On the Extensibility of Formal Methods Tools

Modern software systems often have long lifespans over which they must continually evolve to meet new, and sometimes unforeseen, requirements. One way to effectively deal with this is by developing the system as a series of extensions. As requirements change, the system evolves through the addition of new extensions and, potentially, the removal of existing extensions. In order for this kind of development process to thrive, it is necessary that the system have a high level of extensibility. Extensibility is the capability of a system to support the gradual addition of new, unplanned functionalities. This dissertation investigates extensibility of software systems and focuses on a particular class of software: formal methods tools. The approach is broad in scope. Extensibility of systems is addressed in terms of design, analysis and improvement, which are carried out in terms of source code and software architecture. For additional perspective, extensibility is also considered in the context of formal modelling. The work carried out in this dissertation led to the development of various extensions to the Overture tool supporting the Vienna Development Method, including a new proof obligation generator and integration with theorem provers. Additionally, the extensibility of Overture itself was also improved and it now better supports the development and integration of various kinds of extensions. Finally, extensibility techniques have been applied to formal modelling, leading to an extensible architectural style for formal models

Power extraction circuits for piezoelectric energy harvesters and time series data in water supply systems

This thesis investigates two fundamental technological challenges that prevent water utilities from deploying infrastructure monitoring apparatus with high spatial and temporal resolution: providing sufficient power for sensor nodes by increasing the power output from a vibration-driven energy harvester based on piezoelectric transduction, and the processing and storage of large volumes of data resulting from the increased level of pressure and flow rate monitoring. Piezoelectric energy harvesting from flow-induced vibrations within a water main represents a potential source of power to supply a sensor node capable of taking high- frequency measurements. A main factor limiting the amount of power from a piezoelectric device is the damping force that can be achieved. Electronic interface circuits can modify this damping in order to increase the power output to a reasonable level. A unified analytical framework was developed to compare circuits able to do this in terms of their power output. A new circuit is presented that out-performs existing circuits by a factor of 2, which is verified experimentally. The second problem concerns the management of large data sets arising from resolving challenges with the provision of power to sensor devices. The ability to process large data volumes is limited by the throughput of storage devices. For scientists to execute queries in a timely manner, query execution must be performant. The large volume of data that must be gathered to extract information from historic trends mandates a scalable approach. A scalable, durable storage and query execution framework is presented that is able to significantly improve the execution time of user-defined queries. A prototype database was implemented and validated on a cluster of commodity servers using live data gathered from a London pumping station and transmission mains. Benchmark results and reliability tests are included that demonstrate a significant improvement in performance over a traditional database architecture for a range of frequently-used operations, with many queries returning results near-instantaneously

Stochastic MPC with Dynamic Feedback Gain Selection and Discounted Probabilistic Constraints

This paper considers linear discrete-time systems with additive disturbances, and designs a Model Predictive Control (MPC) law incorporating a dynamic feedback gain to minimise a quadratic cost function subject to a single chance constraint. The feedback gain is selected from a set of candidates generated by solutions of multiobjective optimisation problems solved by Dynamic Programming (DP). We provide two methods for gain selection based on minimising upper bounds on predicted costs. The chance constraint is defined as a discounted sum of violation probabilities on an infinite horizon. By penalising violation probabilities close to the initial time and ignoring violation probabilities in the far future, this form of constraint allows for an MPC law with guarantees of recursive feasibility without an assumption of boundedness of the disturbance. A computationally convenient MPC optimisation problem is formulated using Chebyshev's inequality and we introduce an online constraint-tightening technique to ensure recursive feasibility. The closed loop system is guaranteed to satisfy the chance constraint and a quadratic stability condition. With dynamic feedback gain selection, the conservativeness of Chebyshev's inequality is mitigated and closed loop cost is reduced with a larger set of feasible initial conditions. A numerical example is given to show these properties.Comment: 14 page

An Innovative Control Framework for District Heating Systems: Conceptualisation and Preliminary Results

