Wireless sensor networks have arrived because of further developments of themodern Internet, and this has been considered to be one of the most importanttechnologies of the 21st century. Currently, the wireless sensor network has becomean important technology in a variety of areas and is widely used in thefield of national defense, national security, environmental monitoring, trafficmanagement, anti-terrorism, anti-disaster, and so on. The majority of these applicationsrequire real-time communication as the WSNs are required to sendthe data to the data center within a specified time. In order to meet the real-timedemand for wireless sensor networks, this work mainly focuses on the analysisand measurement of the end-to-end delay, including both single-hop and multihopdelays. This thesis first analyzes the composition of the end-to-end delayand then describes the end-to-end delay measurement algorithms and methods.The measurement is implemented in TelosB motes within TinyOS. Finally thereport will show the evaluation of the end-to-end delay in wireless sensor networks.

In this thesis, we examine the architecture and the security framework underlying the Android operating system. We explore existing Android end-to-end encrypted (E2EE) messaging applications and derive four categories of common issues that are applicable to these applications. We then provide an overview of the known issue of privilege escalation wherein a malicious privileged application can utilize inter-process communication techniques to send protected data to an unauthorized application on a user’s device. We demonstrate through a proof of concept how this behavior can be achieved in real applications, and we suggest potential countermeasures that can help prevent this issue. Furthermore, in the interest of diminishing the common issues that are applicable to E2EE messaging applications, we propose a new design for such applications that employs some of the principal security features offered by the Android operating system. We explain how our design can help eliminate trust-related issues associated with such applications, as well as how it can help minimize issues in other categories. Finally, we demonstrate how our proposed design can be used in practice by implementing a proof of concept.

This thesis deals with the controlling environment in the Revenue Assurance team of the telecommunication company Vodafone Inc. Its main goals are the suggestion of realizing and implementation of a new controlling process in Revenue Assurance. The individual goals of the thesis are introducing the term Revenue Assurance, so that the reader would understand the basic connection, and explanation of understanding the Revenue Assurance concept in other companies. The term is further compared to other controlling concepts such as controlling, risk management and internal audit, and the main differences among them are stated. Additionally, it explains the connection between the Revenue Assurance concept and the Sarbanes-Oxley Act. This thesis' part is then followed by an explanation of understanding the Revenue Assurance concept in the telecommunication company Vodafone CZ. The key part of the thesis is the description of the suggestion, realizing and implementation of the control into the company process. This part is preceded by a descriptive part dealing with description of the chosen method and the analysis of a telecommunication environment. The final part then deals with the question if the control has been implemented successfully and how it was working during its existence. Finally, I state the benefit for the company according to the number of the cases and the revealed amount of revenue leakage.

In this paper, we introduce a novel congestion control algorithm for TCP over multihop IEEE 802.11 wireless networks implementing rate-based scheduling of transmissions within the TCP congestion window. We show how a TCP sender can adapt its transmission rate close to the optimum using an estimate of the current 4-hop propagation delay and the coefficient of variation of recently measured round-trip times. The novel TCP variant is denoted as TCP with Adaptive Pacing (TCP-AP). Opposed to previous proposals for improving TCP over multihop IEEE 802.11 networks, TCP-AP retains the end-to-end semantics of TCP and does neither rely on modifications on the routing or the link layer nor requires cross-layer information from intermediate nodes along the path. A comprehensive simulation study using ns-2 shows that TCP-AP achieves up to 84% more goodput than TCP NewReno, provides excellent fairness in almost all scenarios, and is highly responsive to changing traffic conditions.

