Dense and long-term monitoring of Earth surface processes with passive RFID -- a review

Billions of Radio-Frequency Identification (RFID) passive tags are produced yearly to identify goods remotely. New research and business applications are continuously arising, including recently localization and sensing to monitor earth surface processes. Indeed, passive tags can cost 10 to 100 times less than wireless sensors networks and require little maintenance, facilitating years-long monitoring with ten's to thousands of tags. This study reviews the existing and potential applications of RFID in geosciences. The most mature application today is the study of coarse sediment transport in rivers or coastal environments, using tags placed into pebbles. More recently, tag localization was used to monitor landslide displacement, with a centimetric accuracy. Sensing tags were used to detect a displacement threshold on unstable rocks, to monitor the soil moisture or temperature, and to monitor the snowpack temperature and snow water equivalent. RFID sensors, available today, could monitor other parameters, such as the vibration of structures, the tilt of unstable boulders, the strain of a material, or the salinity of water. Key challenges for using RFID monitoring more broadly in geosciences include the use of ground and aerial vehicles to collect data or localize tags, the increase in reading range and duration, the ability to use tags placed under ground, snow, water or vegetation, and the optimization of economical and environmental cost. As a pattern, passive RFID could fill a gap between wireless sensor networks and manual measurements, to collect data efficiently over large areas, during several years, at high spatial density and moderate cost.Comment: Invited paper for Earth Science Reviews. 50 pages without references. 31 figures. 8 table

Utilization Of A Large-Scale Wireless Sensor Network For Intrusion Detection And Border Surveillance

To control the border more effectively, countries may deploy a detection system that enables real-time surveillance of border integrity. Events such as border crossings need to be monitored in real time so that any border entries can be noted by border security forces and destinations marked for apprehension. Wireless Sensor Networks (WSNs) are promising for border security surveillance because they enable enforcement teams to monitor events in the physical environment. In this work, probabilistic models have been presented to investigate senor development schemes while considering the environmental factors that affect the sensor performance. Simulation studies have been carried out using the OPNET to verify the theoretical analysis and to find an optimal node deployment scheme that is robust and efficient by incorporating geographical coordination in the design. Measures such as adding camera and range-extended antenna to each node have been investigated to improve the system performance. A prototype WSN based surveillance system has been developed to verify the proposed approach

High Accuracy Distributed Target Detection and Classification in Sensor Networks Based on Mobile Agent Framework

High-accuracy distributed information exploitation plays an important role in sensor networks. This dissertation describes a mobile-agent-based framework for target detection and classification in sensor networks. Specifically, we tackle the challenging problems of multiple- target detection, high-fidelity target classification, and unknown-target identification. In this dissertation, we present a progressive multiple-target detection approach to estimate the number of targets sequentially and implement it using a mobile-agent framework. To further improve the performance, we present a cluster-based distributed approach where the estimated results from different clusters are fused. Experimental results show that the distributed scheme with the Bayesian fusion method have better performance in the sense that they have the highest detection probability and the most stable performance. In addition, the progressive intra-cluster estimation can reduce data transmission by 83.22% and conserve energy by 81.64% compared to the centralized scheme. For collaborative target classification, we develop a general purpose multi-modality, multi-sensor fusion hierarchy for information integration in sensor networks. The hierarchy is com- posed of four levels of enabling algorithms: local signal processing, temporal fusion, multi-modality fusion, and multi-sensor fusion using a mobile-agent-based framework. The fusion hierarchy ensures fault tolerance and thus generates robust results. In the meanwhile, it also takes into account energy efficiency. Experimental results based on two field demos show constant improvement of classification accuracy over different levels of the hierarchy. Unknown target identification in sensor networks corresponds to the capability of detecting targets without any a priori information, and of modifying the knowledge base dynamically. In this dissertation, we present a collaborative method to solve this problem among multiple sensors. When applied to the military vehicles data set collected in a field demo, about 80% unknown target samples can be recognized correctly, while the known target classification ac- curacy stays above 95%

A review of laser scanning for geological and geotechnical applications in underground mining

