Due to wireless communication technologies, positioning technologies, and mobile computing develop quickly, mobile services are becoming practical and important on big spatiotemporal databases management. Mobile service users move only inside a spatial network, e.g. a road network. They often issue the K Nearest Neighbor (KNN) query to obtain data objects reachable through the road network. The challenge problem of mobile services is how to efficiently answer the data objects which user interest to the corresponding mobile users. Therefore, how to effectively modeling road networks, effectively indexing, and querying on the road networks have become a popular topic. Lu et. al. have proposed a road network model that captures the real-life road networks better than previous models. Then, based on their model, they have proposed a RNG (Road Network Grid) index for speeding up the KNN query on real-life road networks. The RNG index structure is a quad-tree structure and a point-based structure. However, in their model, they divide the double track road which U-turn is allowed at some parts. This modeling does not capture the real-life road networks accurately. Since they divide the road, this makes the number of points of the graph increase. The number of times of partitioning the graph increases. It increases the execution time of constructing the index structure. The format of the leaf node of the RNG index makes the search time increase. Moreover, the query processing on the RNG index structure has to visit the root repeatedly. This condition makes the search time increase. Therefore, in this thesis, we propose a network model that captures the real-life road networks. We do not have to divide the real-life roads when we map the real-life roads into graph. We map the real-life road networks into graph directly. Then, based on our network model, we propose an EBNA (Edge-Based Nine-Area tree) index structure to make the search time of obtaining the interest edge information quickly. The EBNA index structure is an edge-based index structure. We store all of the edge information on the leaf node. We can obtain the edge information directly. Each edge information entry has a pointer point to link edges. Links of each edge entry consist a graph. This graph makes the KNN query processing visit the root only one time. From our simulation result, we show that the performance of constructing the EBNA index is better than constructing the RNG index and the performance of the KNN query processing by using EBNA index is better than the KNN query processing by using RNG index.

Wireless, Edge-based control and 5G networks are all examples of technologies of the emerging Industry 4.0. Understanding and evaluating these technologies is important to the development of future manufacturing and factories. However, moving from classical, wired control systems to wireless and Edge-based systems comes with new challenges such as communication delays and packet losses. The purpose of this thesis is to develop and evaluate the performance of a wireless 5G and Edge-based control system. Firstly, we aim to find the achievable end-to-end latency of three different network architectures: local control, control over wired Ethernet and control over wireless 5G. Secondly, we propose and test a conservative tuning approach on a Ball and Beam process which represents a time-sensitive and mission-critical process. The proposed conservative tuning approach is based on an Internal Model Control framework which enables an adjustment of the controller parameters based onthe worst-case measured latency. The results show that the measured latency increases as the Task interval time is increasing and as the controller is moving further away from a local level. The results also show that the introduced latency over 5G is making the system unstable if the latency is not taken into account in the design. The proposed conservative tuning approach successfully adjusts the parameters to remove this unstable behavior but degrades the control performance and shows signs of an overly conservative tuning compared to a local controller. The thesis concludes that the proposed conservative tuning approach shows promising results but would benefit from being further developed towards a latency-aware controller. This could be achieved by firstly improving the way latency is measured to enable extensive data collection. The data could then be utilized by using machine learning or time-series to predict the latency and adjust the parameters in real-time, using the proposed tuning approach.

In this thesis, our goal is to enable and achieve effective and efficient real-time stream processing in a geo-distributed infrastructure, by combining the power of central data centers and micro data centers. Our research focus is to address the challenges of distributing the stream processing applications and placing them closer to data sources and sinks. We enable applications to run in a geo-distributed setting and provide solutions for the network-aware placement of distributed stream processing applications across geo-distributed infrastructures.  First, we evaluate Apache Storm, a widely used open-source distributed stream processing system, in the community network Cloud, as an example of a geo-distributed infrastructure. Our evaluation exposes new requirements for stream processing systems to function in a geo-distributed infrastructure. Second, we propose a solution to facilitate the optimal placement of the stream processing components on geo-distributed infrastructures. We present a novel method for partitioning a geo-distributed infrastructure into a set of computing clusters, each called a micro data center. According to our results, we can increase the minimum available bandwidth in the network and likewise, reduce the average latency to less than 50%. Next, we propose a parallel and distributed graph partitioner, called HoVerCut, for fast partitioning of streaming graphs. Since a lot of data can be presented in the form of graph, graph partitioning can be used to assign the graph elements to different data centers to provide data locality for efficient processing. Last, we provide an approach, called SpanEdge that enables stream processing systems to work on a geo-distributed infrastructure. SpenEdge unifies stream processing over the central and near-the-edge data centers (micro data centers). As a proof of concept, we implement SpanEdge by extending Apache Storm that enables it to run across multiple data centers. / <p>QC 20161005</p>

