A future Internet of Things (IoT) system will consist of a huge quantity of heterogeneous IoT devices, each capable of providing services upon request. It is of utmost importance for an IoT device to know if another IoT service is trustworthy when requesting it to provide a service. In this dissertation research, we develop trust-based service management techniques applicable to distributed, centralized, and hybrid IoT environments. For distributed IoT systems, we develop a trust protocol called Adaptive IoT Trust. The novelty lies in the use of distributed collaborating filtering to select trust feedback from owners of IoT nodes sharing similar social interests. We develop a novel adaptive filtering technique to adjust trust protocol parameters dynamically to minimize trust estimation bias and maximize application performance. Our adaptive IoT trust protocol is scalable to large IoT systems in terms of storage and computational costs. We perform a comparative analysis of our adaptive IoT trust protocol against contemporary IoT trust protocols to demonstrate the effectiveness of our adaptive IoT trust protocol. For centralized or hybrid cloud-based IoT systems, we propose the notion of Trust as a Service (TaaS), allowing an IoT device to query the service trustworthiness of another IoT device and also report its service experiences to the cloud. TaaS preserves the notion that trust is subjective despite the fact that trust computation is performed by the cloud. We use social similarity for filtering recommendations and dynamic weighted sum to combine self-observations and recommendations to minimize trust bias and convergence time against opportunistic service and false recommendation attacks. For large-scale IoT cloud systems, we develop a scalable trust management protocol called IoT-TaaS to realize TaaS. For hybrid IoT systems, we develop a new 3-layer hierarchical cloud structure for integrated mobility, service, and trust management. This architecture supports scalability, reconfigurability, fault tolerance, and resiliency against cloud node failure and network disconnection. We develop a trust protocol called IoT-HiTrust leveraging this 3-layer hierarchical structure to realize TaaS. We validate our trust-based IoT service management techniques developed with real-world IoT applications, including smart city air pollution detection, augmented map travel assistance, and travel planning, and demonstrate that our trust-based IoT service management techniques outperform contemporary non-trusted and trust-based IoT service management solutions. / Ph. D. / A future Internet of Things (IoT) system will consist of a huge quantity of heterogeneous IoT devices, each capable of providing services upon request. It is of utmost importance for an IoT device to know if another IoT service is trustworthy when requesting it to provide a service. In this dissertation research, we develop trust-based service management techniques applicable to distributed, centralized, and hybrid IoT environments. We have developed a distributed trust protocol called Adaptive IoT Trust for distributed IoT applications, a centralized trust protocol called IoT-TaaS for centralized IoT applications with cloud access, and a hierarchical trust management protocol called IoT-HiTrust for hybrid IoT applications. We have verified that desirable properties, including solution quality, accuracy, convergence, resiliency, and scalability have been achieved. Furthermore, we validate our trust-based IoT service management techniques developed with real-world IoT applications, including smart city air pollution detection, augmented map travel assistance, and travel planning, and demonstrate that our trust-based IoT service management techniques outperform contemporary non-trusted and trust-based IoT service management solutions.