Laser scanning can provide timely assessments of mine sites despite adverse challenges in the operational environment. Although there are several published articles on laser scanning, there is a need to review them in the context of underground mining applications. To this end, a holistic review of laser scanning is presented including progress in 3D scanning systems, data capture/processing techniques and primary applications in underground mines. Laser scanning technology has advanced significantly in terms of mobility and mapping, but there are constraints in coherent and consistent data collection at certain mines due to feature deficiency, dynamics, and environmental influences such as dust and water. Studies suggest that laser scanning has matured over the years for change detection, clearance measurements and structure mapping applications. However, there is scope for improvements in lithology identification, surface parameter measurements, logistic tracking and autonomous navigation. Laser scanning has the potential to provide real-time solutions but the lack of infrastructure in underground mines for data transfer, geodetic networking and processing capacity remain limiting factors. Nevertheless, laser scanners are becoming an integral part of mine automation thanks to their affordability, accuracy and mobility, which should support their widespread usage in years to come

Structural health monitoring of civil infrastructure

Structural health monitoring (SHM) is a term increasingly used in the last decade to describe a range of systems implemented on full-scale civil infrastructures and whose purposes are to assist and inform operators about continued 'fitness for purpose' of structures under gradual or sudden changes to their state, to learn about either or both of the load and response mechanisms. Arguably, various forms of SHM have been employed in civil infrastructure for at least half a century, but it is only in the last decade or two that computer-based systems are being designed for the purpose of assisting owners/operators of ageing infrastructure with timely information for their continued safe and economic operation. This paper describes the motivations for and recent history of SHM applications to various forms of civil infrastructure and provides case studies on specific types of structure. It ends with a discussion of the present state-of-the-art and future developments in terms of instrumentation, data acquisition, communication systems and data mining and presentation procedures for diagnosis of infrastructural 'health'

Detection of mine roof failure using inexpensive LiDAR technology

Slope, Roof, and mine wall stability problems are some of the main reasons for deaths at U.S. surface or underground mining. The safety instruments were not enough to prevent that failure or even predict it before it occurs. However, the cost of such a tool that can be helpful in detecting roof failures is very high and not reachable in most instances. The present study investigates the feasibility of using the M16 Leddar Evaluation Kit to detect the roof failure in mines. The M16 Leddar Evaluation kit cost is between 300$-800$, so it is the reachable price if it provides the required safety in mines. In fact, the underground mines have many openings, so the needs for instruments that can be distributed in all tunnels and safe all workers are urgent. The Leica Scan Station P40-3D Laser Scanner costs $123915.00, so in mine industry, it is not worthy to establish the mining with such high cost like that. Buying one unit of the Leica ScanStation P40-3D Laser Scanner to provide the safety and minimize the expenses in the mining industry is not a practical idea which is providing safety to some of the workers in one tunnel spot and neglect the others. Steel movement plate has been built and attached to a linear actuator that can move with a resolution around 0.00375 mm per step and stroke 50 mm in order to simulate the roof failures in mines. It is not possible to try the M16 in real mine due to the time limits and absence of not unstable mines locally, besides the intention that the author has to start with an office environment. The M16 Leddar Evaluation kit is aimed directly to movement plate and collecting the deformation derived by the actuator. The results collected has many of anomalies and irregular data that can be eliminated by doing some of the statistical identification of outliers. The results show that the M16 Leddar evaluation kit is capable of detecting the movement plate profile with a precision between 0.1 mm and 3 mm per integration period --Abstract, page iii

Advanced technologies for perimeter intrusion detection sensors

The development of integrated circuit fabrication techniques and the resulting devices have contributed more to the advancement of exterior intrusion detectors and alarm assessment devices than any other technology. The availability of this technology has led to the improvements in and further development of smaller more powerful computers, microprocessors, solid state memories, solid state cameras, thermal imagers, low-power lasers, and shorter pulse width and higher frequency electronic circuitry. This paper presents information on planning a perimeter intrusion detection system, identifies the site characteristics that affect its performance, and describes improvements to perimeter intrusion detection sensors and assessment devices that have been achieved by using integrated circuit technology

Wireless sensor networks for landslide monitoring: application and optimization by visibility analysis on 3D point clouds

