Στη παρούσα διπλωματική εργασία ασχολούμαστε με την ανάπτυξη τεχνικών για υλοποίηση ενός κατανεμημένου φίλτρου Kalman σε ένα Δίκτυο Αισθητήρων (WSN). Τα δίκτυα αυτά έχουν γνωρίσει τα τελευταία χρόνια ραγδαία ανάπτυξη λόγω των εξαιρετικά πολλών εφαρμογών τους σε διάφορα πεδία της ανθρώπινης δραστηριότητας. Το πρόβλημα που πραγματευόμαστε εδώ είναι η προσπάθεια εκτίμησης της κατάστασης μιας στοχαστικής διαδικασίας που επιτηρείται-παρακολουθείται απο το δίκτυο. Οι κόμβοι-αισθητήρες που συναπαρτίζουν αυτά τα δίκτυα έχουν συνήθως περιορίσμενες δυνατότητες ¨αίσθησης¨, πράγμα που σημαίνει ότι κάθε μεμονωμένος κόμβος αδυνατεί να παράξει μια καλή εκτίμηση της κατάστασης. Διάφορες τεχνικές εχούν προταθεί για την επίλυση τέτοιου είδους προβλημάτων όπως το κεντρικό Φίλτρο Kalman ή διάφορες αποκεντρωμένες προσεγγίσεις, οι οποιές είχαν όμως υψηλό υπολογιστικό κόστος και τις καθιστούσαν πρακτικά μη υλοποιήσιμες, ειδικά για δίκτυα με μεγάλο αριθμό κόμβων. Έτσι στη παρούσα εργασία , χρησιμοποιώντας ως εργαλείο την θεώρια των αλγορίθμων ¨Κοινής Συμφωνίας¨ (Consensus Algorithms) , κατασκευάζουμε Αλγορίθμους χαμήλης πολυπλοκότητας, για την υλοποίηση ενός Kατανεμημένου Φίλτρου Kalman, θεωρώντας ότι όλοι οι κόμβοι είναι ομότιμοι (peer-to-peer αρχιτεκτονικές), κάθε κόμβος επικοινωνεί μόνο με τους γειτονικούς του και δεν υπάρχουν Fusion Centers. Παρουσιάζουμε έτσι τρεις διαφορετικούς επαναληπτικούς αλγορίθμους βασιζομένων σε δύο διαφορετικές λογικές και τέλος προσομοιώνουμε και αξιολογούμε την επίδοση καθενός από αυτούς. / In this thesis we deal with the development of techniques for implementing a distributed Kalman filter in a sensor network (WSN). Ιn recent years these networks have experienced rapid growth, due to the numerous applications in various fields of human activity. The problem we discuss here is the attempt to estimate the state of a stochastic process which is monitored by nodes-sensors. The nodes that constitute these networks usually have limited “sense” capabilities, which means that each node is unable to produce a good estimate of the state, using only its own measurements. Various techniques have been proposed to solve this kind of problems, such as the Central Kalman Filter or several decentralized approaches, which did have a high computational cost that renders them impractical, especially for networks with a high number of nodes. Thus, in the present work, , we construct low complexity algorithms for the implementation of a Distirbuted Kalman Filter, using as a tool the theory of Consensus Algorithms and assuming that all nodes are peers (peer-to-peer architectures), each node communicates only with its neighboring and no Fusion Centers exist. Τhus, we present three different iterative algorithms based on two different approaches and finally simulate and evaluate the performance of each of them.

This thesis addresses the problem of positional consensus of multi-agent systems. A positional consensus is achieved when the agents converge to a point. Some applications of this class of problem is in mid-air refueling of the aircraft or UAVs, targeting a geographical location, etc. In this research work some positional consensus algorithms have been developed. They can be categorized in two part (i) Broadcast control based algorithm (ii) Distributed control based algorithm. In case of broadcast based algorithm control strategies for a group of agents is developed to achieve positional consensus. The problem is constrained by the requirement that every agent must be given the same control input through a broadcast communication mechanism. Although the control command is computed using state information in a global framework, the control input is implemented by the agents in a local coordinate frame. The mathematical formulation has been done in a linear programming framework that is computationally less intensive than earlier proposed methods. Moreover, a random perturbation input in the control command, that helps to achieve reasonable proximity among agents even for a large number of agents, which was not possible with the existing strategy in the literature, is introduced. This method is extended to achieve positional consensus at a pre-specified location. A comparison between the LP approach and the existing SOCP based approach is also presented. Some of the algorithm has been demonstrated successfully on a robotic platform made from LEGO Mindstorms NXT Robots. In the second case of broadcast based algorithm, a decentralized algorithm for a group of multiple autonomous agents to achieve positional consensus has been developed using the broadcast concept. Even here, the mathematical formulation has done using a linear programming framework. Each agent has some sensing radius and it is capable of sensing position and orientation with other agents within their sensing region. The method is computationally feasible and easy to implement. In case of distributed algorithms, a computationally efficient distributed rendezvous algorithm for a group of autonomous agents has been developed. The algorithm uses a rectilinear decision domain (RDD), as against the circular decision domain assumed in earlier work available in the literature. This helps in reducing its computational complexity considerably. An extensive mathematical analysis has been carried out to prove the convergence of the algorithm. The algorithm has also been demonstrated successfully on a robotic platform made from LEGO Mindstorms NXT Robots.

