Complex Surface Displacements above the Storage Cavern Field at Epe, NW-Germany, Observed by Multi-Temporal SAR-Interferometry

The storage cavern field at Epe has been brined out of a salt deposit belonging to the lower Rhine salt flat, which extends under the surface of the North German lowlands and part of the Netherlands. Cavern convergence and operational pressure changes cause surface displacements that have been studied for this work with the help of SAR interferometry (InSAR) using distributed and persistent scatterers. Vertical and East-West movements have been determined based on Sentinel-1 data from ascending and descending orbit. Simple geophysical modeling is used to support InSAR processing and helps to interpret the observations. In particular, an approach is presented that allows to relate the deposit pressures with the observed surface displacements. Seasonal movements occurring over a fen situated over the western part of the storage site further complicate the analysis. Findings are validated with ground truth from levelling and groundwater level measurements

The Cloud-to-Thing Continuum

The Internet of Things offers massive societal and economic opportunities while at the same time significant challenges, not least the delivery and management of the technical infrastructure underpinning it, the deluge of data generated from it, ensuring privacy and security, and capturing value from it. This Open Access Pivot explores these challenges, presenting the state of the art and future directions for research but also frameworks for making sense of this complex area. This book provides a variety of perspectives on how technology innovations such as fog, edge and dew computing, 5G networks, and distributed intelligence are making us rethink conventional cloud computing to support the Internet of Things. Much of this book focuses on technical aspects of the Internet of Things, however, clear methodologies for mapping the business value of the Internet of Things are still missing. We provide a value mapping framework for the Internet of Things to address this gap. While there is much hype about the Internet of Things, we have yet to reach the tipping point. As such, this book provides a timely entrée for higher education educators, researchers and students, industry and policy makers on the technologies that promise to reshape how society interacts and operates

AI-based resource management in future mobile networks

Η υποστίριξη και ενίσχυση των δίκτυων 5ης γενιάς και πέρα από αλγόριθμους Τεχνητής Νοημοσύνης για την επίλυση προβλημάτων βελτιστοποίησης δικτύου, μελετάται πρόσφατα προκειμένου η νέα γενιά των δικτύων να ανταποκριθεί στις απαιτήσεις ποιότητας υπηρεσίας σχετικά με την κάλυψη, τη χωρητικότητα των χρηστών και το κόστος εγκατάστασης. Μία από τις βασικές ανάγκες είναι η βελτιστοποίηση στην διαδικασία της εγκατάστασης σταθμών βάσης δικτύου. Σε αυτή την εργασία προτείνεται μια μετα-ευριστική μέθοδος, με όνομα «Γενετικός Αλγόριθμός» (Genetic Algorithm) για την επίλυση προβλημάτων βελτιστοποίησης λαμβάνοντας υπόψη τους περιορισμούς ζήτησης. Ο κύριος στόχος είναι η παρουσίαση της εναλλακτικής αυτής λύσης, η οποία είναι η χρήση του Γενετικού Αλγόριθμου, για τη βελτιστοποίηση της διαδικασίας εγκατάστασης των σταθμών βάσης του δικτύου. Με την χρήση του αλγορίθμου για την εγκατάσταση σταθμών βάσης παρέχονται οι ίδιες υπηρεσίες με πριν και ελαχιστοποιείται την κατανάλωση ενέργειας της υποδομής του δικτύου, λαμβάνοντας υπόψιν ομοιογενή και ετερογενή σενάρια σταθμών βάσης. Οι προσομοιώσεις πραγματοποιήθηκαν σε γλώσσα προγραμματισμού Python και τα καλύτερα αποτελέσματα εγκατάστασης παρουσιάστηκαν και αποθηκεύτηκαν. Έγινε σύγκριση της εγκατάστασης αποκλειστικά μακρο-σταθμών βάσης με μικρότερου μεγέθους (σε κάλυψη) σταθμών βάσης πάνω από την υπάρχουσα. Με την χρήση των μικρότερων σταθμών βάσης, η εγκατάσταση του δικτύου θα επιτρέψει βελτιώσεις στην κάλυψη των χρηστών και θα μειώσει το κόστος, την κατανάλωση ενέργειας και τις παρεμβολές μεταξύ των κυψελών. Όλα τα σενάρια μελετήθηκαν σε 3 περιοχές με διαφορετική πυκνότητα χρηστών (A, B και C). Ως προς την ικανοποίηση των απαιτήσεων αναφορικά με την ποιότητα υπηρεσιών και των κινητών συσκευών, η ανάπτυξη μικρών σταθμών βάσης είναι επωφελής, συγκεκριμένα σε περιοχές hotspot.The 5G and beyond networks supported by Artificial Intelligence algorithms in solving network optimization problems are recently studied to meet the quality-of-service requirements regarding coverage, capacity, and cost. One of the essential necessities is the optimized deployment of network base stations. This work proposes the meta-heuristic algorithm Genetic Algorithm to solve optimization problems considering the demand constraints. The main goal is present the alternative solution, which is using the Genetic Algorithm to optimize BSs network deployment. This deployment provides the same services as existing deployments and minimizes the network infrastructure's energy consumption, including using homogenous and heterogenous scenarios of base stations. The simulations were performed in Python programming language, and the results as the best plans for each generation were presented and saved. A comparison of the macro base station deployment and small base station deployment was made on top of the existing one. By applying the small base stations, the network deployment will enable user coverage enhancements and reduce the deployment cost, energy consumption, and inter-cell interference. All the scenarios were assembled in user density area A, user density area B, and user density area C areas of interest. In meeting the requirements for QoS and UE, the small base station deployment is beneficial, namely in hotspot areas