Occurring in many geographical, geological and climatic environments, landslides represent a major geological hazard. In landslide prone areas, monitoring devices associated with Early Warning Systems are a cost-effective means to reduce the risk with a low environmental and economic impact, and in some cases, they can be the only solution. In this framework, particular interest has been reserved for Wireless Sensor Networks (WSNs), defined as networks of usually low-size and low-cost devices denoted as nodes, which are integrated with sensors that can gather information through wireless links. In this thesis, data from a new prototypical ground instability monitoring instrument called Wi-GIM (Wireless sensor network for Ground Instability Monitoring) have been analysed. The system consists in a WSN made by nodes able to measure their mutual inter-distances by calculating the time of flight of an Ultra-Wide Band impulse. Therefore, no sensors are implemented in the network, as the same signals used for transmission are also used for ranging. The system has been tested in a controlled outdoor environment and applied for the monitoring of the displacements of an actual landslide, the Roncovetro mudflow in Central Italy, where a parallel monitoring with a Robotic Total Station (RTS) allowed to validate the system. The outputs are displacement time series showing the distance of each couple of nodes belonging to the same cluster. Data retrieved from the tests revealed a precision of 2–5 cm and that measurements are influenced by the temperature. Since the correlation with this parameter has proved to be linear, a simple correction is sufficient to improve the precision and remove the effect of temperature. The campaign also revealed that measurements were not affected by rain or snow, and that the system can efficiently communicate up to 150 m with a 360° angle of view without affecting precision. Other key features of the implemented system are easy and quick installation, flexibility, low cost, real-time monitoring and acquisition frequency changeability. The comparison between Wi-GIM and RTS measurements pointed out the presence of an offset (in an order that vary from centimetric to decametric) constant for each single couple, due mainly to the presence of obstacles that can obstruct the Line Of Sight (LOS). The presence of vegetation is the main cause of the non-LOS condition between two nodes, which translates in a longer path of the signals and therefore to a less accurate distance measurements. To go further inside this issue, several tests have been carried out proving the strong influence of the vegetation over both data quantity and quality. To improve them, a MATLAB tool (R2018a, MAthWorks, Natick, MA, USA) called WiSIO (Wireless Sensor network Installation Optimizer) has been developed. The algorithm finds the best devices deployment following three criteria: (i) inter-visibility by means of a modified version of the Hidden Point Removal operator; (ii) equal distribution; (iii) positioning in preselected priority areas. With respect to the existing viewshed analysis, the main novelty is that it works directly with 3D point clouds, without rendering them or performing any surface. This lead to skip the process of generating surface models avoiding errors and approximations, that is essential when dealing with vegetation. A second installation of the Wi-GIM system has been therefore carried out considering the deployment suggested by WiSIO. The comparison of data acquired by the system positioned with and without the help of the proposed algorithm allowed to better comprehend the effectiveness of the tool. The presented results are very promising, showing how a simple elaboration can be essential to have more and more reliable data, improving the Wi-GIM system performances, making it even more usable in very complex environments and increasing its flexibility. The main left limitation of the Wi-GIM system is currently the precision. Such issue is connected to the aim of using only low-cost components, and it can be prospectively overcome if the system undergoes an industrialization process. Furthermore, since the system architecture is re-adaptable, it is prone to enhancements as soon as the technology advances and new low cost hardware enters the market

Internet of Things for Environmental Sustainability and Climate Change

Our world is vulnerable to climate change risks such as glacier retreat, rising temperatures, more variable and intense weather events (e.g., floods, droughts, and frosts), deteriorating mountain ecosystems, soil degradation, and increasing water scarcity. However, there are big gaps in our understanding of changes in regional climate and how these changes will impact human and natural systems, making it difficult to anticipate, plan, and adapt to the coming changes. The IoT paradigm in this area can enhance our understanding of regional climate by using technology solutions, while providing the dynamic climate elements based on integrated environmental sensing and communications that is necessary to support climate change impacts assessments in each of the related areas (e.g., environmental quality and monitoring, sustainable energy, agricultural systems, cultural preservation, and sustainable mining). In the IoT in Environmental Sustainability and Climate Change chapter, a framework for informed creation, interpretation and use of climate change projections and for continued innovations in climate and environmental science driven by key societal and economic stakeholders is presented. In addition, the IoT cyberinfrastructure to support the development of continued innovations in climate and environmental science is discussed

