The use of The Three Stooges’ films as a source of data in an introductory statistics class is described. The Stooges’ films are separated into three populations. Using these populations, students may conduct hypothesis tests with data they collect.

Providing efficient data transport is one of the uppermost objectives in the design of wireless sensor networks (WSNs) since the primary role for each sensor is to report the sensed data to the data sink(s). This thesis focuses on designing efficient data transport schemes for WSNs in the dimensions of energy consumption and time respectively. The developed schemes can be directly applied in a number of applications such as intrusion detection, target tracking, environment monitoring, etc., and can be further extended to underwater acoustic sensor networks and unmanned aerial vehicles (UAVs) networks. With the development of WSN technologies, new challenging research problems such as real-time streaming data gathering and intelligent data communication are emerging. This thesis provides useful foundation for designing next-generation data transport schemes for WSNs. Energy is the most important resource in WSNs because sensor nodes are commonly powered by small batteries, and energy is directly related to the lifetime of nodes and the network. In this thesis, energy-efficient data transport schemes are designed for two major types of WSNs: event-driven sensor networks and time-driven sensor networks. A novel on-line routing scheme called EBGR (Energy-efficient Beaconless Geographic Routing) is designed for event-driven sensor networks characterized by dynamic network topology. The main advantage of EBGR is that it can provide energy-efficient sensor-to-sink routing without any prior neighborhood knowledge. Moreover, the total energy consumption for sensor-to-sink data delivery under EBGR has an upper bound. Time-driven sensor networks, in which all sensors periodically report the sensed data to the sink(s), have been widely used for environment monitoring applications. Unbalanced energy consumption is an inherent problem in time-driven sensor networks. An efficient data gathering scheme, called EBDG (Energy-Balanced Data Gathering), is designed to balance energy consumption for the goal of maximizing network lifetime. Combing all advantages of corona-based network division,mixed-routing and data aggregation, EBDG can prolong network lifetime by an order of magnitude compared with conventional schemes. Time-efficient data transport is another critical issue in WSNs since the data generated by the sensor nodes may become outdated after a certain time interval. This thesis focuses on the problem of providing real-time data gathering in time-driven sensor networks. A novel data gathering scheme based on random access is proposed with the objective to minimize the average duration for completing one round of data gathering. Fully localized solutions have been designed for both linear networks and tree networks. A simple data gathering protocol called RADG (Random Access Data Gathering) is designed. Simulation results show that RADG outperforms CSMA based schemes when the size of the data packets is small. / Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2009

The main focus of this thesis is on data gathering process in the chosen company. The main goal of the thesis is to map the process of data gathering, analyse the current situation and suggest improvements based on revealed problems. The thesis is divided into two parts. The first part focuses on the theoretical foundation of data gathering. There are mentions of the basics of quality management and parts of the ISO 9001:2015 which can be applied to data gathering systems, process and the tools for its mapping and the methods of data gathering. There is a part which focuses on FMEA method, which is used for the analysis of the current state of the process. The second part introduces chosen company. There are used a few tools to map the process with the focus on spots where the data are gathered. According to the FMEA method and its process, there is an analysis of the current system. The attention is paid to the problematic parts of the process of data gathering. Then there can be found few propositions how to make the data gathering process in the company better.

Master of Science / Department of Computing and Information Sciences / Gurdip Singh / Wireless sensor networks are characterized by severe energy constraints, one to many flows and low rate redundant data. Most of the routing algorithms for traditional networks are address centric, and the ad hoc nature of wireless sensor network makes them unsuitable for practical applications. Also the algorithms designed for mobile ad hoc networks are unsuitable for wireless sensor networks due to severe energy constraints that require nodes to perform for months with limited resources, as well as the low data rate which the constraint implies. This thesis examines a model driven data gathering algorithm framework for wireless sensor networks. It was designed with a goal to decrease the overall cost in transmission by lowering the number of messages transmitted in the network. A combination of data- centric and address-centric approaches was used as guidelines during the design process. A shortest path heuristic where intermediate nodes forward interest messages whenever it is of lower cost is one of the heuristics used. Another heuristic used is the greedy incremental approach to build a lower cost tree from a graph with various producers and consumers. A cost division heuristic is used to divide cost of shared path into distinct paths as the path forks in a tree. This thesis analyzes the effects of these heuristics on the performance of the algorithm and how it lowers the overall cost with the addition of each heuristic.