The use of aerial platforms such as unmanned aerial vehicles (UAVs), popularly known as drones, has emerged as a promising solution for providing reliable and cost-effective wireless communications. In particular, UAVs can be quickly and efficiently deployed to support cellular networks and enhance their quality-of-service (QoS) by establishing line-of-sight communication links. With their inherent attributes such as mobility, flexibility, and adaptive altitude, UAVs admit several key potential applications in wireless systems. Remarkably, despite these inherent advantages of UAVbased communications, little work has analyzed the performance tradeoffs associated with using UAVs as aerial wireless platforms. The key goal of this dissertation is to develop the analytical foundations for deployment, performance analysis, and optimization of UAV-enabled wireless networks. This dissertation makes a number of fundamental contributions to various areas of UAV communications that include: 1) Efficient deployment of UAVs, 2) Performance evaluation and optimization, and 3) Design of new flying, three-dimensional (3D) wireless systems. For deployment, using tools from optimization theory, holistic frameworks are developed for the optimal 3D placement of UAV base stations in uplink and downlink scenarios. The results show that the proposed deployment approaches significantly improve the downlink coverage for ground users, and enable ultra-reliable and energy-efficient uplink communications in Internet of Things (IoT) applications. For performance optimization, a novel framework is developed for maximizing the performance of a UAV-based wireless system, in terms of data service, under UAVs’ flight time constraints. To this end, using the mathematical framework of optimal transport theory, the optimal cell associations, that lead to a maximum data service to ground users within the limited UAVs’ hover duration, are analytically derived. The results shed light on the tradeoff between hover time and quality-of-service in UAV-based wireless networks. For performance evaluation, this dissertation provides a comprehensive analysis on the performance of a UAV-based communication system in coexistence with a terrestrial network. In particular, a tractable analytical framework is proposed for analyzing the coverage and rate performance of a network with a UAV base station and deviceto-device (D2D) users. The results reveal the fundamental tradeoffs in such a UAV-D2D network that allow adopting appropriate system design parameters. Then, this dissertation sheds light on the design of three new drone-enabled wireless systems. First, a novel framework for effective use of cache-enabled UAVs in wireless networks is developed. The results demonstrate how the users’ quality of experience substantially improves by exploiting UAVs’ mobility and user-centric information. Second, a new framework is proposed for deploying and operating a drone-based antenna array system that delivers wireless service to ground users within a minimum time. The results show significant enhancement in QoS, spectral and energy efficiency while levering the proposed drone antenna array system. Finally, to effectively incorporate various use cases of drones ranging from aerial users to base stations, the new concept of a fully-fledged 3D cellular network is introduced. For this new type of 3D wireless network, a unified framework for deployment, network planning, and performance optimization is developed that yields a maximum coverage and minimum latency in the network. In a nutshell, the analytical foundations and frameworks presented in this dissertation provide key guidelines for effective design and operation of UAV-based wireless communication systems. / Ph. D. / Unmanned aerial vehicles (UAVs), commonly known as drones, have been the subject of concerted research over the past few years, owing to their autonomy, flexibility, and broad range of application domains. Indeed, UAVs have been considered as enablers of various applications that include military, surveillance and monitoring, telecommunications, delivery of medical supplies, and rescue operations. The unprecedented recent advances in drone technology has made it possible to widely deploy UAVs, such small aircrafts, balloons, and airships for wireless communication purposes. In particular, if properly deployed and operated, UAVs can provide reliable and cost-effective wireless communication solutions for a variety of real-world scenarios. On the one hand, drones can be used as aerial base stations that can deliver reliable, cost-effective, and on-demand wireless communications to desired areas. On the other hand, drones can function as aerial user equipments, known as cellular-connected UAVs, in coexistence with ground users. Despite such promising opportunities for drones, one must address a number of technical challenges in order to effectively use them for each specific networking application. For instance, while using drone-BS, the key design considerations include performance characterization, optimal 3D deployment of drones, wireless and computational resource allocation, flight time and trajectory optimization, and network planning. Meanwhile, in the drone-UE scenario, handover management, channel modeling, low-latency control, 3D localization, and interference management are among the main challenges. Therefore, this dissertation addresses the fundamental challenges in UAV-enabled wireless communications that allows providing broadband, wide-scale, cost-effective, and reliable wireless connectivity. To this end, various mathematical frameworks and efficient algorithms are proposed to design, optimize, deploy, and operate UAV-based communication systems. The results shows that, the proposed aerial communication system can deliver ultra-reliable, and cost-effective wireless services, thus providing ubiquitous high speed Internet connectivity for the whole world.

Currently, water utilities are facing monetary crises to maintain and expand services to meet the current as well as the future demands. Standard practice in pipeline infrastructure asset management is to collect data and predict the condition of pipelines using models and tools. Water utilities want to be proactive in fixing or replacing the pipes as fixing-when-it-fails ideology leads to increased cost and can affect environmental quality and societal health. There is a number of modeling techniques available for assessing the condition of the pipelines, but there is a massive shortage of methods to check the reliability of the results obtained using different modeling techniques. It is mainly because of the limited data one utility collects and absence of piloting of these models at various water utilities. In general, water utilities feel confident about their in-house condition prediction and failure models but are willing to utilize a reliable methodology which can overcome the issues related to the validation of the results. This paper presents the methodology that can enhance the reliability of model results for water pipeline performance analysis which can be used to parallel the output of the real system with confidence. The proposed methodology was checked using the dataset of two large water utilities and was found that it can potentially help water utilities gain confidence in their analyses results by statistically signifying the results. / Master of Science / Water utilities are facing monetary crises to maintain and expand services to meet the current as well as the future demands. Standard practice in pipeline infrastructure asset management is to collect data and predict the condition of pipelines using models and tools. There is a number of modeling techniques available for assessing the condition of the pipelines, but there is a massive shortage of methods to check the reliability of the results obtained using different modeling techniques. This study presents the methodology that can enhance the reliability of model results for water pipeline performance analysis which can potentially help water utilities to be proactive in fixing or replacing the pipelines with confidence. Different types of analyses on the data received from the two large water utilities (name confidential) were performed to understand and check the application of the proposed methodology in the real world and was found that it can potentially help water utilities gain confidence in their analyses results by statistically signifying the result

