of aerosol in Beijing appears a bimodal log-normal structures and parameters of fine mode in AERONET data are mainly contributed by PM $_{\text{2.5}}$. In this paper, a physically based model is developed to estimate the concentration of PM $_{\text{2.5}}$ , in which, fine mode aerosol optical depth (AOD) at 440, 550, and 675 nm, Effective Radius of the Fine particles, ground-based fine particulate matter (PM $_{\text{2.5}}$ ) data, relative humidity, and boundary layer height data from 2015 to 2016 are used. Those from 2015 are used for calculating integrated extinction efficiencies (〈Qext〉) based on the model, and those from 2016 are used for PM $_{\text{2.5}}$ validation. Result shows that R 2 of retrieved PM $_{\text{2.5}}$ against ground-based PM $_{\text{2.5}}$ can reach to 0.70 and RMSE is 33.67 μg/m3 at Beijing site at 440 nm. This study concludes that this method has the potential to retrieve PM $_{\text{2.5}}$ by using AERONET AOD in Beijing, which is independent of ground-based PM $_{\text{2.5}}$ measurement. [ABSTRACT FROM AUTHOR]

This paper evaluates two alternative regression techniques for oceanic chlorophyll-a (Chl-a) content estimation. One of the investigated methodologies is the recently introduced Gaussian process regression (GPR) model. We explore two feature ranking methods derived for the GPR model, namely sensitivity analysis (SA) and automatic relevance determination (ARD). We also investigate a second regression method, the partial least squares regression (PLSR) for oceanic Chl-a content estimation. Feature relevance in the PLSR model can be accessed through the variable importance in projection (VIP) feature ranking algorithm. This paper thus analyzes three feature ranking models, SA, ARD, and VIP, which are all derived from different fundamental principles, and uses the ranked features as inputs to the GPR and PLSR to assess regression strengths. We compare the regression performances using some common performance measures, and show how the feature ranking methods can be used to find the lowest number of features to estimate oceanic Chl-a content by using the GPR and PLSR models, while still producing comparable performance to the state-of-the-art algorithms. We evaluate the models on a global MEdium Resolution Imaging Spectrometer matchup dataset. Our results show that the GPR model has the best regression performance for most of the input feature sets we used, and our conclusion is this model can favorably be used for Chl-a content retrieval, already with two features, ranked by either the SA or ARD methods. [ABSTRACT FROM PUBLISHER]

From the attenuation measurements collected by a network of spatially distributed sensors, radio tomography constructs spatial loss fields (SLFs) that quantify absorption of radiofrequency waves at each location. These SLFs can be used for interference prediction in (possibly cognitive) wireless communication networks, for environmental monitoring or intrusion detection in surveillance applications, for through-the-wall imaging, for survivor localization after earthquakes or fires, etc. The cornerstone of radio tomography is to model attenuation as the bidimensional integral of the SLF of interest scaled by a weight function. Unfortunately, existing approaches (i) rely on heuristic assumptions to select the weight function and (ii) are limited to imaging changes in the propagation medium or they require a separate calibration step with measurements in free space. The first major contribution in this paper addresses (i) by means of a blind radio tomographic approach that learns the SLF together with the aforementioned weight function from the attenuation measurements. This challenging problem is tackled by capitalizing on contemporary kernel-based learning tools together with various forms of regularization that leverage prior knowledge. The second contribution addresses (ii) by means of a novel calibration technique capable of imaging static structures without separate calibration steps. Numerical tests with real and synthetic measurements validate the efficacy of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, there are usually a small number of position-aware nodes, referred to as anchors. Every other node, called target node (or sensor), can estimate its distances to surrounding anchors according to the received signal strength. Then, trilateration can be employed for self localization. This system is low-cost, simple, and thus popular, but it suffers from two major drawbacks. The first problem is the requirement for realtime channel parameters, which should be estimated and transmitted to target sensors by a central node. Another problem arises from the fact that positioning accuracy is dependent on number of anchors, but anchor density is quite limited for economic consideration in the conventional localization systems. To solve these problem, we proposed in this paper, a new localization framework for wireless sensor networks, which employs a mobile node to serve as virtual anchors. This new framework has two advantages over the conventional one: 1) Offline measurements are totally unnecessary; 2) the number of virtual anchors can be vastly increased with negligible cost, leading to very high positioning accuracy. To justify the superiority of this new framework, extensive analysis and simulations were conducted. Besides, we implemented it on a drone and did field experiments, which showed very promising results. [ABSTRACT FROM PUBLISHER]