<p>The purpose of this master thesis was to study computer vision algorithms for vehicle detection in monochrome images captured by mono camera. The work has mainly been focused on detecting rear-view cars in daylight conditions. Previous work in the literature have been revised and algorithms based on edges, shadows and motion as vehicle cues have been modified, implemented and evaluated. This work presents a combination of a multiscale edge based detection and a shadow based detection as the most promising algorithm, with a positive detection rate of 96.4% on vehicles at a distance of between 5 m to 30 m. For the algorithm to work in a complete system for vehicle detection, future work should be focused on developing a vehicle classifier to reject false detections.</p>

Hlavním cílem této diplomové práce byla texturní analýzou fundus snímku se zaměřením na směrovost vrstvy nervových vláken. Úvodní část popisuje fyziologii lidského oka a glaukomové onemocnění. Jedná se tedy o literární rešerši. Hlavní část této práce je pak zaměřena na metody texturní analýzy za účelem zobrazení směrovosti nervových vláken. Metody byly realizovány a otestovány v programovém prostředí Matlab R2009b.

Yes / Internet of Things (IoT) platforms are responsible for overall data processing in the IoT System. This ranges from analytics and big data processing to gathering all sensor data over time to analyze and produce long-term trends. However, this comes with prohibitively high demand for resources such as memory, computing power and bandwidth, which the highly resource constrained IoT devices lack to send data to the platforms to achieve efficient operations. This results in poor availability and risk of data loss due to single point of failure should the cloud platforms suffer attacks. The integrity of the data can also be compromised by an insider, such as a malicious system administrator, without leaving traces of their actions. To address these issues, we propose in this work an edge-based blockchain enabled anomaly detection technique to prevent insider attacks in IoT. The technique first employs the power of edge computing to reduce the latency and bandwidth requirements by taking processing closer to the IoT nodes, hence improving availability, and avoiding single point of failure. It then leverages some aspect of sequence-based anomaly detection, while integrating distributed edge with blockchain that offers smart contracts to perform detection and correction of abnormalities in incoming sensor data. Evaluation of our technique using real IoT system datasets showed that the technique remarkably achieved the intended purpose, while ensuring integrity and availability of the data which is critical to IoT success. / Petroleum Technology Development Fund(PTDF) Nigeria, Grant/Award Number:PTDF/ED/PHD/TYM/858/16

Developments are reported of unstructured-mesh methods for simulating stratified, turbulent and shear flows. The numerical model employs nonoscillatory forward in-time integrators for anelastic and incompressible flow PDEs, built on Multidimensional Positive Definite Advection Transport Algorithm (MPDATA) and a preconditioned conjugate residual elliptic solver. Finite-volume spatial discretisation adopts an edge-based data structure. Tetrahedral-based and hybrid-based median-dual options for unstructured meshes are developed, enabling flexible spatial resolution. Viscous laminar and detached eddy simulation (DES) flow solvers are developed based on the edge-based NFT MPDATA scheme. The built-in implicit large eddy simulation (ILES) capability of the NFT scheme is also employed and extended to fully unstructured tetrahedral and hybrid meshes. Challenging atmospheric and engineering problems are solved numerically to validate the model and to demonstrate its applications. The numerical problems include simulations of stratified, turbulent and shear flows past obstacles involving complex gravity-wave phenomena in the lee, critical-level laminar-turbulence transitioning and various vortex structures in the wake. Qualitative flow patterns and quantitative data analysis are both presented in the current study.

Gradient-based aerodynamic shape optimization, based on Computational Fluid Dynamics analysis of the flow, is a method that can automatically improve designs of aircraft components. The prospect is to reduce a cost function that reflects aerodynamic performances. When the shape is described by a large number of parameters, the calculation of one gradient of the cost function is only feasible by recourse to techniques that are derived from the theory of optimal control. In order to obtain the best computational efficiency, the so called adjoint method is applied here on the complete mapping, from the parameters of design to the values of the cost function. The mapping considered here includes the Euler equations for compressible flow discretized on unstructured meshes by a median-dual finite-volume scheme, the primal-to-dual mesh transformation, the mesh deformation, and the parameterization. The results of the present research concern the detailed derivations of expressions, equations, and algorithms that are necessary to calculate the gradient of the cost function. The discrete adjoint of the Euler equations and the exact dual-to-primal transformation of the gradient have been implemented for 2D and 3D applications in the code Edge, a program of Computational Fluid Dynamics used by Swedish industries. Moreover, techniques are proposed here in the aim to further reduce the computational cost of aerodynamic shape optimization. For instance, an interpolation scheme is derived based on Radial Basis Functions that can execute the deformation of unstructured meshes faster than methods based on an elliptic equation. In order to improve the accuracy of the shape, obtained by numerical optimization, a moving mesh adaptation scheme is realized based on a variable diffusivity equation of Winslow type. This adaptation has been successfully applied on a simple case of shape optimization involving a supersonic flow. An interpolation technique has been derived based on a mollifier in order to improve the convergence of the coupled mesh-flow equations entering the adaptive scheme. The method of adjoint derived here has also been applied successfully when coupling the Euler equations with the boundary-layer and parabolized stability equations, with the aim to delay the laminar-to-turbulent transition of the flow. The delay of transition is an efficient way to reduce the drag due to viscosity at high Reynolds numbers.

