Trust management in mobile networks is challenging due to dynamically changing network environments and the lack of a centralized trusted authority. In this dissertation research, we design and validate a class of dynamic trust management protocols for mobile networks, and demonstrate the utility of dynamic trust management with trust-based applications. Unlike existing work, we consider social trust derived from social networks in addition to traditional quality-of-service (QoS) trust derived from communication networks to obtain a composite trust metric as a basis for evaluating trust of nodes in mobile network applications. Untreated in the literature, we design and validate trust composition, aggregation, propagation, and formation protocols for dynamic trust management that can learn from past experiences and adapt to changing environment conditions to maximize application performance and enhance operation agility. Furthermore, we propose, explore and validate the design concept of application-level trust optimization in response to changing conditions to maximize application performance or best satisfy application requirements. We provide formal proof for the convergence, accuracy, and resiliency properties of our trust management protocols. To achieve the goals of identifying the best trust protocol setting and optimizing the use of trust for trust-based applications, we develop a novel model-based analysis methodology with simulation validation for analyzing and validating our dynamic trust management protocol design. The dissertation research provides new understanding of dynamic trust management for mobile wireless networks. We gain insight on the best trust composition and trust formation out of social and QoS trust components, as well as the best trust aggregation and propagation protocols for optimizing application performance. We gain insight on how a modeling and analysis tool can be built, allowing trust composition, aggregation, propagation, and formation designs to be incorporated, tested and validated. We demonstrate the utility of dynamic trust management protocol for mobile networks including mobile ad-hoc networks, delay tolerant networks, wireless sensor networks, and Internet of things systems with practical applications including misbehaving node detection, trust-based survivability management, trust-based secure routing, and trust-based service composition. Through model-based analysis with simulation validation, we show that our dynamic trust management based protocols outperform non-trust-based and Bayesian trust-based protocols in the presence of malicious, erroneous, partly trusted, uncertain and incomplete information, and are resilient to trust related attacks. / Ph. D.

The increasing attention towards energy efficiency issues has triggered an important process involving the renovation of existing buildings and, at the same time, the creation of recognized certifications assuring the quality of the projects. In line with this trend, the Sweco headquarters, an office building characterized by 24700 m2 of floor area and located in Stockholm, was totally retrofitted in 2012 and obtained the Gold rating after being assessed with the Miljöbyggnad certification procedure. The HVAC system was a key element of the retrofit project since one of the final aims was to combine high indoor environment standards with efficient system performances. However, even if the quality of the design is certified, it is possible that, under real operating conditions, complex systems behave differently from the expectations and adjustments are necessary to correct the emerged gap. To achieve this goal, it is essential to identify the points of weakness of the system by carrying out an energy performance analysis, which is the core of this project. In fact, after providing an overview of the building and the retrofit, this work focuses on the analysis of the cooling system installed in the Sweco building and proves the importance of adopting a step-by-step approach to the problem. Therefore, an increasing level of detail characterizes each step of the analysis, whose final aim is to highlight potential aspects to be improved and create a baseline to test possible solutions. / SIRen

This study assesses the relative strengths and weaknesses of a hotel, Holiday Inn, as viewed by two customer segments: business travelers and vacationers. The evaluation is based on the analytical framework of importance-performance analysis using 28 hotel attributes. Despite some differences, there are a number of similarities between the two segments in terms of the attributes they rely on when choosing a hotel and how they evaluate Holiday Inn on these attributes. These insights are important to the management in designing pointed strategies to better serve the needs of their guests.

The changing and highly competitive job market is putting pressure on both the private and public sectors to maintain and prepare an educated and well-trained human resource. In this context, a well-integrated educational and training strategy must be formulated and implemented, jointly, by the public and private sectors. This study presents the effort of Portugal in this direction. It emphasises a process orientation to achieve the goal of upgrading the educational skills of Portuguese employees, part of whom are entrepreneurs. For the purpose of this study cluster analysis, regression analysis and the important performance analysis were used. Based on the findings, the implications of the cooperation between the public and private sectors are discussed and a conceptual framework to aid policymakers, decision makers, and future researchers is offered. The benchmarking implications of this process are underscored.