<div>Distributed estimation algorithms have received considerable attention lately, owing to the advancements in computing, communication and battery technologies. They offer increased scalability, robustness and efficiency. In applications such as formation flight, where any discrepancies between sensor estimates has severe consequences, it becomes crucial to require consensus of estimates amongst all sensors. The Kalman Consensus Filter (KCF) is a seminal work in the field of distributed consensus-based estimation, which accomplishes this. </div><div><br></div><div>However, the KCF algorithm is mathematically sub-optimal, and does not account for the cross-correlation between the estimates of sensors. Other popular algorithms, such as the Information weighted Consensus Filter (ICF) rely on ad-hoc definitions and approximations, rendering them sub-optimal as well. Another major drawback of KCF is that it utilizes unweighted consensus, i.e., each sensor assigns equal weightage to the estimates of its neighbors. This fact has been shown to cause severely degraded performance of KCF when some sensors cannot observe the target, and can even cause the algorithm to be unstable.</div><div><br></div><div>In this work, we develop a novel algorithm, which we call Optimal Kalman Consensus Filter for Weighted Directed Graphs (OKCF-WDG), which addresses both of these limitations of existing algorithms. OKCF-WDG integrates the KCF formulation with that of matrix-weighted consensus. The algorithm achieves consensus on a weighted digraph, enabling a directed flow of information within the network. This aspect of the algorithm is shown to offer significant performance improvements over KCF, as the information may be directed from well-performing sensors to other sensors which have high estimation error due to environmental factors or sensor limitations. We validate the algorithm through simulations and compare it to existing algorithms. It is shown that the proposed algorithm outperforms existing algorithms by a considerable margin, especially in the case where some sensors are naive (i.e., cannot observe the target).</div>

Distributed systems offer a reliable and scalable solution for tackling massive and complex tasks that cannot be handled by a single computer. However, standard consensus protocols used in such systems often replicate data without considering the workload, leading to unnecessary retransmissions. This thesis proposes using machine learning (ML) to optimize consensus protocols and make them adaptable to recurring workloads. It introduces a cache that encodes frequently-transmitted data between nodes to reduce network traffic. To implement this, the thesis builds a caching layer at all nodes using the decided logs, which represent a consistent view of the application history. The cache can encode and decode incoming log entries to reduce the average message size and improve throughput under limited network bandwidth. The thesis selects an ML-based model that combines various caching policies and adapts to changing access patterns in the workload. Experimental results show that this approach can improve throughput up to 250%, assuming negligible preprocessing overhead. / Distribuerade system erbjuder en pålitlig och skalbar lösning för att hantera massiva och komplexa uppgifter som inte kan hanteras av en enskild dator. Konventionella konsensusprotokoll som används i dessa system replikerar emellertid ofta data utan att ta hänsyn till arbetsbelastningen, vilket leder till överflödig dataöverföring. Denna avhandling föreslår att använda maskinin lärning (ML) för att optimera konsensusprotokoll och göra dem anpassade till återkommande mönster i arbetsbelastningen. Den introducerar en cache som kodar och komprimerar data som ofta överförs mellan noder för att minska nätverkstrafiken. För att implementera detta byggs ett cache baserat på den bestämda loggen på alla noder, som representerar en konsekvent syn på programhistoriken. Cachen kan koda inkommande data för att minska genomsnittlig meddelandestorlek och förbättra genomströmning under begränsad nätverksbandbredd. En ML-baserad modell som kombinerar olika cachningpolicyer och anpassar sig till ändrade åtkomstmönster i arbetsbelastningen används. Experimentella resultat visar att denna metod kan förbättra genomströmningen med 250%, under förutsättning att förbearbetningsöverhuvudet är försumbart.