Remote sensing is a powerful technology for Earth observation (EO), and it plays an essential role in many applications, including environmental monitoring, precision agriculture, resource managing, urban characterization, disaster and emergency response, etc. However, due to limitations in the spectral, spatial, and temporal resolution of EO sensors, there are many situations in which remote sensing data cannot be fully exploited, particularly in the context of emergency response (i.e., applications in which real/near-real-time response is needed). Recently, with the rapid development and availability of social media data, new opportunities have become available to complement and fill the gaps in remote sensing data for emergency response. In this paper, we provide an overview on the integration of social media and remote sensing in time-critical applications. First, we revisit the most recent advances in the integration of social media and remote sensing data. Then, we describe several practical case studies and examples addressing the use of social media data to improve remote sensing data and/or techniques for emergency response. [ABSTRACT FROM PUBLISHER]

Commercial wireless microwave links have been recently proven to be an effective tool for precipitation monitoring, mainly for accurate rainfall estimation and high-resolution rainfall mapping. This paper focuses on the challenge of precipitation classification from the measurements of received signal level (RSL) in several commercial wireless microwave links, by suggesting a tree of classification based on the physical features that distinguish between different phenomena. Wet periods are first identified, followed by a classification of the wet periods into pure rain or sleet. The classification is based on the kernel Fisher discriminant analysis, followed by a decision-making process. The suggested procedure is tested on real data, and its performance is evaluated. It is shown that the proposed classification is in very good agreement (85%) with that of a special-purpose meteorological device called disdrometer. [ABSTRACT FROM AUTHOR]

This paper presents the development of a low-power electronic system powered by a thermoelectric generator, based on Peltier cells, which takes advantage of daily temperature variations in the environment using a tank with 6 liters of water as a thermal accumulator. The generator works both day and night, since it uses a dc-dc boost converter with bipolar input. The system avoids using batteries because it charges a 0.1 F supercapacitor module between 2.40 and 3.15 V, so the useful energy in each charging cycle is 208 mJ. This energy is sufficient so that the system can measure the ambient temperature, represent it on a display and record it along with the date and time on a memory card when there is enough stored energy. The PSpice model and the simulation of the generator is also presented and experimental results of its operation are presented for two days. It is derived that in that period, with a maximum temperature difference of 10 °C, the system is able to charge the supercapacitor thirteen times per day, which means a daily useful energy of 2.7 J. Finally, results of the autonomous system performance for four days are presented. [ABSTRACT FROM AUTHOR]

Management of resource consumption in residential areas should take into account advances in the concept of Community Energy Management Systems, in which communities are involved in the monitoring and coordination of resource consumption. This paper presents a monitoring environment enabling the provision of monitoring and negotiation rules among residential users in order to minimize dispersion in total power consumption. It establishes mechanisms of detection of inconsistencies based on Linear Temporal Logic rules and mechanisms based on publication-subscription for transfer of quota among neighbors. The system is implemented and validated in a home environment. [ABSTRACT FROM PUBLISHER]

This paper is concerned with the problem of distributed multitask learning over networks, which aims to simultaneously infer multiple node-specific parameter vectors in a collaborative manner. Most of the existing works on the distributed multitask problem modeled the task relatedness by assuming some similarities of parameter vectors in an explicit way. In this paper, we implicitly model the similarity of parameter vectors by assuming that the parameter vectors share a common low-dimensional predictive structure on hypothesis spaces, which is learned using the available data in networks. A distributed structure learning algorithm for the in-network cooperative estimation problem is then derived based on the block coordinate descent method integrated with the inexact alternating direction method of multipliers technique. Simulations on both synthetic and real-world datasets are given to verify the effectiveness of the proposed algorithm. In the case that each node shares a common predictive subspace, it is demonstrated that the proposed multitask algorithm outperforms the noncooperative learning algorithm. Moreover, the use of the inexact approach can significantly reduce the communication bandwidth and still provide the same optimal solution as the corresponding centralized approach. [ABSTRACT FROM AUTHOR]

The abrupt event monitoring is a challenging and critical issue in water environment systems. There are two main different abrupt events in the monitoring system, namely, the emergency water pollution accident and the abrupt sensor fault. The two different abrupt events have similar data characteristics, and few methods can be used to recognize the events. In this paper, a novel abrupt event monitoring approach based on kernel principal component analysis (KPCA) and support vector machines is proposed, which is combined with the physical redundancy method. The trust mechanism is introduced into the proposed approach to reduce the interference of external noise and improve the performance of quick response for the abrupt events. A spare data area is set up to store the data for the KPCA modeling. The data in the spare data area are updated continuously, and the KPCA model is updated subsequently to improve the adaptivity of the KPCA model for the abrupt event monitoring. The experimental results show that the proposed approach is capable of detecting and recognizing the two different abrupt events efficiently. [ABSTRACT FROM PUBLISHER]