A mobile user may voluntarily disconnect itself from the web server to save battery life and also to avoid the high communication price. To allow web pages to be updated while the mobile user is disconnected from the web server, updates can be staged in the mobile unit and propagated back to the web server upon reconnection. In this thesis, we investigate methods for supporting disconnected write operations and develop a performance model which helps identify the optimal length of the disconnection period under which the cost of update propagation is minimized. We validate the analytic model with simulation in the thesis. We also show how the result can be applied to real-time web applications with a deadline requirement to propagate updates of web pages. The analysis result is particularly applicable to web applications which allow wireless mobile users to modify web contents while on the go. The algorithms that we have developed can be generally applied to other data items such as files and databases. / Master of Science

Performance regression testing is a difficult task with several intricacies and complexities. In the absence of analysis tools, manual analysis must be conducted which is undoubtedly infeasible. Thereby, in this thesis, an automated performance analysis framework is proposed, aiming to mitigate the faced issues. To make this possible, the adequacy of the data needed to be established. Additionally, a fault detection algorithm had to be developed. From investigating the current state-of-the-art of performance anomaly detection, evidently, statistical models have been utilised far more than classical machine learning, and deep learning. Consequently, based on this knowledge and based on the types of anomalies present in the data, a cumulative sum based statistical method is proposed. The findings demonstrate that the data is adequate for detecting faults, and verifying their validity, as they are consistently observable in several test configurations. However, tests are not performed frequently enough which consequently leads to challenges in identifying the exact locations of faults. The algorithm was evaluated on artificial data with injected faults and could detect over 90 % of anomalies if they were prominent enough. Longer sequences before fault deviations occur, improved the ability of detecting the faults. Thus, further motivating the need to collect data more frequently. On a final note, the automated performance analysis framework successfully improved the efficiency of fault detection, and greater contextual data awareness were achieved through the visualization features. Manual analysis can however detect faults with greater accuracy. On that ground, these results should be interpreted with caution.

Thesis (MScEng (Mechanical and Mechatronic Engineering))--University of Stellenbosch, 2009. / This thesis describes the development of a vacuum arc thruster (VAT) to be used as a potential low mass (< 500 g), low power (< 5–10W) propulsion system for nanosatellites. The thruster uses a high voltage capacitive circuit to initiate and power the arc process with a 400 ns high current (150–800A) pulse. A one-dimensional steady state analyticalmodel describing the cathode region of the vacuum arc was developed. The model made use of mass and energy balances at the sheath region and cathode surface respectively to predict key quantities such as thrust, ion velocity, ion-to-arc current ratio and erosion rate. Predicted results were shown to be within the limits of reported literature (∼63 μN/A, 26.12 km/s, 0.077 and 110 μg/C respectively). A sensitivity analysis of the analytical model found that a high electric field in the cathode region impedes and decelerates ion flow, which is used for thrust. This was confirmed experimentally for thrust values at arc voltages greater than 2000 V. Both direct and indirect means of measuring thrust were achieved by using a deflecting cantilever beam and an ion collector system, respectively. The transient response of the cantilever beam to impulsive thrust was analytically modeled, whilst the ion current was found by measuring the current induced on a plate subject to ion bombardment. Knowledge of the ion current density distribution was successfully used to approximate the effective normal thrust vector. Direct and indirect thrust levels were roughly 140 and 82 μN/A of average arc current, respectively. Measured thrust was found to be higher than predicted thrust due to thrust contributions fromthe ablation of Teflon insulation. The discrepancy is also due to the uncertainty in quantifying free parameters in the analytical model such as the fraction of generated ions flowing away from the cathode region. The thrust-topower ratio, specific impulse and efficiency of the vacuum arc thruster at an average arc current of 200 A was measured to be 0.6 μN/W, 160 s and 0.05 %, respectively. A thruster performance analysis and specification showed that the VAT is capable of achieving specific orbital and slew manoeuvres within a constant 5–10 W average power. It was concluded that thruster performance could be improved by using a two-stage arc circuit consisting of a high voltage, low current, short pulse trigger and a low voltage, high current, long pulse driver.