Intelligent Computing: The Latest Advances, Challenges and Future

Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human-computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing. Intelligent computing is still in its infancy and an abundance of innovations in the theories, systems, and applications of intelligent computing are expected to occur soon. We present the first comprehensive survey of literature on intelligent computing, covering its theory fundamentals, the technological fusion of intelligence and computing, important applications, challenges, and future perspectives. We believe that this survey is highly timely and will provide a comprehensive reference and cast valuable insights into intelligent computing for academic and industrial researchers and practitioners

Design for novel enhanced weightless neural network and multi-classifier.

Weightless neural systems have often struggles in terms of speed, performances, and memory issues. There is also lack of sufficient interfacing of weightless neural systems to others systems. Addressing these issues motivates and forms the aims and objectives of this thesis. In addressing these issues, algorithms are formulated, classifiers, and multi-classifiers are designed, and hardware design of classifier are also reported. Specifically, the purpose of this thesis is to report on the algorithms and designs of weightless neural systems. A background material for the research is a weightless neural network known as Probabilistic Convergent Network (PCN). By introducing two new and different interfacing method, the word "Enhanced" is added to PCN thereby giving it the name Enhanced Probabilistic Convergent Network (EPCN). To solve the problem of speed and performances when large-class databases are employed in data analysis, multi-classifiers are designed whose composition vary depending on problem complexity. It also leads to the introduction of a novel gating function with application of EPCN as an intelligent combiner. For databases which are not very large, single classifiers suffices. Speed and ease of application in adverse condition were considered as improvement which has led to the design of EPCN in hardware. A novel hashing function is implemented and tested on hardware-based EPCN. Results obtained have indicated the utility of employing weightless neural systems. The results obtained also indicate significant new possible areas of application of weightless neural systems

The State-of-the-Art Survey on Optimization Methods for Cyber-physical Networks

Cyber-Physical Systems (CPS) are increasingly complex and frequently integrated into modern societies via critical infrastructure systems, products, and services. Consequently, there is a need for reliable functionality of these complex systems under various scenarios, from physical failures due to aging, through to cyber attacks. Indeed, the development of effective strategies to restore disrupted infrastructure systems continues to be a major challenge. Hitherto, there have been an increasing number of papers evaluating cyber-physical infrastructures, yet a comprehensive review focusing on mathematical modeling and different optimization methods is still lacking. Thus, this review paper appraises the literature on optimization techniques for CPS facing disruption, to synthesize key findings on the current methods in this domain. A total of 108 relevant research papers are reviewed following an extensive assessment of all major scientific databases. The main mathematical modeling practices and optimization methods are identified for both deterministic and stochastic formulations, categorizing them based on the solution approach (exact, heuristic, meta-heuristic), objective function, and network size. We also perform keyword clustering and bibliographic coupling analyses to summarize the current research trends. Future research needs in terms of the scalability of optimization algorithms are discussed. Overall, there is a need to shift towards more scalable optimization solution algorithms, empowered by data-driven methods and machine learning, to provide reliable decision-support systems for decision-makers and practitioners

