Quantifying the errors in animal contacts recorded by proximity loggers

9openInternationalBothAutomated contact detection by means of proximity loggers permits the measurement of encounters between individuals (animal-animal contacts) and the time spent by individuals in the proximity of a focal resource of interest (animal-fixed logger contacts). The ecological inference derived from contact detection is intrinsically associated with the distance at which the contact occurred. But no proximity loggers currently exist that record this distance and therefore all distance estimations are associated with error. Here we applied a probabilistic approach to model the relationship between contact detection and inter-logger distance, and quantify the associated error, on free-ranging animals in semi-controlled settings. The probability of recording a contact declined with the distance between loggers, and this decline was steeper for weaker radio transmission powers. Even when proximity loggers were adjacent, contact detection was not guaranteed, irrespective of the radio transmission power. Accordingly, the precision and sensitivity of the system varied as a function of inter-logger distance, radio transmission power, and experimental setting (e.g., depending on animal body mass and fine-scale movements). By accounting for these relationships, we were able to estimate the probability that a detected contact occurred at a certain distance, and the probability that contacts were missed (i.e., false negatives). These calibration exercises have the potential to improve the predictability of the study and enhance the applicability of proximity loggers to key wildlife management issues such as disease transmission rates or wildlife use of landscape features and resourcesopenOssi, F.; Focardi, S.; Tolhurst, B.; Picco, G.; Murphy, A.; Molteni, D.; Giannini, N.; Gaillard, J.M.; Cagnacci, F.Ossi, F.; Focardi, S.; Tolhurst, B.; Picco, G.; Murphy, A.; Molteni, D.; Giannini, N.; Gaillard, J.M.; Cagnacci, F

Quantifying the errors in animal contacts recorded by proximity loggers

Automated contact detection by means of proximity loggers permits the measurement of encounters between individuals (animal-animal contacts) and the time spent by individuals in the proximity of a focal resource of interest (animal-fixed logger contacts). The ecological inference derived from contact detection is intrinsically associated with the distance at which the contact occurred. But no proximity loggers currently exist that record this distance and therefore all distance estimations are associated with error. Here we applied a probabilistic approach to model the relationship between contact detection and inter-logger distance, and quantify the associated error, on free-ranging animals in semi-controlled settings. The probability of recording a contact declined with the distance between loggers, and this decline was steeper for weaker radio transmission powers. Even when proximity loggers were adjacent, contact detection was not guaranteed, irrespective of the radio transmission power. Accordingly, the precision and sensitivity of the system varied as a function of inter-logger distance, radio transmission power, and experimental setting (e.g., depending on animal body mass and fine-scale movements). By accounting for these relationships, we were able to estimate the probability that a detected contact occurred at a certain distance, and the probability that contacts were missed (i.e., false negatives). These calibration exercises have the potential to improve the predictability of the study and enhance the applicability of proximity loggers to key wildlife management issues such as disease transmission rates or wildlife use of landscape features and resources

The design of efficient and secure P2PSIP systems

Doktorgradsavhandling i informasjons- og kommunikasjonsteknologi, Universitetet i Agder, Grimstad, 201

Secure Communication in Disaster Scenarios

Während Naturkatastrophen oder terroristischer Anschläge ist die bestehende Kommunikationsinfrastruktur häufig überlastet oder fällt komplett aus. In diesen Situationen können mobile Geräte mithilfe von drahtloser ad-hoc- und unterbrechungstoleranter Vernetzung miteinander verbunden werden, um ein Notfall-Kommunikationssystem für Zivilisten und Rettungsdienste einzurichten. Falls verfügbar, kann eine Verbindung zu Cloud-Diensten im Internet eine wertvolle Hilfe im Krisen- und Katastrophenmanagement sein. Solche Kommunikationssysteme bergen jedoch ernsthafte Sicherheitsrisiken, da Angreifer versuchen könnten, vertrauliche Daten zu stehlen, gefälschte Benachrichtigungen von Notfalldiensten einzuspeisen oder Denial-of-Service (DoS) Angriffe durchzuführen. Diese Dissertation schlägt neue Ansätze zur Kommunikation in Notfallnetzen von mobilen Geräten vor, die von der Kommunikation zwischen Mobilfunkgeräten bis zu Cloud-Diensten auf Servern im Internet reichen. Durch die Nutzung dieser Ansätze werden die Sicherheit der Geräte-zu-Geräte-Kommunikation, die Sicherheit von Notfall-Apps auf mobilen Geräten und die Sicherheit von Server-Systemen für Cloud-Dienste verbessert