Over recent years, wireless communication systems have been experiencing a dramatic and continuous growth in the number of subscribers, thus placing extra demands on system capacity. At the same time, keeping Quality of Service (QoS) at an acceptable level is a critical concern and a challenge to the wireless network designer. In this sense, performance analysis must be the first step in designing or improving a network. Thus, powerful mathematical tools for analysing most of the performance metrics in the network are required. A good modelling and analysis of the wireless cellular networks will lead to a high level of QoS. In this thesis, different analytical models of various handoff schemes and resource re-allocation in homogeneous and heterogeneous wireless cellular networks are developed and investigated. The sustained increase in users and the request for advanced services are some of the key motivations for considering the designing of Hierarchical Cellular Networks (HCN). In this type of system, calls can be blocked in a microcell flow over to an overlay macrocell. Microcells in the HCN can be replaced by WLANs as this can provide high bandwidth and its users have limited mobility features. Efficient sharing of resources between wireless cellular networks and WLANs will improve the capacity as well as QoS metrics. This thesis first presents an analytical model for priority handoff mechanisms, where new calls and handoff calls are captured by two different traffic arrival processes, respectively. Using this analytical model, the optimised number of channels assigned to II handover calls, with the aim of minimising the drop probability under given network scenarios, has been investigated. Also, an analytical model of a network containing two cells has been developed to measure the different performance parameters for each of the cells in the network, as well as altogether as one network system. Secondly, a new solution is proposed to manage the bandwidth and re-allocate it in a proper way to maintain the QoS for all types of calls. Thirdly, performance models for microcells and macrocells in hierarchical cellular networks have been developed by using a combination of different handoff schemes. Finally, the microcell in HCN is replaced by WLANs and a prioritised vertical handoff scheme in an integrated UMTS/WLAN network has been developed. Simulation experiments have been conducted to validate the accuracy of these analytical models. The models have then been used to investigate the performance of the networks under different scenarios.

<p>With the increasing capacity of today's integrated circuits, a number ofheterogeneous  system-on-chip (SoC)  architectures  in embedded  systemshave been proposed. In order to achieve energy and design cost efficientstreaming applications  on these  systems, new design  space explorationframeworks  and  performance  analysis  approaches are  required.   Thisthesis  considers three state-of-the-art  SoCs architectures,  i.e., themulti-processor SoCs (MPSoCs)  with network-on-chip (NoC) communication,the hybrid CPU/FPGA architectures, and the run-time reconfigurable (RTR)FPGAs.  The main topic of the  author?s research is to model and capturethe  application  scheduling,  architecture  customization,  and  bufferdimensioning  problems, according to  the real-time  requirement.  Sincethese  problems  are NP-complete,  heuristic  algorithms and  constraintprogramming solver are used to compute a solution.For  NoC  communication  based  MPSoCs,  an  approach  to  optimize  thereal-time    streaming    applications    with   customized    processorvoltage-frequency levels and memory  sizes is presented. A multi-clockedsynchronous  model  of  computation   (MoC)  framework  is  proposed  inheterogeneous  timing analysis and  energy estimation.   Using heuristicsearching  (i.e., greedy  and  taboo search),  the  experiments show  anenergy reduction (up to 21%)  without any loss in application throughputcompared with an ad-hoc approach.On hybrid CPU/FPGA architectures,  the buffer minimization scheduling ofreal-time streaming  applications is addressed.  Based  on event models,the  problem  has  been  formalized  decoratively  as  constraint  basescheduling,  and  solved  by  public domain  constraint  solver  Gecode.Compared  with  traditional  PAPS  method,  the  proposed  method  needssignificantly smaller  buffers (2.4%  of PAPS in  the best  case), whilehigh throughput guarantees can still be achieved.Furthermore, a  novel compile-time analysis approach  based on iterativetiming  phases is  proposed  for run-time  reconfigurations in  adaptivereal-time   streaming   applications  on   RTR   FPGAs.   Finally,   thereconfigurations analysis and design trade-offs analysis capabilities ofthe proposed  framework have been  exemplified with experiments  on bothexample and industrial applications.</p> / Andres