This paper presents a holistic innovative solution for the transformation of the current district heating and cooling systems to automated more efficient systems. A variety of technological advancements have been developed and integrated to support the effective energy management of future district heating and cooling sector. First, we identify and discuss the main challenges and needs that are in line with the EU objectives and policy expectations. We give an overview of the main parts that our solution consists of, with emphasis on the forecasting tools and an advanced control system that addresses unit commitment and economic load dispatch problems. The proposed control approach employs distributed and scalable optimisation algorithms for optimising the short-term operations of a district heating and cooling plant subject to technical constraints and uncertainties in the energy demand. To test the performance and validate the proposed control system, a district heating plant with multiple energy generation units and real-life heat load data were used. Simulation experiments were also used to evaluate the benefits of using thermal storage units in district heating systems. The results show that the proposed method could achieve significant cost savings when energy storage is employed. The proposed control strategy can be applied for both operating optimally district heating plants with storage and supporting investment planning for new storage units

Optimisation and Management of Energy Generated by a Multifunctional MFC-Integrated Composite Chassis for Rail Vehicles

With the advancing trend towards lighter and faster rail transport, there is an increasing interest in integrating composite and advanced multifunctional materials in order to infuse smart sensing and monitoring, energy harvesting and wireless capabilities within the otherwise purely mechanical rail structures and the infrastructure. This paper presents a holistic multiphysics numerical study, across both mechanical and electrical domains, that describes an innovative technique of harvesting energy from a piezoelectric micro fiber composites (MFC) built-in composite rail chassis structure. Representative environmental vibration data measured from a rail cabin have been critically leveraged here to help predict the actual vibratory and power output behaviour under service. Time domain mean stress distribution data from the Finite Element simulation were used to predict the raw AC voltage output of the MFCs. Conditioned power output was then calculated using circuit simulation of several state-of-the-art power conditioning circuits. A peak instantaneous rectified power of 181.9 mW was obtained when eight-stage Synchronised Switch Harvesting Capacitors (SSHC) from eight embedded MFCs were located. The results showed that the harvested energy could be sufficient to sustain a self-powered structural health monitoring system with wireless communication capabilities. This study serves as a theoretical foundation of scavenging for vibrational power from the ambient state in a rail environment as well as to pointing to design principles to develop regenerative and power neutral smart vehicles

Working With Incremental Spatial Data During Parallel (GPU) Computation

Central to many complex systems, spatial actors require an awareness of their local environment to enable behaviours such as communication and navigation. Complex system simulations represent this behaviour with Fixed Radius Near Neighbours (FRNN) search. This algorithm allows actors to store data at spatial locations and then query the data structure to find all data stored within a fixed radius of the search origin. The work within this thesis answers the question: What techniques can be used for improving the performance of FRNN searches during complex system simulations on Graphics Processing Units (GPUs)? It is generally agreed that Uniform Spatial Partitioning (USP) is the most suitable data structure for providing FRNN search on GPUs. However, due to the architectural complexities of GPUs, the performance is constrained such that FRNN search remains one of the most expensive common stages between complex systems models. Existing innovations to USP highlight a need to take advantage of recent GPU advances, reducing the levels of divergence and limiting redundant memory accesses as viable routes to improve the performance of FRNN search. This thesis addresses these with three separate optimisations that can be used simultaneously. Experiments have assessed the impact of optimisations to the general case of FRNN search found within complex system simulations and demonstrated their impact in practice when applied to full complex system models. Results presented show the performance of the construction and query stages of FRNN search can be improved by over 2x and 1.3x respectively. These improvements allow complex system simulations to be executed faster, enabling increases in scale and model complexity

An effective optimisation method for multifactor and reliability-related structural design problems

This thesis first presents a systematic design procedure which satisfies the required strength and stiffness, and structural mass for conceptual engineering structural designs. The procedure employs a multi-objective and multi-disciplinary (MO–MD) optimisation method (multifactor optimisation of structure techniques, MOST) which is coupled with finite element analysis (FEA) as an analysis tool for seeking the optimum design. The effectiveness of the MOST technique is demonstrated in two case studies.Next, a reliability-related multi-factor optimisation method is proposed and developed, representing a combination of MOST (as a method of multi-factor optimisation) and the reliability-loading case index (RLI) (as a method of calculating the reliability index). The RLI is developed based on a well-known reliability method: the first-order reliability method (FORM). The effectiveness and robustness of the proposed methodology are demonstrated in two case studies in which the method is used to simultaneously consider multi-objective, multi-disciplinary, and multi-loading-case problems. The optimised designs meet the targeted performance criteria under various loading conditions.The results show that the attributes of the proposed optimisation methods can be used to address engineering design problems which require simultaneous consideration of multi-disciplinary problems. An important contribution of this study is the development of a conceptual MO–MD design optimisation method, in which multi-factor structural and reliability design problems can be simultaneously considered