Intelligent computing : the latest advances, challenges and future

Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing

Explainable IDS for DoS Attacks

RÉSUMÉ : L’internet des objets (Internet of Things, IoT) est un secteur d’activité en plein développe-ment. Cette technologie va permettre de faire communiquer entre eux di˙érents appareils qui pourront alors échanger un nombre important de données. Sécuriser les informations trans-mises est un requis important de l’IoT. Des mécanismes de sécurité utilisés dans les réseaux actuels peuvent être repris (chi˙rement, authentification, etc). Néanmoins, l’augmentation de la surface d’attaque nécessite de développer de nouveaux outils afin d’améliorer la sécurité de ce type de réseau.Le mécanisme étudié dans cette étude est le système de détection d’intrusions (Intrusion Detection System, IDS). Les systèmes de détection d’intrusions analysent un ensemble de données afin de détecter de potentielles intrusions. Le développement de l’apprentissage automatique a permis d’augmenter les performances de ces algorithmes. Néanmoins, les al-gorithmes d’apprentissage automatique sont souvent très diÿcilement interprétables par un humain. Des méthodes, nommées Explainable Artificial Intelligence (XAI), ont été dévelop-pées pour permettre une meilleure interprétation des résultats. La revue de littérature a montré que plusieurs méthodes pouvaient être utilisées afin de réaliser un système de détec-tion. Les contraintes des objets connectés nous ont orientés vers une approche de détection d’anomalie à l’aide de l’analyse de paquets réseau. L’étude de la littérature a mis en avant l’algorithme Suport Vector Machine dans la détection des intrusions et la méthode Partial Dependence Plot (PDP) pour l’interprétation des résultats. Nous proposons une approche combinant ces deux algorithmes dans l’objectif d’obtenir un système de détection d’intrusions performant et ayant une meilleure interprétabilité.----------ABSTRACT : The Internet of Things (IoT) is a rapidly developing sector of activity. This technology will enable di˙erent devices to communicate with each other and exchange a large amount of data. Securing the information transmitted is an important requirement of the IoT. Security mechanisms used in current networks can be used (encryption, authentication, etc.). Nevertheless, the increase of the attack surface requires the development of new tools to improve the security of this type of network.The mechanism studied in this study is the Intrusion Detection System (IDS). Intrusion detection systems analyse a set of information in order to detect potential intrusions. The development of automatic learning has made it possible to increase the performance of these algorithms. Nevertheless, machine learning algorithms are often very diÿcult for a human to interpret. Methods, called Explainable Artificial Intelligence (XAI), have been developed to allow a better interpretation of the results. The literature review showed that several methods could be used to build a detection system. The constraints of the connected objects led us to an anomaly detection approach using network packet analysis. The literature review highlighted the Support Vector Machine algorithm in intrusion detection and the Partial Dependence Plot (PDP) method for the interpretation of the results. We propose an approach combining these two algorithms with the objective of obtaining a high-performance intrusion detection system with better interpretability.The resulting mechanism has been the subject of 3 experiments: an analysis of the errors in the detection algorithm using the PDP method, a comparison with an algorithm attacking the IDS and an implementation in a network simulator

A Computational Framework for Axisymmetric Linear Elasticity and Parallel Iterative Solvers for Two-Phase Navier–Stokes

This dissertation explores ways to improve the computational efficiency of linear elasticity and the variable density/viscosity Navier--Stokes equations. While the approaches explored for these two problems are much different in nature, the end goal is the same - to reduce the computational effort required to form reliable numerical approximations.\\ The first topic considered is the axisymmetric linear elasticity problem. While the linear elasticity problem has been studied extensively in the finite-element literature, to the author\u27s knowledge, this is the first study of the elasticity problem in an axisymmetric setting. Indeed, the axisymmetric nature of the problem means that a change of variables to cylindrical coordinates reduces a three-dimensional problem into a decoupled one-dimensional and two-dimensional problem. The change of variables to cylindrical coordinates, however, affects the functional form of the divergence operator and the definition of the inner products. To develop a computational framework for the linear elasticity problem in this context, a new projection operator is defined that is tailored to the cylindrical form of the divergence and inner products. Using this framework, a stable finite-element quadruple is derived for $k=1,2$. These computational rates are then validated with a few computational examples.\\ The second topic addressed in this work is the development of a new Schur complement approach for preconditioning the two-phase Navier--Stokes equations. Considerable research effort has been invested in the development of Schur complement preconditioning techniques for the Navier--Stokes equations, with the pressure-convection diffusion (PCD) operator and the least-squares commutator being among the most popular. Furthermore, more recently researchers have begun examining preconditioning strategies for variable density / viscosity Stokes and Navier--Stokes equations. This work contributes to recent work that has extended the PCD Schur complement approach for single phase flow to the variable phase case. Specifically, this work studies the effectiveness of a new two-phase PCD operator when applied to dynamic two-phase simulations that use the two-phase Navier--Stokes equations. To demonstrate the new two-phase PCD operators effectiveness, results are presented for standard benchmark problems, as well as parallel scaling results are presented for large-scale dynamic simulations for three-dimensional problems