The current state of the industry’s methods of collecting background data reflecting diagnostic and usage information are often opaque and require users to place a lot of trust in the entity receiving the data. For vendors, having a centralized database of potentially sensitive data is a privacy protection headache and a potential liability should a breach of that database occur. Unfortunately, high profile privacy failures are not uncommon, so many individuals and companies are understandably skeptical and choose not to contribute any information. It is a shame, since the data could be used for improving reliability, or getting stronger security, or for valuable academic research into real-world usage patterns. We propose, implement and evaluate a framework for non-realtime anonymous data collection, aggregation for analysis, and feedback. Departing from the usual “trusted core” approach, we aim to maintain reporters’ anonymity even if the centralized part of the system is compromised. We design a peer-to-peer mix network and its protocol that are tuned to the properties of background diagnostic traffic. Our system delivers data to a centralized repository while maintaining (i) source anonymity, (ii) privacy in transit, and (iii) the ability to provide analysis feedback back to the source. By removing the core’s ability to identify the source of data and to track users over time, we drastically reduce its attractiveness as a potential attack target and allow vendors to make concrete and verifiable privacy and anonymity claims.

The ability to transfer data reliably and with low delay over an unreliable service is intrinsic to a number of emerging technologies, including digital video broadcasting, over-the-air software updates, public/private cloud storage, and, recently, wireless vehicular networks. In particular, modern vehicles incorporate tens of sensors to provide vital sensor information to electronic control units (ECUs). In the current architecture, vehicle sensors are connected to ECUs via physical wires, which increase the cost, weight and maintenance effort of the car, especially as the number of electronic components keeps increasing. To mitigate the issues with physical wires, wireless sensor networks (WSN) have been contemplated for replacing the current wires with wireless links, making modern cars cheaper, lighter, and more efficient. However, the ability to reliably communicate with the ECUs is complicated by the dynamic channel properties that the car experiences as it travels through areas with different radio interference patterns, such as urban versus highway driving, or even different road quality, which may physically perturb the wireless sensors. This thesis develops a suite of reliable and efficient communication schemes built upon feedback-based rateless codes, and with a target application of vehicular networks. In particular, we first investigate the feasibility of multi-hop networking for intra-car WSN, and illustrate the potential gains of using the Collection Tree Protocol (CTP), the current state of the art in multi-hop data aggregation. Our results demonstrate, for example, that the packet delivery rate of a node using a single-hop topology protocol can be below 80% in practical scenarios, whereas CTP improves reliability performance beyond 95% across all nodes while simultaneously reducing radio energy consumption. Next, in order to migrate from a wired intra-car network to a wireless system, we consider an intermediate step to deploy a hybrid communication structure, wherein wired and wireless networks coexist. Towards this goal, we design a hybrid link scheduling algorithm that guarantees reliability and robustness under harsh vehicular environments. We further enhance the hybrid link scheduler with the rateless codes such that information leakage to an eavesdropper is almost zero for finite block lengths. In addition to reliability, one key requirement for coded communication schemes is to achieve a fast decoding rate. This feature is vital in a wide spectrum of communication systems, including multimedia and streaming applications (possibly inside vehicles) with real-time playback requirements, and delay-sensitive services, where the receiver needs to recover some data symbols before the recovery of entire frame. To address this issue, we develop feedback-based rateless codes with dynamically-adjusted nonuniform symbol selection distributions. Our simulation results, backed by analysis, show that feedback information paired with a nonuniform distribution significantly improves the decoding rate compared with the state of the art algorithms. We further demonstrate that amount of feedback sent can be tuned to the specific transmission properties of a given feedback channel.

The aim of this thesis is to design universal sensoric platform for data collection in manufacturing environment. Device is capable of collecting data from variety of sensors, but it is also able to control external systems. Platform is controled by Lua scripts. The scripts are generated in user friendly visual programming environment Blockly. Platform is based on WiFi module ESP-WROOM-02.

The diploma thesis deals with the development of a software library for aggregating data from electricity meters and sending data to a central station. The most used protocols in smart grids are described. The work deals in more detail with the most important parts of the DLMS/COSEM standard. The DLMS protocol is described, which ensures communication between clients and servers. Next, the COSEM object model is described, which specifies object modeling, for access to measuring devices, and the OBIS system, which determines the identification of data objects. The Gurux library, which specializes in intelligent meter readings, is also described. The results of the work describe the implementation and testing of a software library for aggregation of data from servers using the DLMS/COSEM protocol using the GURUX library. At the end of the work is described a series of tests that were performed using this software library.

This project includes a basic overview of concepts as is communication, data communication and distributed to systems. Further focuses on the technology use for remote data collection. Part is dedicated PLC technologies, which use systems for remote data collection. The thesis is also mentioned the concept of quality of electrical energy and standard ČSN EN 50160. The main part is focused on the design, implementation and measurement of the PLC network.

The subject of this Master thesis is to identify the current situation and find an optimal solutions for communication; especially for e-communication between Municipal District Prague 9 and citizens, based on outcomes from surveys conducted with the citizens, employees and observe other Municipal offices. The theoretical part is mainly focused on the interpretation of terms, some of the relevant types of e-communication, methodology and process of market research. In the practical part is depict the current situation, further analyzed the outcome from the conducted survey research and the resulted suggestions and recommendations. I strongly believe that the outcomes from this Master thesis will contribute to achieve a desired objective of Municipal District Prague 9, which is optimal satisfaction of their citizens.