In present road traffic system, drone-network based traffic monitoring using the Internet of Vehicles (IoVs) is a promising solution. However, camera-based traffic monitoring does not collect complete data, cover all areas, provide quick medical services, or take vehicle follow-ups in case of an incident. Drone-based system helps to derive important information (such as commuter's behavior, traffic patterns, vehicle follow-ups) and sends this information to centralized or distributed authorities for making traffic diversions or necessary decisions as per laws. The present approaches fail to meet the requirements such as (i) collision free, (ii) drone navigation, and (iii) less computational and communicational overheads. This work has considered the collision-free drone-based movement strategies for road traffic monitoring using Software Defined Networking (SDN). The SDN controllable drone network results in lesser overhead over drones and provide efficient drone-device management. In simulation, two case studies are simulated using JaamSim simulator. Results show that the zones-based strategy covers a large area in few hours and consume 5 kWs to 25 kWs energy for 150 drones (Case study 1). Zone-less based strategies (case study-2) show that the energy consumption lies between 5 kWs to 18 kWs for 150 drones. Further, the use of SDN-based drones controller reduces the overhead over drone-network and increases the area coverage with a minimum of 1.2% and maximum of 2.6%. Simulation (using AnyLogic simulator) shows the 3D view of successful implementation of collision free strategies.

Parallel sparse matrix-matrix multiplication algorithms (PSpGEMM) spend most of their running time on inter-process communication. In the case of distributed matrix-matrix multiplications, much of this time is spent on interchanging the partial results that are needed to calculate the final product matrix. This overhead can be reduced with a one-dimensional distributed algorithm for parallel sparse matrix-matrix multiplication that uses a novel accumulation pattern based on the logarithmic complexity of the number of processors (i.e., O (log (p)) where p is the number of processors). This algorithm's MPI communication overhead and execution time were evaluated on an HPC cluster, using randomly generated sparse matrices with dimensions up to one million by one million. The results showed a reduction of inter-process communication overhead for matrices with larger dimensions compared to another one dimensional parallel algorithm that takes O(p) run-time complexity for accumulating the results.

No description available.

Projecting performance of applications and hardware is important to several market segments—hardware designers, software developers, supercomputing centers, and end users. Hardware designers estimate performance of current applications on future systems when designing new hardware. Software developers make performance estimates to evaluate performance of their code on different architectures and input datasets. Supercomputing centers try to optimize the process of matching computing resources to computing needs. End users requesting time on supercomputers must provide estimates of their application’s run time, and incorrect estimates can lead to wasted supercomputing resources and time. However, application performance is challenging to predict because it is affected by several factors in application code, specifications of system hardware, choice of compilers, compiler flags, and libraries. This dissertation uses statistical techniques to model and optimize performance of scientific applications across different computer processors. The first study in this research offers statistical models that predict performance of an application across different input datasets prior to application execution. These models guide end users to select parameters that produce optimal application performance during execution. The second study offers a suite of statistical models that predict performance of a new application on a new processor. Both studies present statistical techniques that can be generalized to analyze, optimize, and predict performance of diverse computation- and data-intensive applications on different hardware.

The control of batch pulp digesters is hampered by insufficient measurements as well as nonlinearity and weak correlation between consecutive cooks. This makes a model-based approach to control attractive. Due to the age of the industry, many legacy controllers are in place on digesters around the world. The theoretical variance obtained by Monte Carlo modelling of a new controller is used as a benchmark for performance comparison between an old control system (S-factor) and a new model based controller developed by the University of Pretoria (the UP controller). This study covers the development of the controller, Monte Carlo modelling of the old and new controllers and in-situ testing of the UP controller on an operating digester. During Monte Carlo simulation, the UP controller outperformed the legacy controller, obtaining a theoretical overall variance of 3,07 (which will be used as the baseline for performance measurement) while also showing larger responses to tuning factors. The S-factor performed at 6,8 times the theoretical optimum variance during in situ testing, while the UP controller performed at 3,9 times the theoretical optimum (43% better than the S-factor controller). An average error 90% lower than that of the S-factor controller was obtained when using the UP controller. Additional benefits of the new controller include easy inclusion of new measurements and clear relations between the tuning parameters used and the conditions in the digester. / Dissertation (MEng (Control))--University of Pretoria, 2003. / Chemical Engineering / unrestricted

No description available.