Support des compilateurs statiques et dynamiques pour les systèmes informatiques alimentés par intermittence

With the advent of Internet of things (IoT), there is a need to provide energy for a massive number of smart tiny devices without using large, heavy, and high maintenance batteries. One promising way is to harvest energy from the environment and store it into an energy buffer such as a capacitor. In this way, programs are being executed as long as there is available energy in the capacitor, and crash when it exhausts. Recently, different software and hardware based checkpointing strategies have been proposed to make forward progress toward execution for energy harvesting IoT devices. This thesis introduces two different software solutions based on static and dynamic compilation. The proposed static compiler inserts checkpoints based on statically-computed worst-case energy consumption of code sections. Moreover, it applies classical compiler optimizations in order to decrease the required number of checkpoints at runtime. The proposed dynamic compilation technique delays checkpoint placement and specialization to the runtime and takes decisions based on the past power failures and execution paths taken before each power failure. Both proposed solutions guarantee making forward progress as well as keeping the memory consistent. Furthermore, they aim to increase portability by not using any hardware feature of the IoT device. In addition, they are transparent to the programmer.Récemment, différentes stratégies de checkpointing basées sur le logiciel et le matériel ont été proposées pour avancer vers l'exécution pour les dispositifs IoT de récolte d'énergie. Cette thèse présente deux solutions logicielles différentes basées sur la compilation statique et dynamique. Le compilateur statique proposé insère des points de contrôle basés sur la pire consommation d'énergie des sections de code calculée de manière statique. En outre, il applique les optimisations classiques du compilateur afin de réduire le nombre de points de contrôle requis à l'exécution. La technique de compilation dynamique proposée reporte le placement et la spécialisation des points de contrôle au moment de l'exécution et prend des décisions en fonction des pannes de courant passées et des chemins d'exécution empruntés avant chaque panne de courant. Les deux solutions proposées garantissent une progression vers l'avant ainsi que le maintien de la cohérence de la mémoire. En outre, elles visent à accroître la portabilité en n'utilisant aucune caractéristique matérielle des systèmes IoT. En outre, elles sont transparentes pour le programmeur

Smart-antenna techniques for energy-efficient wireless sensor networks used in bridge structural health monitoring