<p>Recently, wireless sensor network (WSN) has become a promising technologywith a wide range of applications such as supply chain monitoringand environment surveillance. It is typically composed of multiple tiny devicesequipped with limited sensing, computing and wireless communicationcapabilities. Design of such networks presents several technique challengeswhile dealing with various requirements and diverse constraints. Performanceanalysis techniques are required to provide insight on design parametersand system behaviors.</p><p>Based on network calculus, we present a deterministic analysis methodfor evaluating the worst-case delay and buffer cost of sensor networks. Tothis end, three general traffic flow operators are proposed and their delayand buffer bounds are derived. These operators can be used in combinationto model any complex traffic flowing scenarios. Furthermore, the methodintegrates a variable duty cycle to allow the sensor nodes to operate at lowrates thus saving power. In an attempt to balance traffic load and improveresource utilization and performance, traffic splitting mechanisms areintroduced for mesh sensor networks. Based on network calculus, the delayand buffer bounds are derived in non-splitting and splitting scenarios.In addition, analysis of traffic splitting mechanisms are extended to sensornetworks with general topologies. To provide reliable data delivery in sensornetworks, retransmission has been adopted as one of the most popularschemes. We propose an analytical method to evaluate the maximum datatransmission delay and energy consumption of two types of retransmissionschemes: hop-by-hop retransmission and end-to-end retransmission.</p><p>We perform a case study of using sensor networks for a fresh food trackingsystem. Several experiments are carried out in the Omnet++ simulationenvironment. In order to validate the tightness of the two bounds obtainedby the analysis method, the simulation results and analytical results arecompared in the chain and mesh scenarios with various input traffic loads.From the results, we show that the analytic bounds are correct and tight.Therefore, network calculus is useful and accurate for performance analysisof wireless sensor network.</p> / Ipack VINN Excellence Center

With rising complexity of high performance computing systems and their parallel software, performance analysis and optimization has become essential in the development of efficient applications. The comparison of performance data is a key operation required in performance analysis. An analyst may conduct different types of comparisons in order to understand the performance properties of an application. One use case is comparing performance data from multiple measurements. Typical examples for such comparisons are before/after comparisons when applying optimizations or changing code versions. Besides comparing performance between multiple runs, also comparing performance characteristics across the parallel execution streams of an application is essential to detect performance problems. This is typically useful to detect imbalances, outliers, or changing runtime behavior during the execution of an application. While such comparisons are straightforward for the aggregated data in performance profiles, only limited solutions exist for comparing event traces. Trace-based analysis, i.e., the collection of fine-grained information on individual application events with timestamps and application context, has proven to be a powerful technique. The detailed performance information included in event traces make them very suitable for performance analysis. However, this level of detail also presents a challenge because it implies a large and overwhelming amount of data. Currently, users need to perform manual comparison of event traces, which is extremely challenging and time consuming because of the large volume of detailed data and the need to correctly line up trace events. To fill the gap of missing solutions for automatic comparison of event traces, this work proposes a set of techniques that automatically align traces. The alignment allows their structural comparison and the highlighting of differences between them. A set of novel metrics provide the user with an objective measure of the differences between traces, both in terms of differences in the event stream and timing differences across events. An additional important aspect of trace-based analysis is the visualization of performance data in event timelines. This has proven to be a powerful approach for the detection of various types of performance problems. However, visualization of large numbers of event timelines quickly hits the limits of available display resolution. Likewise, identifying performance problems is challenging in the large amount of visualized performance data. To alleviate these problems this work proposes two new approaches for event timeline visualization. First, novel folding strategies for event timelines facilitate visual scalability and provide powerful overviews of performance data at the same time. Second, this work presents an effective approach that automatically identifies and highlights several types of performance critical sections in an application run. This approach identifies time dominant functions of an application and subsequently uses them to analyze runtime imbalances throughout the application run. Intuitive visualizations present the resulting runtime variations and guide the analyst to performance hot spots. Evaluations with benchmarks and real-world applications assess all introduced techniques. The effectiveness of the comparison approaches is demonstrated by showing automatically detected performance issues and structural differences between different versions of applications and across parallel execution streams. Case studies showcase the capabilities of the event timeline visualization techniques by demonstrating scalable performance data visualizations and detecting performance problems and code inefficiencies in real-world applications.