Storage-Centric Wireless Sensor Networks for Smart Buildings

In the first part of the dissertation, we propose a model-based systems design framework, called WSNDesign, to facilitate the design and implementation of wireless sensor networks for Smart Buildings. We apply model-based systems engineering principles to enhance model reusability and collaboration among multiple engineering domains. Specifically, we describe a hierarchy of model libraries to model various behaviors and structures of sensor networks in the context of Smart Buildings, and introduce a system design flow to compose both continuous-time and event-triggered modules to develop applications with support for performance evaluation. WSNDesign can obtain early feedback and high-confidence evaluation of a design without requiring any intrusive and costly deployment. In addition, we develop a graphical tool that exposes a sequence of design choices to system designers, and provides instant feedback about the influence of a design decision on the complexity of system analysis. Our tool can facilitate comprehensive analysis and bring competitive advantage to the systems design workflow by reducing costly unanticipated behaviors. One of the main challenges to design efficient sensor networks is to collect and process the data generated by various sensor motes in Smart Buildings efficiently. To make this task easier, we provide an abstraction for data collection and retrieval in the second part of the dissertation. Specifically, we design and implement a distributed database system, called HybridDB, for application development. HybridDB enables sensors to store large-scale datasets in situ on local NAND flash using a novel resource-aware data storage system, and can process typical queries in sensor networks extremely efficiently. In addition, HybridDB supports incremental $\epsilon$-approximate querying that enables clients to retrieve a just-sufficient set of sensor data by issuing refinement and zoom-in sub-queries to search events and analyze sensor data efficiently. HybridDB can always return an approximate dataset with guaranteed maximum absolute ($L_\infty$-norm) error bound, after applying temporal approximate locally on each sensor, and spatial approximate in the neighborhood on the proxy. Furthermore, HybridDB exploits an adaptive error distribution mechanism between temporal approximate and spatial approximate for trade-offs of energy consumption between sensors and the proxy, and response times between the current sub-query and the following sub-queries. The implementation of HybridDB in TinyOS 2.1 is transformed and imported to WSNDesign as a part of the model libraries

Hotspots of soil water movement induced by vegetation canopies

Vegetation and soil form a highly interactive system, within which water is one of the most important factors. By the redistribution of precipitation and its separation into interception, throughfall and stemflow, vegetation canopies introduce a strong small-scale heterogeneity to downwards-directed water fluxes in forests. This could importantly affect subsequent hydrological and biogeochemical processes. In my study, I addressed the formation of patterns and hotspots of below-canopy precipitation and their imprint on soil water conditions. In a comprehensive experimental approach, I used a high-resolution statistical design to capture overall patterns, and hotspot locations trees to identify extreme impacts of canopy-induced water flow on soil water and properties. In Chapter 1, I show that soil properties, instead of net precipitation patterns, most prominently shaped spatial patterns of soil water content. Soil properties, yet, showed to be spatially organized due to the position of trees, forming areas of enhanced soil drainage around the trunks. In Chapter2, the effects of tree, neighborhood and stand properties on stemflow were identified using linear mixed effects models. Stand density and species diversity increased stemflow due to high woody surface area. The temporal stability of stemflow variation indicates that vegetational impacts are highly relevant. Chapter 3 assesses the spatial distribution of infiltration from stemflow and throughfall and the impact of hotspots on soil properties. Stemflow infiltration areas proved to be extremely small and infiltration depth high. These hotspots formed distinct soil microsites at the base of trees by accelerating soil formation. Thus, vegetation induces water flow hotspots from the canopy to below the rooting zone. This is likely to influence hydrological responses and the separation of rainfall to plant available water in contrast to deep percolation and groundwater recharge

Optimization of Safety Control System for Civil Infrastructure Construction Projects

Labor-intensive repetitive activities are common in civil construction projects. Construction workers are prone to developing musculoskeletal disorders-related injuries while performing such tasks. The government regulatory agency provides minimum safety requirement guidelines to the construction industry that might not be sufficient to prevent accidents and injuries in a construction site. Also, the regulations do not provide insight into what can be done beyond the mandatory requirements to maximize safety and underscore the level of safety that can be attained and sustained on a site. The research addresses the aforestated problem in three stages: (i) identification of theoretical maximum attainable level of safety, safety frontier, (ii) identification of underlying system inefficiencies and operational inefficiencies, and (iii) identification of achievable level of safety, sustainable safety. The research proposes a novel approach to identify the safety frontier by kinetic analysis of the human body while performing labor-intensive repetitive tasks. The task is a combination of different unique actions, which further involve several movements. For identifying a safe working procedure, each movement frame needs to be analyzed to compute the joint stress. Multiple instances of repetitive tasks can then be analyzed to identify unique actions exerting minimum stress on joints. The safety frontier is a combination of such unique actions. For this, the research proposes to track the skeletal positional data of workers performing different repetitive tasks. Unique actions involved in all tasks were identified for each movement frame. For this, several machine learning techniques were implemented. Moreover, the inverse dynamics principle was used to compute the stress induced by essential joints. In addition to the inverse dynamics principle, several machine learning algorithms were implemented to predict lower back moments. Then, the safety frontier was computed, combining the unique actions exerting minimum stress to the joints. Furthermore, the research conducted a questionnaire survey with construction experts to identify the factors affecting system inefficiencies that are not under the control of the project management team and operational inefficiencies that are under control. Then, the sustainable safety was computed by adding system inefficiencies to the safety frontier and removing operational inefficiencies from observed safety. The research validated the applicability of the proposed methodology in a real construction site. The application of random forest classifier, one-vs-rest classifier, and support vector machine approach were validated with high accuracy (\u3e95%). Similarly, random forest regressor, lasso regression, gradient boosting evaluation, stacking regression, and deep neural network were explored to predict the lower back moment. Random forest regressor and deep neural network predicted the lower back moment with an explained variance of 0.582 and 0.700, respectively. The computed safety frontier and sustainable safety can potentially facilitate the construction sector to improve safety strategies by providing a higher safety benchmark for monitoring, including the ability to monitor postural safety in real-time. Moreover, different industrial sectors such as manufacturing and agriculture can implement the similar approach to identify safe working postures for any labor-intensive repetitive task