The advent of routine collection of high-quality digital photography provides for traditional uses, as well as “remote sensing” uses such as the monitoring of environmental indicators. A well-devised monitoring system, based on consistent data and methods, provides the opportunity to track and communicate changes in features of interest in a way that has not previously been possible. Data that are geometrically and radiometrically consistent are fundamental to establishing systems for monitoring. In this paper, we focus on models for the radiometric calibration of mosaics consisting of thousands of images. We apply the models to the data acquired by the Australian Commonwealth Scientific and Industrial Research Organisation and its partners as part of regular systematic acquisitions over the city of Perth for a project known as Urban Monitor. One goal of the project, and hence the model development, is to produce annually updated mosaics calibrated to reflectance at 0.2-m ground sample distance for an area of approximately 9600 \km^2. This equates to terabytes of data and, for frame-based instruments, tens of thousands of images. For the experiments considered in this paper, this requires mosaicking estimates derived from 3000 digital photographic frames, and the methods will shortly be expanded to 30 000+ frames. A key part of the processing is the removal of spectral variation due to the viewing geometry, typically attributed to the bidirectional reflectance distribution function (BRDF) of the land surface. A variety of techniques based on semiempirical BRDF kernels have been proposed in the literature for correcting the BRDF effect in single frames, but mosaics with many frames provide unique challenges. This paper presents and illuminates a complete empirical radiometric calibration method for digital aerial frame mosaics, based on a combined model that uses kernel-based techniques for BRDF correction and incorporates additive and multiplicative terms for correcting other effects, such as variations due to the sensor and atmosphere. Using ground truth, which consists of laboratory-measured white, gray, and black targets that were placed in the field at the time of acquisition, we calculate the fundamental limitations of each model, leading to an optimal result for each model type. We demonstrate estimates of ground reflectance that are accurate to approximately 10%, 5%, and 3% absolute reflectances for ground targets having reflectances of 90%, 40%, and 4%, respectively. [ABSTRACT FROM AUTHOR]

Although the Geoscience Laser Altimeter System (GLAS) onboard the NASA Ice, Cloud and Land Elevation Satellite was not designed for urban applications, its 3-D measurement capability over the globe makes it a nice feature for consideration in monitoring urban heights. However, this has not been previously done. In this paper, we report some preliminary assessment of the GLAS data for building height and density estimation in a suburb of Beijing, China. Building heights can be directly calculated from a GLAS data product (GLA14). Because GLA14 limits height levels to six in each ground footprint, we developed a new method to remove this restriction by processing the raw GLAS data. The maximum heights measured in the field at selected GLAS footprints were used to validate the GLAS measurement results. By assuming a constant incident energy and surface reflectance within a GLAS footprint, the building density can be estimated from GLA14 or from our newly processed GLAS data. The building density determined from high-resolution images in Google Earth was used to validate the GLAS estimation results. The results indicate that the newly developed method can produce more accurate building height estimation within each GLAS footprint ( R^2 = 0.937, \rmse = 6.4\ \m, and n = 26) than the GLA14 data product (R^2 = 0.808, \rmse = 11.5\ \m, and n = 26). However, satisfactory estimation results on building density cannot be obtained from the GLAS data with the methods investigated in this paper. Forest cover could be a challenge to building height and density estimation from the GLAS data. It should be addressed in future research. [ABSTRACT FROM AUTHOR]

Contour line estimation in a monitored physical phenomenon using wireless sensor networks (WSNs) with cross-layer considerations is investigated in this paper. Herein, we present a statistical approach to locate contour points for contour line construction in the presence of irregular sensor deployment with the assistant of Delaunay triangular meshes. The proposed contour-estimation algorithm has certain error-correction capability, and it is robust to the data degradation coming from sensing and/or communication noise. To ensure the success of the proposed algorithm, we further analyze three sources of signal distortion: sensing noise, data quantization error, and data communication noise. We find that choosing a proper data quantization level can balance the communication cost and the desired signal quality. Meanwhile, the impact of channel impairment can be mitigated by the appropriate data-fusion mechanism. In this paper, an adaptive data-fusion mechanism is proposed to avoid excessive packet retransmissions. The correctness of the proposed approach is verified through simulation, and the effects of different system parameters on the overall system performance are also given to show the robustness of the proposed algorithm. This paper also offers some guidelines on the design of the WSN system. [ABSTRACT FROM AUTHOR]