Unmanned Aerial Vehicles (UAV) are agile and are able to fly in and out of areas that are either dangerous for humans or have complex terrains making ground robots unsuitable. For their autonomous operation, the ability to explore unmapped areas is imperative. This has applications in data gathering tasks, search and rescue etc.  The objective of this thesis is to ascertain that it is, in fact, possible and feasible to use UAVs equipped with 2D laser scanners to perform autonomous exploration tasks in indoor environments. The system is evaluated by testing it in different simulated and real environments. The results presented show that the system is capable of completely and safely exploring unmapped and/or unexplored regions. / Obemannade flygfarkoster (UAV) är smidiga och kan flyga in och ut ur områden som är farliga för människor eller är svårtillgängliga för markrobotar. För att nå höga nivåer av autonomitet måste en UAV kunna utforska och kartlägga ett okänt område på egen hand. Det finns flera tillämpningar för detta, så som räddningsuppdrag och datainsamling. Målet med denna avhandling är att visa attdet är möjligt att använda en UAV utrustad med 2D-laserskannrar för att utföra autonoma kartläggningsuppdrag i inomhusmiljöer. Systemet utvärderas genom att testa det i olika simulerade och verkliga miljöer. De presenterade resultaten visar att systemet kan utforska okända områden på ett säkert sätt.

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L'utilisation des réseaux de capteurs sans fil (WSN) ne cesse d'augmenter au point de couvrir divers domaines et applications. Cette tendance est supportée par les avancements techniques achevés dans la conception des capteurs, qui ont permis de réduire le coût ainsi que la taille de ces composants. Toutefois, il reste plusieurs défis qui font face au déploiement et au bon fonctionnement de ce type de réseaux et qui parviennent principalement de la limitation des ressources de capteurs ainsi de l'imperfection des données collectées. Dans cette thèse, on adresse le problème de collecte de données et de détection d'anomalies dans les réseaux de capteurs. Nous visons à assurer ces deux fonctionnalités tout en économisant l'utilisation des ressources de capteurs et en prolongeant la durée de vie de réseaux. Tout au long de ce travail, nous présentons plusieurs solutions qui permettent une collecte efficace de données de capteurs ainsi que une bonne détection des éventuelles anomalies. Dans notre première contribution, nous décrivons une solution basée sur la technique Compressive Sensing (CS) qui permet d'équilibrer le trafic transmis par les nœuds dans le réseau. Notre approche diffère des solutions existantes par la prise en compte de la corrélation temporelle ainsi que spatiale dans le processus de décompression des données. De plus, nous proposons une nouvelle formulation pour détecter les anomalies. Les simulations réalisées sur des données réelles prouvent l'efficacité de notre approche en termes de reconstruction de données et de détection d'anomalies par rapport aux approches existantes. Pour mieux optimiser l'utilisation des ressources de WSNs, nous proposons dans une deuxième contribution une solution de collecte de données et de détection d'anomalies basée sur la technique Matrix Completion (MC) qui consiste à transmettre un sous ensemble aléatoire de données de capteurs. Nous développons un algorithme qui estime les mesures manquantes en se basant sur plusieurs propriétés des données. L'algorithme développé permet également de dissimuler les anomalies de la structure normale des données. Cette solution est améliorée davantage dans notre troisième contribution, où nous proposons une formulation différente du problème de collecte de données et de détection d'anomalies. Nous reformulons les connaissances a priori sur les données cibles par des contraintes convexes. Ainsi, les paramètres impliqués dans l'algorithme développé sont liés a certaines propriétés physiques du phénomène observé et sont faciles à ajuster. Nos deux approches montrent de bonnes performances en les simulant sur des données réelles. Enfin, nous proposons dans la dernière contribution une nouvelle technique de collecte de données qui consiste à envoyer que les positions les plus importantes dans la représentation parcimonieuse des données uniquement. Nous considérons dans cette approche le bruit qui peut s'additionner aux données reçues par le nœud collecteur. Cette solution permet aussi de détecter les pics dans les mesures prélevées. En outre, nous validons l'efficacité de notre solution par une analyse théorique corroborée par des simulations sur des données réelles / The use of Wireless Sensor Networks (WSN)s is steadily increasing to cover various applications and domains. This trend is supported by the technical advancements in sensor manufacturing process which allow a considerable reduction in the cost and size of these components. However, there are several challenges facing the deployment and the good functioning of this type of networks. Indeed, WSN's applications have to deal with the limited energy, memory and processing capacities of sensor nodes as well as the imperfection of the probed data. This dissertation addresses the problem of collecting data and detecting anomalies in WSNs. The aforementioned functionality needs to be achieved while ensuring a reliable data quality at the collector node, a good anomaly detection accuracy, a low false alarm rate as well as an efficient energy consumption solution. Throughout this work, we provide different solutions that allow to meet these requirements. Foremost, we propose a Compressive Sensing (CS) based solution that allows to equilibrate the traffic carried by nodes regardless their distance from the sink. This solution promotes a larger lifespan of the WSN since it balances the energy consumption between sensor nodes. Our approach differs from existing CS-based solutions by taking into account the sparsity of sensory representation in the temporal domain in addition to the spatial dimension. Moreover, we propose a new formulation to detect aberrant readings. The simulations carried on real datasets prove the efficiency of our approach in terms of data recovering and anomaly detection compared to existing solutions. Aiming to further optimize the use of WSN resources, we propose in our second contribution a Matrix Completion (MC) based data gathering and anomaly detection solution where an arbitrary subset of nodes contributes at the data gathering process at each operating period. To fill the missing values, we mainly relay on the low rank structure of sensory data as well as the sparsity of readings in some transform domain. The developed algorithm also allows to dissemble anomalies from the normal data structure. This solution is enhanced in our third contribution where we propose a constrained formulation of the data gathering and anomalies detection problem. We reformulate the textit{a prior} knowledge about the target data as hard convex constraints. Thus, the involved parameters into the developed algorithm become easy to adjust since they are related to some physical properties of the treated data. Both MC based approaches are tested on real datasets and demonstrate good capabilities in terms of data reconstruction quality and anomaly detection performance. Finally, we propose in the last contribution a position based compressive data gathering scheme where nodes cooperate to compute and transmit only the relevant positions of their sensory sparse representation. This technique provide an efficient tool to deal with the noisy nature of WSN environment as well as detecting spikes in the sensory data. Furthermore, we validate the efficiency of our solution by a theoretical analysis and corroborate it by a simulation evaluation