Spatio-temporal coverage optimization of sensor networks

Les réseaux de capteurs sont formés d’un ensemble de dispositifs capables de prendre individuellement des mesures d’un environnement particulier et d’échanger de l’information afin d’obtenir une représentation de haut niveau sur les activités en cours dans la zone d’intérêt. Une telle détection distribuée, avec de nombreux appareils situés à proximité des phénomènes d’intérêt, est pertinente dans des domaines tels que la surveillance, l’agriculture, l’observation environnementale, la surveillance industrielle, etc. Nous proposons dans cette thèse plusieurs approches pour effectuer l’optimisation des opérations spatio-temporelles de ces dispositifs, en déterminant où les placer dans l’environnement et comment les contrôler au fil du temps afin de détecter les cibles mobiles d’intérêt. La première nouveauté consiste en un modèle de détection réaliste représentant la couverture d’un réseau de capteurs dans son environnement. Nous proposons pour cela un modèle 3D probabiliste de la capacité de détection d’un capteur sur ses abords. Ce modèle inègre également de l’information sur l’environnement grâce à l’évaluation de la visibilité selon le champ de vision. À partir de ce modèle de détection, l’optimisation spatiale est effectuée par la recherche du meilleur emplacement et l’orientation de chaque capteur du réseau. Pour ce faire, nous proposons un nouvel algorithme basé sur la descente du gradient qui a été favorablement comparée avec d’autres méthodes génériques d’optimisation «boites noires» sous l’aspect de la couverture du terrain, tout en étant plus efficace en terme de calculs. Une fois que les capteurs placés dans l’environnement, l’optimisation temporelle consiste à bien couvrir un groupe de cibles mobiles dans l’environnement. D’abord, on effectue la prédiction de la position future des cibles mobiles détectées par les capteurs. La prédiction se fait soit à l’aide de l’historique des autres cibles qui ont traversé le même environnement (prédiction à long terme), ou seulement en utilisant les déplacements précédents de la même cible (prédiction à court terme). Nous proposons de nouveaux algorithmes dans chaque catégorie qui performent mieux ou produits des résultats comparables par rapport aux méthodes existantes. Une fois que les futurs emplacements de cibles sont prédits, les paramètres des capteurs sont optimisés afin que les cibles soient correctement couvertes pendant un certain temps, selon les prédictions. À cet effet, nous proposons une méthode heuristique pour faire un contrôle de capteurs, qui se base sur les prévisions probabilistes de trajectoire des cibles et également sur la couverture probabiliste des capteurs des cibles. Et pour terminer, les méthodes d’optimisation spatiales et temporelles proposées ont été intégrées et appliquées avec succès, ce qui démontre une approche complète et efficace pour l’optimisation spatio-temporelle des réseaux de capteurs.Sensor networks consist in a set of devices able to individually capture information on a given environment and to exchange information in order to obtain a higher level representation on the activities going on in the area of interest. Such a distributed sensing with many devices close to the phenomena of interest is of great interest in domains such as surveillance, agriculture, environmental monitoring, industrial monitoring, etc. We are proposing in this thesis several approaches to achieve spatiotemporal optimization of the operations of these devices, by determining where to place them in the environment and how to control them over time in order to sense the moving targets of interest. The first novelty consists in a realistic sensing model representing the coverage of a sensor network in its environment. We are proposing for that a probabilistic 3D model of sensing capacity of a sensor over its surrounding area. This model also includes information on the environment through the evaluation of line-of-sight visibility. From this sensing model, spatial optimization is conducted by searching for the best location and direction of each sensor making a network. For that purpose, we are proposing a new algorithm based on gradient descent, which has been favourably compared to other generic black box optimization methods in term of performance, while being more effective when considering processing requirements. Once the sensors are placed in the environment, the temporal optimization consists in covering well a group of moving targets in the environment. That starts by predicting the future location of the mobile targets detected by the sensors. The prediction is done either by using the history of other targets who traversed the same environment (long term prediction), or only by using the previous displacements of the same target (short term prediction). We are proposing new algorithms under each category which outperformed or produced comparable results when compared to existing methods. Once future locations of targets are predicted, the parameters of the sensors are optimized so that targets are properly covered in some future time according to the predictions. For that purpose, we are proposing a heuristics for making such sensor control, which deals with both the probabilistic targets trajectory predictions and probabilistic coverage of sensors over the targets. In the final stage, both spatial and temporal optimization method have been successfully integrated and applied, demonstrating a complete and effective pipeline for spatiotemporal optimization of sensor networks