Enabling emergent configurations in the industrial internet of things for oil and gas explorations : a survey

Abstract: Several heterogeneous, intelligent, and distributed devices can be connected to interact with one another over the Internet in what is termed internet of things (IoT). Also, the concept of IoT can be exploited in the industrial environment for enhancing the production of goods and services and for mitigating the risk of disaster occurrences. This application of IoT for enhancing industrial production is known as industrial IoT (IIoT). Emergent configuration (EC) is a technology that can be adopted to enhance the operation and collaboration of IoT connected devices in order to improve the efficiency of the connected IoT systems for maximum user satisfaction. To meet user goals, the connected devices are required to cooperate with one another in an adaptive, interoperable, and homogeneous manner. In this paper, a survey of the concept of IoT is presented in addition to a review of IIoT systems. The application of ubiquitous computing-aided software define networking (SDN)-based EC architecture is propounded for enhancing the throughput of oil and gas production in the maritime ecosystems by managing the exploration process especially in emergency situations that involve anthropogenic oil and gas spillages

Distributed Inference and Learning with Byzantine Data

We are living in an increasingly networked world with sensing networks of varying shapes and sizes: the network often comprises of several tiny devices (or nodes) communicating with each other via different topologies. To make the problem even more complicated, the nodes in the network can be unreliable due to a variety of reasons: noise, faults and attacks, thus, providing corrupted data. Although the area of statistical inference has been an active area of research in the past, distributed learning and inference in a networked setup with potentially unreliable components has only gained attention recently. The emergence of big and dirty data era demands new distributed learning and inference solutions to tackle the problem of inference with corrupted data. Distributed inference networks (DINs) consist of a group of networked entities which acquire observations regarding a phenomenon of interest (POI), collaborate with other entities in the network by sharing their inference via different topologies to make a global inference. The central goal of this thesis is to analyze the effect of corrupted (or falsified) data on the inference performance of DINs and design robust strategies to ensure reliable overall performance for several practical network architectures. Specifically, the inference (or learning) process can be that of detection or estimation or classification, and the topology of the system can be parallel, hierarchical or fully decentralized (peer to peer). Note that, the corrupted data model may seem similar to the scenario where local decisions are transmitted over a Binary Symmetric Channel (BSC) with a certain cross over probability, however, there are fundamental differences. Over the last three decades, research community has extensively studied the impact of transmission channels or faults on the distributed detection system and related problems due to its importance in several applications. However, corrupted (Byzantine) data models considered in this thesis, are philosophically different from the BSC or the faulty sensor cases. Byzantines are intentional and intelligent, therefore, they can optimize over the data corruption parameters. Thus, in contrast to channel aware detection, both the FC and the Byzantines can optimize their utility by choosing their actions based on the knowledge of their opponent’s behavior. Study of these practically motivated scenarios in the presence of Byzantines is of utmost importance, and is missing from the channel aware detection and fault tolerant detection literature. This thesis advances the distributed inference literature by providing fundamental limits of distributed inference with Byzantine data and provides optimal counter-measures (using the insights provided by these fundamental limits) from a network designer’s perspective. Note that, the analysis of problems related to strategical interaction between Byzantines and network designed is very challenging (NP-hard is many cases). However, we show that by utilizing the properties of the network architecture, efficient solutions can be obtained. Specifically, we found that several problems related to the design of optimal counter-measures in the inference context are, in fact, special cases of these NP-hard problems which can be solved in polynomial time. First, we consider the problem of distributed Bayesian detection in the presence of data falsification (or Byzantine) attacks in the parallel topology. Byzantines considered in this thesis are those nodes that are compromised and reprogrammed by an adversary to transmit false information to a centralized fusion center (FC) to degrade detection performance. We show that above a certain fraction of Byzantine attackers in the network, the detection scheme becomes completely incapable (or blind) of utilizing the sensor data for detection. When the fraction of Byzantines is not sufficient to blind the FC, we also provide closed form expressions for the optimal attacking strategies for the Byzantines that most degrade the detection performance. Optimal attacking strategies in certain cases have the minimax property and, therefore, the knowledge of these strategies has practical significance and can be used to implement a robust detector at the FC. In several practical situations, parallel topology cannot be implemented due to limiting factors, such as, the FC being outside the communication range of the nodes and limited energy budget of the nodes. In such scenarios, a multi-hop network is employed, where nodes are organized hierarchically into multiple levels (tree networks). Next, we study the problem of distributed inference in tree topologies in the presence of Byzantines under several practical scenarios. We analytically characterize the effect of Byzantines on the inference performance of the system. We also look at the possible counter-measures from the FC’s perspective to protect the network from these Byzantines. These counter-measures are of two kinds: Byzantine identification schemes and Byzantine tolerant schemes. Using learning based techniques, Byzantine identification schemes are designed that learn the identity of Byzantines in the network and use this information to improve system performance. For scenarios where this is not possible, Byzantine tolerant schemes, which use game theory and error-correcting codes, are developed that tolerate the effect of Byzantines while maintaining a reasonably good inference performance in the network. Going a step further, we also consider scenarios where a centralized FC is not available. In such scenarios, a solution is to employ detection approaches which are based on fully distributed consensus algorithms, where all of the nodes exchange information only with their neighbors. For such networks, we analytically characterize the negative effect of Byzantines on the steady-state and transient detection performance of conventional consensus-based detection schemes. To avoid performance deterioration, we propose a distributed weighted average consensus algorithm that is robust to Byzantine attacks. Next, we exploit the statistical distribution of the nodes’ data to devise techniques for mitigating the influence of data falsifying Byzantines on the distributed detection system. Since some parameters of the statistical distribution of the nodes’ data might not be known a priori, we propose learning based techniques to enable an adaptive design of the local fusion or update rules. The above considerations highlight the negative effect of the corrupted data on the inference performance. However, it is possible for a system designer to utilize the corrupted data for network’s benefit. Finally, we consider the problem of detecting a high dimensional signal based on compressed measurements with secrecy guarantees. We consider a scenario where the network operates in the presence of an eavesdropper who wants to discover the state of the nature being monitored by the system. To keep the data secret from the eavesdropper, we propose to use cooperating trustworthy nodes that assist the FC by injecting corrupted data in the system to deceive the eavesdropper. We also design the system by determining the optimal values of parameters which maximize the detection performance at the FC while ensuring perfect secrecy at the eavesdropper