Abstract: It is well known that wireless sensor networks differ from other computing platforms in that 1- they typically require a minimal amount of computing power at the nodes; 2- it is often desirable for sensor nodes to have drastically low power consumption. The main benefit of the this work is a substantial network life before batteries need to be replaced or, alternatively, the capacity to function off of modest environmental energy sources (energy harvesting). In the context of Structural Health Monitoring (SHM), battery replacement is particularly problematic since nodes can be in difficult to access locations. Furthermore, any intervention on a bridge may disrupt normal bridge operation, e.g. traffic may need to be halted. In this regard, switchbeam smart antennas in combination with wireless sensor networks (WSNs) have shown great potential in reducing implementation and maintenance costs of SHM systems. The main goal of implementing switch-beam smart antennas in our application is to reduce power consumption, by focusing the radiated energy only where it is needed. SHM systems capture the dynamic vibration information of a bridge structure in real-time in order to assess the health of the structure and to predict failures. Current SHM systems are based on piezoelectric patch sensors. In addition, the collection of data from the plurality of sensors distributed over the span of the bridge is typically performed through an expensive and bulky set of shielded wires which routes the information to a data sink at one end of the structure. The installation, maintenance and operational costs of such systems are extremely high due to high power consumption and the need for periodic maintenance. Wireless sensor networks represent an attractive alternative, in terms of cost, ease of maintenance, and power consumption. However, network lifetime in terms of node battery life must be very long (ideally 5–10 years) given the cost and hassle of manual intervention. In this context, the focus of this project is to reduce the global power consumption of the SHM system by implementing switched-beam smart antennas jointly with an optimized MAC layer. In the first part of the thesis, a sensor network platform for bridge SHM incorporating switched-beam antennas is modelled and simulated. where the main consideration is the joint optimization of beamforming parameters, MAC layer, and energy consumption. The simulation model, built within the Omnet++ network simulation framework, incorporates the energy consumption profiles of actual selected components (microcontroller, radio interface chip). The energy consumption and packet delivery ratio (PDR) of the network with switched-beam antennas is compared with an equivalent network based on omnidirectional antennas. In the second part of the thesis, this system model is leveraged to examine two distinct but interrelated aspects: Gallium Arsenide (GaAs) based solar energy harvesting and switched-beam antenna strategies. The main consideration here is the joint optimization of solar energy harvesting and switchedbeam directional antennas, where an equivalent network based on omnidirectional antennas acts as a baseline reference for comparison purposes.Il est bien connu que les réseaux de capteurs sans fils diffèrent des autres plateformes informatiques étant donné 1- qu’ils requièrent typiquement une puissance de calcul minimale aux noeuds du réseau ; 2- qu’il est souvent désirable que les noeuds capteurs aient une consommation d’énergie dramatiquement faible. La principale retombée de ce travail réside en la durée de vie allongée du réseau avant que les piles ne doivent être remplacées ou, alternativement, la capacité de fonctionner indéfiniment à partir de modestes sources d’énergie ambiente (glânage d’énergie). Dans le contexte du contrôle de la santé structurale (CSS), le remplacement de piles est particulièrement problématique puisque les noeuds peuvent se trouver en des endroits difficiles d’accès. De plus, toute intervention sur un pont implique une perturbation de l’opération normale de la structure, par exemple un arrêt du traffic. Dans ce contexte, les antennes intelligentes à commutation de faisceau en combinaison avec les réseaux de capteurs sans fils ont démontré un grand potentiel pour réduire les coûts de réalisation et d’entretien de systèmes de CSS. L’objectif principal de l’intégration d’antennes à commutation de faisceau dans notre application réside dans la réduction de la consommation énergétique, réalisée en concentrant l’énergie radiée uniquement là où elle est nécessaire. Les systèmes de CSS capturent l’information dynamique de vibration d’une structure de pont en temps réel de manière à évaluer la santé de la structure et prédire les failles. Les systèmes courants de CSS sont basés sur des senseurs piézoélectriques planaires. De plus, la collecte de données à partir de la pluralité de senseurs distribués sur l’étendue du pont est typiquement effectuée par le biais d’un ensemble coûteux et encombrant de câbles blindés qui véhiculent l’information jusqu’à un point de collecte à une extremité de la structure. L’installation, l’entretien, et les coûts opérationnels de tels systèmes sont extrêmement élevés étant donné la consommation de puissance élevée et le besoin d’entretien régulier. Les réseaux de capteurs sans fils représentent une alternative attrayante, en termes de coût, facilité d’entretien et consommation énergétique. Toutefois, la vie de réseau en termes de la durée de vie des piles doit être très longue (idéalement de 5 à 10 ans) étant donné le coût et les problèmes liés à l’intervention manuelle. Dans ce contexte, ce projet se concentre sur la réduction de la consommation de puissance globale d’un système de CSS en y intégrant des antennes intelligentes à commutation de faisceau conjointement avec une couche d’accès au médium (couche MAC) optimisée. Dans la première partie de la thèse, une plateforme de réseau de capteurs sans fils pour le CSS d’un pont incorporant des antennes à commutation de faisceaux est modélisé et simulé, avec pour considération principale l’optimisation des paramètres de sélection de faisceau, de la couche MAC et de la consommation d’énergie. Le modèle de simulation, construit dans le logiciel de simulation de réseaux Omnet++, incorpore les profils de consommation d’énergie de composants réels sélectionnés (microcontrôleur, puce d’interface radio). La consommation d’énergie et le taux de livraison de paquets du réseau avec antennes à commutation de faisceau est comparé avec un réseau équivalent basé sur des antennes omnidirectionnelles. Dans la deuxième partie de la thèse, le modèle système proposé est mis à contribution pour examiner deux aspects distrincts mais interreliés : le glânage d’énergie à partir de cellules solaire à base d’arséniure de Gallium (GaAs) et les stratégies liées aux antennes à commutation de faisceau. La considération principale ici est l’optimisation conjointe du glânage d’énergie et des antennes à commutation de faisceau, en ayant pour base de comparaison un réseau équivalent à base d’antennes omnidirectionnelles