The dissertation focuses on detecting contextual outliers from heterogeneous data sources. Modern sensor-based applications such as Internet of Things (IoT) applications and autonomous vehicles are generating a huge amount of heterogeneous data including not only the structured multi-variate data points, but also other complex types of data such as time-stamped sequence data and image data. Detecting outliers from such data sources is critical to diagnose and fix malfunctioning systems, prevent cyber attacks, and save human lives. The outlier detection techniques in the literature typically are unsupervised algorithms with a pre-defined logic, such as, to leverage the probability density at each point to detect outliers. Our analysis of the modern applications reveals that this rigid probability density-based methodology has severe drawbacks. That is, low probability density objects are not necessarily outliers, while the objects with relatively high probability densities might in fact be abnormal. In many cases, the determination of the outlierness of an object has to take the context in which this object occurs into consideration. Within this scope, my dissertation focuses on four research innovations, namely techniques and system for scalable contextual outlier detection from multi-dimensional data points, contextual outlier pattern detection from sequence data, contextual outlier image detection from image data sets, and lastly an integrative end-to-end outlier detection system capable of doing automatic outlier detection, outlier summarization and outlier explanation. 1. Scalable Contextual Outlier Detection from Multi-dimensional Data. Mining contextual outliers from big datasets is a computational expensive process because of the complex recursive kNN search used to define the context of each point. In this research, leveraging the power of distributed compute clusters, we design distributed contextual outlier detection strategies that optimize the key factors determining the efficiency of local outlier detection, namely, to localize the kNN search while still ensuring the load balancing. 2. Contextual Outlier Detection from Sequence Data. For big sequence data, such as messages exchanged between devices and servers and log files measuring complex system behaviors over time, outliers typically occur as a subsequence of symbolic values (or sequential pattern), in which each individual value itself may be completely normal. However, existing sequential pattern mining semantics tend to mis-classify outlier patterns as typical patterns due to ignoring the context in which the pattern occurs. In this dissertation, we present new context-aware pattern mining semantics and then design efficient mining strategies to support these new semantics. In addition, methodologies that continuously extract these outlier patterns from sequence streams are also developed. 3. Contextual Outlier Detection from Image Data. An image classification system not only needs to accurately classify objects from target classes, but also should safely reject unknown objects that belong to classes not present in the training data. Here, the training data defines the context of the classifier and unknown objects then correspond to contextual image outliers. Although the existing Convolutional Neural Network (CNN) achieves high accuracy when classifying known objects, the sum operation on multiple features produced by the convolutional layers causes an unknown object being classified to a target class with high confidence even if it matches some key features of a target class only by chance. In this research, we design an Unknown-aware Deep Neural Network (UDN for short) to detect contextual image outliers. The key idea of UDN is to enhance existing Convolutional Neural Network (CNN) to support a product operation that models the product relationship among the features produced by convolutional layers. This way, missing a single key feature of a target class will greatly reduce the probability of assigning an object to this class. To further improve the performance of our UDN at detecting contextual outliers, we propose an information-theoretic regularization strategy that incorporates the objective of rejecting unknowns into the learning process of UDN. 4. An End-to-end Integrated Outlier Detection System. Although numerous detection algorithms proposed in the literature, there is no one approach that brings the wealth of these alternate algorithms to bear in an integrated infrastructure to support versatile outlier discovery. In this work, we design the first end-to-end outlier detection service that integrates outlier-related services including automatic outlier detection, outlier summarization and explanation, human guided outlier detector refinement within one integrated outlier discovery paradigm. Experimental studies including performance evaluation and user studies conducted on benchmark outlier detection datasets and real world datasets including Geolocation, Lighting, MNIST, CIFAR and the Log file datasets confirm both the effectiveness and efficiency of the proposed approaches and systems.

Paper forms are a commonly used format for collecting information, including information that ultimately will be added to a digital database. This work focuses on the automatic extraction of information from form images. It examines what can be achieved at parsing forms without any textual information. The resulting model, FUDGE, shows that computer vision alone is reasonably successful at the problem. Drawing from the strengths and weaknesses of FUDGE, this work also introduces a novel model, Dessurt, for end-to-end document understanding. Dessurt performs text recognition implicitly and is capable of outputting arbitrary text, making it a more flexible document processing model than prior methods. Dessurt is capable of parsing the entire contents of a form image into a structured format directly, achieving better performance than FUDGE at this task. Also included is a technique to generate synthetic handwriting, which provides synthetic training data for Dessurt.