The Internet of Things (IoT) market is growing by the day and there are no signs of stagnation. As the market grows, it becomes all the more important to address security concerns. A major security issue of IoT is that the devices usually send their collected data to a centralized entity, creating a single point of failure. A solution to this is decentralization. Blockchain technology offers not only decentralization, but also immutability and data integrity. In blockchain, it is the consensus algorithm that is used to coordinate the devices and achieve unanimity within the network. These consensus algorithms are generally computationally expensive and are typically not compatible with IoT devices, due to the limited resources of the devices and the need to send data in real-time. This study implemented and compared two versions of the consensus algorithms Proof of Authentication, which are designed specifically for IoT devices. The results show that one algorithm is considerably faster than the other, however, a fair comparison could not be made due to unforeseeable difficulties with the Industrial IoT(IIoT) device used in this study. This study concluded that algorithm 1 is most likely a viable choice as a consensus algorithm for IIoT networks. No conclusion could be drawn for algorithm 2 due to the unsatisfactory implementation because of the limitations of the devices used in this study. / Sakernas internet (IoT)-marknaden växer dagligen och det finns inga tecken på stagnation. När marknaden växer blir det desto viktigare att hantera säkerhetsproblem. En viktig säkerhetsfråga för IoT är att enheterna vanligtvis skickar sin insamlade data till en central enhet, vilket skapar en enda svag länk. En lösning på detta är decentralisering. Blockchain-tekniken erbjuder inte bara decentralisering utan också oföränderlighet och dataintegritet. I blockchain är det konsensusalgoritmen som används för att samordna enheterna i nätverket och uppnå enhällighet. Dessa algoritmer är i allmänhet beräkningsmässigt dyra och är vanligtvis inte kompatibla med IoT-enheter på grund av enheternas begränsade resurser och behov av att skicka data i realtid. Denna studie implementerade och jämförde två konsensusalgoritmer utformade speciellt för IoT-enheter. Resultaten visar att ena algoritmen är betydligt snabbare än den andra, men en rättvis jämförelse kunde inte göras på grund av oförutsägbara svårigheter med Industriella IoT (IIoT)-enheten som användes i denna studie. Denna studie drog slutsatsen att algoritm 1 sannolikt är ett genomförbart val som en konsensusalgoritm för IIoT-nätverk. Ingen slutsats kunde dras för algoritm 2 på grund av det otillfredsställande genomförandet på grund av begränsningarna för enheterna som används i denna studie.

The introduction of Physical Unclonable Functions (PUFs) and lightweight consensus algorithms to aid in the bolstering of security and privacy in both IoT and IoE does show a great deal of promise not only in these areas, but in resource cost over traditional methods of blockchain.  However, several previous studies make claims regarding performance of novel solutions without providing detailed information as to the physical components of their experiments.  This comparative study shows that Proof of Authentication (PoAh) performs the best out of three selected consensus algorithms and that the claims made regarding the performance of PUFChain and Proof of PUF-enabled Authentication (PoP) could not be replicated in this instance.