The purpose of this master thesis was to study computer vision algorithms for vehicle detection in monochrome images captured by mono camera. The work has mainly been focused on detecting rear-view cars in daylight conditions. Previous work in the literature have been revised and algorithms based on edges, shadows and motion as vehicle cues have been modified, implemented and evaluated. This work presents a combination of a multiscale edge based detection and a shadow based detection as the most promising algorithm, with a positive detection rate of 96.4% on vehicles at a distance of between 5 m to 30 m. For the algorithm to work in a complete system for vehicle detection, future work should be focused on developing a vehicle classifier to reject false detections.

A brokerless edge service marketplace could play a significant role in enabling an eco- system where a large number of edge providers and Communication Service Providers (CSPs) offer Mobile Edge Infrastructure Services (EISs) to providers of edge-based applications and services. The marketplace would be the bridge between EIS providers and their customers, managing the relations between actors in the mobile edge eco- system. One of the key services of the marketplace is service selection where not only a list of EISs matching the customers’ demands is provided but also enables service selection based on the customers’ requirements to fully automate the process.We firstly consider different selection scenarios and investigate essential parameters for service selection in such a marketplace, including (i) important attributes of EISs such as coverage area, latency, pricing, etc, and (ii) the requirements of edge-based applications such as latency and reliability. We formulate how these application requirements can be fulfilled by choosing the right set of EISs among available services in the marketplace as two different optimization problems considering average latency and cost as separate objectives to minimize. First, we relax the objective function in average delay minimization problem, which is a linear-fractional programming problem. We solve the relaxed version of the problem by Branch and bound (BnB) and Bound and Branch with Priority Queue (BnBPQ) algorithms. Additionally, we relax the objective function in total monetary cost minimization problem which is an integer linear programming problem. We propose Best Fit (BF) and Improved Best Fit (IBF) algorithms to solve the problem. Furthermore, the IBM CPLEX Optimizer [1] is also implemented to solve the two problems. The evaluation shows that the algorithms we implemented can solve the problems with results close to optimal as compared to the results of exhaustive search and shorter run time than exhaustive search. Meanwhile, the CPLEX can solve the problem well, but it’s not scalable for huge problem instances since the problems are NP-complete and not solvable in polynomial time. / En marknadslös marknadsföring på kanten av tjänster kan spela en viktig roll för att möjliggöra ett ekosystem där ett stort antal kantleverantörer och acp CSP erbjuder Mobile acp EIS till leverantörer av kantbaserade applikationer och tjänster. Marknadsplatsen skulle vara bron mellan ac EIS leverantörer och deras kunder och hantera relationerna mellan aktörer i det mobila ekosystemet. En av marknadens viktigaste tjänster är val av tjänster där inte bara en lista över acp EIS som matchar kundernas krav tillhandahålls utan också möjliggör serviceval baserat på kundernas krav för att automatisera processen helt.Vi överväger för det första olika urvalsscenarier och undersöker väsentliga parametrar för val av tjänster på en sådan marknadsplats, inklusive (i) viktiga attribut för acp EIS som täckningsområde, latens, prissättning osv. Och (ii) kraven på kant- baserade applikationer som latens och tillförlitlighet. Vi formulerar hur dessa applikationskrav kan uppfyllas genom att välja rätt uppsättning acp EIS bland tillgängliga tjänster på marknaden som två olika optimeringsproblem med tanke på genomsnittlig latens och kostnad som separata mål för att minimera. Först slappnar vi av objektivfunktionen i ett genomsnittligt förseningsminimeringsproblem, vilket är ett linjärt-fraktionellt programmeringsproblem. Vi löser den avslappnade versionen av problemet med ac BnB och ac BnBPQ algoritmer. Dessutom slappnar vi av objektivfunktionen i totala monetära kostnadsminimeringsproblem som är ett heltal linjärt programmeringsproblem. Vi föreslår ac BF och ac IBF algoritmer för att lösa problemet. Dessutom implementeras IBM CPLEX Optimizer cite cplex för att lösa de två problemen. Utvärderingen visar att algoritmerna vi implementerade kan lösa problemen med resultat som är nära optimala jämfört med resultaten av uttömmande sökning och kortare körtid än uttömmande sökning. Under tiden kan CPLEX lösa problemet väl, men det är inte skalbart för stora problemstillfällen eftersom problemen är NP-kompletta och inte lösbara under polynom tid.