Due to its ubiquitous availability, Short Message Service (SMS), first introduced in the 1980s, became not only the most popular way of communication, but also stimulated the development of SMS-based value added services. This application-to-person traffic is delivered to end users through SMS aggregators who provide the link between service providers and mobile carriers. In order to perform optimal traffic routing, the aggregators need to estimate the quality of each potential international route to the specified destination. The evaluation criteria include end-to-end delivery time, as well as correct verification of delivered data. This thesis suggests a method of quality of service (QoS) assessment for international SMS service which combines two types of tests, end-to-end delay measurements and various verification tests. A prototype of the testing system for international SMS service was developed to generate SMS traffic, collect and analyze results, and evaluate the experienced QoS of the SMS route used in accordance with the proposed approach. As a part of end-to- end delay measurement tests, SMS traffic was sent to Singtel network in Singapore along two routes. The verification tests were executed via different routes to two mobile networks: Singtel and Tele2 (Sweden). The results of the performed measurements determined the route with the highest QoS, i.e. the one with bigger bottleneck bandwidth and lower data loss rate. The prototype of the SMS testing system can be used by SMS aggregators to verify delivery of a SMS message, check the integrity of the message, figure out interconnection type of the route supplier with the destination carrier and to identify the presence of load balancers in the path. The prototype also makes it possible to compare end-to-end delay times of several routes and compute bottleneck values for each of the tested routes.

Imaging systems have long been designed in separated steps: the experience-driven optical design followed by sophisticated image processing. Such a general-propose approach achieves success in the past but left the question open for specific tasks and the best compromise between optics and post-processing, as well as minimizing costs. Driven from this, a series of works are proposed to bring the imaging system design into end-to-end fashion step by step, from joint optics design, point spread function (PSF) optimization, phase map optimization to a general end-to-end complex lens camera. To demonstrate the joint optics application with image recovery, we applied it to flat lens imaging with a large field of view (LFOV). In applying a super-resolution single-photon avalanche diode (SPAD) camera, the PSF encoded by diffractive op tical element (DOE) is optimized together with the post-processing, which brings the optics design into the end-to-end stage. Expanding to color imaging, optimizing PSF to achieve DOE fails to find the best compromise between different wavelengths. Snapshot HDR imaging is achieved by optimizing a phase map directly. All works are demonstrated with prototypes and experiments in the real world. To further compete for the blueprint of end-to-end camera design and break the limits of a simple wave optics model and a single lens surface. Finally, we propose a general end-to-end complex lens design framework enabled by a differentiable ray tracing image formation model. All works are demonstrated with prototypes and experiments in the real world. Our frameworks offer competitive alternatives for the design of modern imaging systems and several challenging imaging applications.

Local-first software has the potential to offer users a great experience by combining the best aspects of traditional applications with cloud-based applications. However, not much has been documented regarding developing backends for local-first software, particularly one that is scalable while still supporting end-to-end encryption. This thesis presents a backend architecture that was then implemented and evaluated. The implementation was shown to be scalable and was able to maintain an estimated end-to-end latency of around 30-50ms as the number of simulated clients increased. The architecture supports end-to-end encryption to offer user privacy and to ensure that neither cloud nor service providers can access user data. Furthermore, by occasionally performing snapshots the encryption overhead was shown to be manageable compared to the raw data, at around 18.2% in the best case and 118.9% when using data from automerge-perf, a standard benchmark. Lastly, the processing times were shown to be upwards of 50 times faster when using snapshots compared to handling individual changes.

None of the three largest webmail service providers (serving over 1 billion users) support end-to-end message encryption. Encrypted email has never seen mass adoption because it is prohibitive for non-experts to use. Private WebMail (Pwm) is our extension to popular webmail systems that lets users easily encrypt sensitive messages without having to first contact the recipient and share information. It is designed to spread quickly in a grassroots fashion so that a user receiving their first encrypted message can quickly and easily start using the system. This thesis describes the design and implementation of Pwm, then measures its usability through analysis and a user study.