De nos jours, les applications utilisant des quadrirotors autonomes sont en plein essor. La surveillance et la sécurité de sites industriels ou sensibles, de zones géographiques pour l’agriculture par exemple sont quelques-unes des applications les plus célèbres des véhicules aériens sans pilote (UAV). Actuellement, certains chercheurs et scientifiques se concentrent sur le déploiement multi-drones pour l’inspection et la surveillance de vastes zones. L’objectif de cette thèse est de concevoir des algorithmes afin de réaliser une commande de vol en formation distribuée/décentralisée de multi-UAVs en temps réel dans une perspective de systèmes de systèmes. Tout d’abord, nous avons passé en revue certains travaux récents de la littérature sur la commande de vol en formation et la commande de quadrirotors. Nous avons présenté une brève introduction sur les systèmes de systèmes, leur définition et leurs caractéristiques. Ensuite, nous avons introduit la commande de vol en formation avec ses structures les plus utilisées dans la littérature. Nous avons alors présenté quelques travaux existants traitant du flocking (comportement de regroupement en flotte), les méthodes de modélisation les plus utilisés pour les quadrirotors et quelques approches de commande les plus utilisées pour stabiliser des quadrirotors. Deuxièmement, nous avons utilisé la structure de la commande comportementale pour réaliser un vol en formation de plusieurs UAVs. Nous avons conçu un comportement pour réaliser le vol en formation de multi-UAVs sans fragmentation. Le comportement proposé traite le problème flocking dans une perspective globale, c’est-à-dire, nous avons inclus une tendance dans chaque drone pour former une formation. Les défis des Systèmes de systèmes nous a motivés à chercher des algorithmes de flocking et de consensus introduits dans la littérature qui peuvent être utiles pour répondre à ces défis. Cela nous a amenés à proposer quatre lois de commande en visant à être compatibles avec le modèle non linéaire des quadrirotors et pouvant être expérimentés sur des plates-formes réelles. Les lois de commande ont été exécutées à bord de chaque quadrirotor dans la formation et chaque quadrirotor interagit avec ses voisins pour assurer un vol en formation sans collision. Enfin, nous avons validé nos lois de commande par des simulations et des expériences en temps réel. Pour la simulation, nous avons utilisé un simulateur de multi quadrirotors développé au laboratoire Heudiasyc. Pour les expériences, nous avons mis en œuvre nos lois de contrôle sur des quadrirotors ArDrone2 évolués dans un environnement intérieur équipé d’un système de capture de mouvement (Optitrack). / Nowadays, applications of autonomous quadrotors are increasing rapidly. Surveillance and security of industrial sites, geographical zones for agriculture for example are some popular applications of Unmanned Aerial Vehicles (UAVs). Nowadays, researchers and scientists focus on the deployment of multi-UAVs for the inspection and the surveillance of large areas. The objective of this thesis is to design algorithms and techniques to perform a real-time distributed/decentralized multi-UAVs flight formation control, from a system of systems perspective. Firstly, we reviewed recent works of the literature about flight formation control and the control of quadrotors. We presented a brief introduction about systems of systems, their definition and characteristics. Then, we introduced the flight formation control with its most used structures in the literature, some existing works dealing with flocking. Finally, we presented the most used modeling methodologies for quadrotors and some control approaches that are used to stabilize quadrotors. Secondly, we used the behavioral-based control structure to achieve a multiple UAV flocking. We conceived a behavior intending to address the control design towards a successful achievement of the flocking task without fragmentation. The proposed behavior treats the flocking problem from a global perspective, that is, we included a tendency of separated UAVs to form a flock.System of systems challenges motivated us to look for flocking and consensus algorithms introduced in the literature that could be helpful to answer to these challenges. This led us to propose four flocking control laws aiming at being compatible with the nonlinear model of quadrotors and at being implemented on experimental platforms. The control laws were run aboard each quadrotor in the flock. By running the control law, each quadrotor interacts with its neighbors to ensure a collision-free flocking. Finally, we validated our proposed control laws by simulations and real-time experiments. For the simulation, we used a PC-based simulator of flock of multiple quadrotors which was developed at Heudiasyc laboratory. For experiments, we implemented our control laws on ArDrone2 quadrotors evolved in an indoor environment equipped with an Optitrack motion capture system.

Ce travail s’inscrit dans le cadre de la commande d’un système multi agents/ multi véhicules. Cette thèse traite en particulier le cas de la commande d’un système multi-robots mobiles non-holonomes. L'objectif est de concevoir des lois de commandes appropriées pour chaque robot de sorte que l’ensemble des robots puisse exécuter des tâches spécifiques, de suivre des trajectoires désirées tout en maintenant des configurations géométriques souhaitées. L’approche leadeur-suiveur pour la commande d’un groupe de robots mobiles non-holonomes est étudiée en intégrant la technologie backstepping, avec une approche basée sur les neurodynamiques bioinspirées. Le problème de commande distribuée d’un système multi robots sur le consensus est également étudié. Des lois de commandes cinématiques distribuées sont développés afin de garantir au système multi-robots la convergence exponentielle vers une configuration géométrique souhaitée. Afin de tenir compte de la dynamique des paramètres inconnus, des commandes adaptatives de couple sont développés pour que le système multi-robots puisse converger asymptotiquement vers le modèle géométrique souhaité. Lorsque la dynamique est inconnue, des commandes à base de réseaux de neurones sont proposées / This work is based on the multi-agent system / multi-vehicles. This thesis especially focuses on formation control of multiple nonholonomic mobile robots. The objective is to design suitable controllers for each robot according to different control tasks and different constraint conditions, such that a group of mobile robots can form and maintain a desired geomantic pattern and follow a desired trajectory. The leader-follower formation control for multiple nonholonomic mobile robots is investigated under the backstepping technology, and we incorporate a bioinspired neurodynamics scheme in the robot controllers, which can solve the impractical velocity jumps problem. The distributed formation control problem using consensus-based approach is also investigated. Distributed kinematic controllers are developed, which guarantee that the multi-robots can at least exponentially converge to the desired geometric pattern under the assumption of "perfect velocity tracking". However, in practice, "perfect velocity tracking" doesn’t hold and the dynamics of robots should not be ignored. Next, in consideration of the dynamics of robot with unknown parameters, adaptive torque controllers are developed such that the multi-robots can asymptotically converge to the desired geometric pattern under the proposed distributed kinematic controllers. Furthermore, When the partial knowledge of dynamics is available, an asymptotically stable torque controller has been proposed by using robust adaptive control techniques. When the dynamics of robot is unknown, the neural network controllers with the robust adaptive term are proposed to guarantee robust velocity tracking