A new approach for electric modeling of vertical axis wind turbine rotors in view to the construction of a global electric model for the wind energy conversion system / Nouvelle approche de modélisation électrique des rotors d’éoliennes à axe vertical en vue de la construction d’un modèle électrique global de la chaine de conversion éolienne

The combine effects of climate changes, the continue and considerable increase of energy demand in the world, the instability of fossil fuel prices as well as the energetic dependence of some countries who are not fuel producers leads to important development of renewable energies. More again, the energy bill in its dizzying rise causes the prices increase for raw materials, goods and even basic necessities. It is therefore imperative to go in for diversification of energy sources on the one hand and means to produce energy at a relatively low cost on the other hand. This thesis proposes a novel approach for aerodynamic modelling of Darrieus type vertical axes wind turbines (VAWTs). Indeed, it is a contribution to the build-up of a global electric model for vertical axis wind turbine conversion system with a view to improve monitoring, diagnosis and maintenance. The goal of this research work is double: firstly, to propose an innovative approach for aerodynamic modelling of VAWTs and secondly, based on this approach, to build a new equivalent electric model for the study, analysis and aerodynamic optimisation of Darrieus type VAWTs. More broadly, this work aims to contribute to the development of new tools, techniques and methods to improve the design, optimization, diagnosis, prognosis and monitoring of Darrieus VAWTs to better ensure their maintenance. The purpose is to provide the scientific community and the wind turbine industry with new tools that can ameliorate the reliability and thus increase performance and efficiency of Darrieus type VAWTs. An equivalent electric circuit model for three-blade DT-VAWT rotors was builded. The new model that we named the Tchakoua model is based on a recently developed approach for modelling DT-VAWT rotors using the equivalent electrical circuit analogy. The proposed model was built from the mechanical description given by the Paraschivoiu double-multiple streamtube model and was based on an analogy between mechanical and electrical circuits. Thus, the rotating blades and the blades’ mechanical coupling to the shaft are modelled using the mechanical-electrical analogy, and the wind flow is modelled as a source of electric current. Model simulations were conducted using MATLAB for a three-bladed rotor architecture, characterized by a NACA0012 profile, an average Reynolds number of 40,000 for the blade and a tip speed ratio of 5. The results obtained show strong agreement with findings from both aerodynamic and computational fluid dynamics (CFD) models in the literature. Les effets conjugués du réchauffement climatique, l’augmentation considérable et continue de la demande énergétique mondiale, la volatilité des prix des énergies fossiles, la dépendance énergétique de certains pays non producteurs de pétrole, font que les énergies renouvelables bénéficient actuellement d’un essor très important. De plus, la facture énergétique dans sa hausse vertigineuse entraîne les matières premières, les biens de consommation et même les produits de première nécessité. Il est donc impératif de réfléchir à la diversification des sources d’énergie d’une part et aux moyens de produire de l’énergie à un coup relativement bas d’autre part. Cette thèse propose un modèle électrique équivalent pour les rotors d’éoliennes à axes vertical. Elle constitue une contribution à la construction d’un modèle électrique global de la chaîne de conversion éolienne. L’objectif de cette recherche est d’une part de proposer une nouvelle approche de modélisation des éoliennes à axe vertical; et d’autre part d’élaborer, sur la base de cette approche, un nouveau modèle pour l’étude, l’analyse et l’optimisation aérodynamique des éoliennes à axe vertical de type Darrieus. De façon plus large, ce travail vise à contribuer au développement de nouveaux outils, techniques et méthodes devant permettre d’améliorer la conception, l’optimisation, le diagnostic, le pronostic et la surveillance des éoliennes à axe vertical de type Darrieus afin de mieux assurer leur maintenance. La finalité étant de mettre à la disposition de la communauté scientifique et de l’industrie de l’éolienne de nouveaux outils pouvant permettre d’accroître la performance aérodynamique ainsi que le rendement énergétique des éoliennes à axe vertical de type Darrieus. Un modèle électrique équivaut pour rotor d’éolienne de type Darrius tri-pales a été développé. Ce nouveau modèle que nous avons nommé le ‘Tchakoua model’ est basé sur une nouvelle approche de modélisation des rotors d’éoliennes à axe vertical utilisant des circuits électriques équivalents. Le modèle a été construit sur la base de description à double disque actuateur à multiple tubes de vent faite par Paraschivoiu et est basé sur l’analogie circuits mécaniques et circuits électriques appliquée à l’aérodynamique. Ainsi, les pales et le couplage mécanique à l’arbre lent sont modélisées en utilisant l’analogie mécanique électrique tandis que l’écoulement de vent est modélisé comme source de courant. Les simulations ont été effectuées avec le logiciel MATLAB pour un rotor tripal caractérisé par un profil NACA0012, un nombre de Reynold moyen de 40000 et une vitesse spécifique de 5. Une validation croisée de ces résultats avec les données issues des modèles aérodynamiques ou même des modèles numériques de dynamique des fluides montre de bonnes concordances