Abstract-A second-order microelectromechanical systems (MEMS) filter with high selectivity and sharp rolloff is required in wireless transceivers used in dense wireless sensor networks (WSNs). These sensors are expected to replace existing wired sensors used in industrial-plant management and environmental monitoring. These filters, together with MEMS-based oscillators and mixers, are expected to replace off-chip components and enable the development of a single-chip transceiver. Such a transceiver will leverage the integrated MEMS components' characteristics to operate at lower power and, hence, longer battery life, making autonomous WSNs more feasible in a wider range of applications. As a result, this paper presents the design and optimization of the coupling beam of wineglass-mode micromechanical disk filters using simulated annealing. The filter under consideration consists of two identical wineglass-mode disk resonators, mechanically coupled by a flexural-mode beam. The coupled two-resonator system exhibits two mechanical-resonance modes with closely spaced frequencies that define the filter passband. A constraint is added on the beam length to eliminate the effect of the coupling-beam mass on the filter's resonant frequency. A new process flow is proposed to realize self-aligned overhanging coupling beams designed in this paper. [ABSTRACT FROM AUTHOR]

Nowadays, Earth is being affected by the greenhouse effect. A first step to improve the current situation is to track the involved variables under temporal and spatial sampling. In this paper, we propose an integrated system for monitoring the related greenhouse effect gasses and several environmental variables. The system has been designed to be mounted on an unmanned aerial vehicle to overcome the spatial constraints and it also has been designed to work under the Internet of Things paradigm to overcome the temporal constraints. Unlike previous approaches, we have integrated an optimal filtering step with Kalman Filter, improving the reliability and precision of the measurements. Our experiments show that the proposed system can provide the information to the final user in near real-time. In addition, the use of the Kalman filter decreases the mean square error of our system with respect to a reference sensor. [ABSTRACT FROM AUTHOR]

Microwave radiometers are the key sensors to globally monitor environmental parameters; however, it suffers from its low and nonuniform spatial resolution. In this paper, a superresolution (SR) technique based on image degradation and residual network is proposed to enhance the spatial resolution of microwave radiometer data. Specifically, an improved degradation model is proposed to construct pairs of high-resolution (HR) and low-resolution (LR) data for training and testing. In addition, a new residual network connected by the SR main and gradient auxiliary branches in parallel is designed to achieve SR reconstructions, where eight-channel gradient maps extracted from LR data are input into the auxiliary branch to help to reconstruct. SR results are eventually generated by the trained SR network. Experiments executed on both simulated and actual data demonstrate the soundness and the superiority of the proposed SR technique. [ABSTRACT FROM AUTHOR]

Besides traditional 5G MBB and ULLRC services, 5G networks will also embody connections to billions of Internet of Things (IoT) objects. Future IoT 5G-mMTC applications for environmental monitoring, remote metering, and surveillance over metropolitan areas are among the main enablers of the Smart City concept. The role of satellite to transfer large amount of data gathered from (any type of) sensors spread over geographical metropolitan areas in a country can be helpful to promote the implementation of smart applications aimed for example to optimize usage of resources (e.g., energy), transportation flows, centralized control of critical infrastructures, etc., at a nation-wide scale. One of the main limitations of this scenario consists in the availability of sensors devices incorporating transmission facilities to directly connect to the satellite. In this paper, we assess the achievable performance of a FttC-based capillary network extended to include terrestrial radio stations (RSs) collecting data from surrounding IoT devices. RS data are then transferred using one (or more) copper pair(s) to the satellite modem installed at the cabinet as well as at the central office. Performance of the considered network architecture is expressed in terms of the blocking probability for satellite access. [ABSTRACT FROM AUTHOR]

In this paper, the estimation of a scalar field over a bidimensional scenario (e.g., the atmospheric pressure in a wide area) through a self-organizing wireless sensor network (WSN) with energy constraints is investigated, The sensor devices (denoted as nodes) are randomly distributed; they transmit samples to a supervisor by using a clustered network. This paper provides a mathematical framework to analyze the interdependent aspects of WSN communication protocol and signal processing design. Channel modelling and connectivity issues, multiple access control and routing, and the role of distributed digital signal processing (DDSP) techniques are accounted for. The possibility that nodes perform DDSP is studied through a distributed compression technique based on signal resampling. The DDSP impact on network energy efficiency is compared through a novel mathematical approach to the case where the processing is performed entirely by the supervisor. The trade-off between energy conservation (i,e., network lifetime) and estimation error is discussed and a design criterion is proposed as well. Comparison to simulation outcomes validates the model. As an example result, the required node density is found as a trade-off between estimation quality and network lifetime for different system parameters and scalar field characteristics. It is shown that both the DDSP technique and the MAC protocol choice have a relevant impact on the performance of a WSN. [ABSTRACT FROM AUTHOR]