This thesis deals with the possibilities of using methods of Competitive Intelligence in practice. The main objective of thesis is to design and realize system for collecting, analysis and distribution of data to end users at Crystal Glamour, Ins. To achieve this goal was set several sub - objectives. Analyze the company activities, find its strengths and weaknesses, identify opportunities and threats of company. Based on these data are identified goals of CI and information sources that cover the domain of interest. Then, using software applications from Tovek, spol. Ltd., the relevant articles are searched and analyzed, which are then distributed by the well-arranged report to individual users.

Wireless sensor networks (WSNs) typically consist of a significant number of inexpensive sensor nodes, each of which is powered by a battery or another finite energy source that is difficult to replace because of the environment they are in or the cost of doing so. The applications of WSNs include military surveillance, disaster management, target tracking and monitoring environmental conditions. In order to increase the lifespan of WSNs, energy-efficient sensing and communication approaches for sensor nodes are essential. Recently, there has been an increase in interest in using unmanned aerial vehicles (UAVs) as portable data collectors for ground sensor nodes in WSN. Several approaches to solving effective communication between sensor nodes and the fusion center have been investigated in this thesis. Because processing, sensing range, transmission bandwidth, and energy consumption are always limited, it is beneficial not to use all the information provided at each sensor node in order to prolong its life span and reduce communication costs. In order to address this problem, first, efficient measurement quantization techniques are proposed using a single fusion center and multiple sensors. The dynamic bit distribution is done among all the sensors and within the measurement elements. The problem is then expanded to include multiple fusion centers, and a novel algorithm is proposed to associate sensors to fusion centers. The bandwidth distribution for targets which are being monitored by several sensors is addressed. Additionally, how to use the situation in which the sensors are in the coverage radius of multiple fusion centers in order to share the targets between them is discussed. Finally, performance bounded data collection algorithms are proposed where the necessary accuracy for each target is specified. In order to determine the minimum number of data collectors needed and their initial placement, an algorithm is proposed. When there are fewer fixed data collectors than there are regions to collect the data from, a coverage path planning method is developed. Since the optimal solution requires an enormous computational requirement and not realistic for real-time online implementation, approximate algorithms are proposed for multi-objective integer optimization problems. In order to assess each suggested algorithm's effectiveness, many simulated scenarios are used together with baselines and simple existing methods. / Thesis / Doctor of Philosophy (PhD)

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