This work deal with developing dedicated autonomous device for data collection. The work describes the approaches how to design that. Solve choosing of the sensor taking into his properties for this device. And regard on the comparison with similar devices on market according behavior and function. General concept of the suggested datalogger. The thesis contains hardware and software equipment of this device.

Les réseaux de capteurs sont utilisés dans l’IoT pour collecter des données. Cependant, une expertise envers les réseaux de capteurs est requise pour interagir avec ces infrastructures. Pour un ingénieur logiciel, cibler de tels systèmes est difficile. Les spécifications des plateformes composant l'infrastructure de capteurs les obligent à travailler à un bas niveau d'abstraction et à utiliser des plateformes hétérogènes. Cette fastidieuse activité peut conduire à un code exploitant de manière non optimisée l’infrastructure. En étant spécifiques à une infrastructure, ces applications ne peuvent également pas être réutilisées facilement vers d’autres infrastructures. De plus, le déploiement de ces applications est hors du champ de compétences d’un ingénieur logiciel car il doit identifier la ou les plateforme(s) requise(s) pour supporter l’application. Enfin, l’architecture peut ne pas être conçue pour supporter l’exécution simultanée d’application, engendrant des déploiements redondants lorsqu’une nouvelle application est identifiée. Dans cette thèse, nous présentons une approche qui supporte (i) la définition de politiques de collecte de données à haut niveau d’abstraction et réutilisables, (ii) leur déploiement sur une infrastructure hétérogène dirigée par des modèles apportés par des experts réseau et (iii) la composition automatique de politiques sur des infrastructures hétérogènes. De ces contributions, un ingénieur peut dès lors manipuler un réseau de capteurs sans en connaitre les détails, en réutilisant des abstractions architecturales disponibles lors de l'expression des politiques, des politiques qui pourront également coexister au sein d'un même réseau. / Sensing infrastructures are classically used in the IoT to collect data. However, a deep knowledge of sensing infrastructures is needed to properly interact with the deployed systems. For software engineers, targeting these systems is tedious. First, the specifies of the platforms composing the infrastructure compel them to work with little abstractions and heterogeneous devices. This can lead to code that badly exploit the network infrastructure. Moreover, by being infrastructure specific, these applications cannot be easily reused across different systems. Secondly, the deployment of an application is outside the domain expertise of a software engineer as she needs to identify the required platform(s) to support her application. Lastly, the sensing infrastructure might not be designed to support the concurrent execution of various applications leading to redundant deployments when a new application is contemplated. In this thesis we present an approach that supports (i) the definition of data collection policies at high level of abstraction with a focus on their reuse, (ii) their deployment over a heterogeneous infrastructure driven by models designed by a network export and (iii) the automatic composition of the policy on top of the heterogeneous sensing infrastructures. Based on these contributions, a software engineer can exploit sensor networks without knowing the associated details, while reusing architectural abstractions available off-the-shelf in their policy. The network will also be shared automatically between the policies.

One of the greatest challenges facing school staff is problem behavior in the classroom (Public Agenda, 2004). Children who engage in problem behavior in the classroom setting greatly challenge teachers and diminish the ability to learn. This study evaluated the effects of self-monitoring using the Individualized Behavior Rating Scale Tool (IBRST) on problem behavior and on-task behavior in a classroom setting using a multiple-baseline across participants design. This study also evaluated the extent to which students self-rating on the IBRST correlated with direct observation data. Results indicate that self-monitoring using the IBRST was an effective strategy for increasing on-task behavior and decreasing problem behavior for all three students. Results also indicate that the IBRST may be an accurate and reliable means of measuring data when direct observation data may not be feasible or possible. There were 56/60 perfect agreements, with the other four opportunities being only 1-pt value apart. Limitations and future research are discussed.

Data collection and online personalization has become essential part of modern marketing, and thus, embedded into consumer’s everyday life. This has emerged a lot of negative attention in the media and privacy concerns among consumers – however, their attitudes towards privacy seems to be controversial with lack of privacy enhancing behavior. The purpose of this study was to find out what is consumers take on online personalization, data collection and GDPR. In order to the tackle the causing reasons of such perceptions, focus group discussions were performed. The emerging thoughts were analyzed with the concepts of privacy paradox and information asymmetry – how structural imbalance between the advertisement network, companies and consumers impacted to their thinking about personalization and which factors caused the unwillingness to enhance one’s privacy, despite the attitudes that would predict different behavior. The results showed, that many respondents do not mind personalization if they perceive it relevant. However, the intrusive nature of its practices made the participants, directly or indirectly, reluctant towards it, as it was highlighted that it is not personalization per se that made the respondents uncomfortable, but how it was done. Due to the advertisement networks’ opaque nature, the participants founded challenging to comprehend how personalization was performed. Thus, conspiracy theories about surveillance, such as tapping via smartphone, were broad up to explain companies’ ability to know and target them so well. The main channel for companies to inform consumers about their privacy policy is terms and conditions. However, due to several reasons, the decision making for one’s privacy face many hinders, that may influence in how consumers perceive their privacy and how their personal data is collected and used. A controversiality between GDPR’s, companies’ and consumers’ view on privacy self-management is evident, as the regulation and companies rely too much on consumer’s own responsibility.