This thesis describes basis of blockchain technology implementation for SAP Cloud platform with emphasis to security and safety of critical data which are stored in blockchain. This diploma thesis implements letter of credit to see and control business process administration. It also compares all the possible technology modification. Thesis describes all elementary parts of software which are necessary to implement while storing data and secure integrity. This thesis also leverages ideal configuration of each programable block in implementation. Alternative configurations of possible solutions are described with pros and cons as well. Another part of diploma thesis is actual working implementation as a proof of concept to cover letter of credit. All parts of code are design to be stand alone to provide working concept for possible implementation and can source as a help to write productive code. User using this concept will be able to see whole process and create new statutes for whole letter of credit business process.

Blockchain is a decentralized database that is distributed among a computer network. To enable a smooth decision making process without any authority, different blockchain applications use their own consensus algorithms. The problem is that for a new blockchain application, there is limited aid in deciding which algorithm it should implement. Selecting consensus algorithms is crucial because reaching consensus is the fundamental issue of a decentralized system. Different algorithms are designed with their own advantages and limitations, making it complex to navigate one’s way through a list of consensus algorithms. This thesis attempts to contribute to solving this problem by surveying 15 existing cryptocurrencies’ consensus algorithms used in their blockchain application and then producing a decision tree as the aid for algorithm selection. The top 5 algorithms from each category in Proof of Work (PoW), Proof of Stake (PoS), and Hybrid Proof of Work + Proof of Stake (PoW + PoS) are selected. The research method is qualitative. The study shows that different consensus algorithms often share some properties, but they are usually built to solve the issues of another algorithm, which means they also have their own distinctive advantages. Therefore, the decision tree reveals how these algorithms are logically connected and the key properties blockchain consensus algorithms possess. Based on the result of this thesis, further research can be conducted to include more algorithms in order to make the decision tree more comprehensive. Implementations of these algorithms in similar network setup can also be done to experiment with their claimed properties. The decision tree can be sent to industry for further feedback. / Blockchain är en decentraliserad databas som distribueras i ett datornätverk. För att möjliggöra en smidig beslutsprocess utan någon auktoritet använder olika blockkedjeapplikationer sina egna konsensusalgoritmer. Problemet är att för en ny blockchain-applikation finns det begränsad hjälp för att bestämma vilken algoritm den ska implementera. Att välja konsensusalgoritmer är avgörande eftersom att nå konsensus är den grundläggande frågan för ett decentraliserat system. Olika algoritmer är designade med sina egna fördelar och begränsningar, vilket gör det komplicerat att navigera sig igenom en lista med konsensusalgoritmer. Forskningsmetoden är kvalitativ. Det här dokumentet försöker bidra till att lösa detta problem genom att kartlägga 15 befintliga kryptovalutors konsensusalgoritmer som används i deras blockkedjeapplikation och sedan ta fram ett beslutsträd som hjälp för val av algoritmer. De 5 bästa algoritmerna från varje kategori i Proof of Work (PoW), Proof of Stake (PoS) och Hybrid Proof of Work + Proof of Stake (PoW + PoS) väljs. Studien visar att olika konsensusalgoritmer ofta delar vissa egenskaper, men de är vanligtvis byggda för att lösa problem med en annan algoritm, vilket innebär att de också har sina egna distinkta fördelar. Därför avslöjar beslutsträdet hur dessa algoritmer är logiskt kopplade och de nyckelegenskaper som blockchain konsensusalgoritmer besitter. Baserat på resultatet av denna artikel kan ytterligare forskning utföras för att inkludera fler algoritmer för att göra beslutsträdet mer heltäckande. Implementeringar av dessa algoritmer i liknande nätverksuppsättningar kan också göras för att experimentera med deras påstådda egenskaper. Beslutsträdet kan skickas till industrin för vidare feedback.