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BERICHTSRAHMEN NACHHALTIGE KOMMUNE AUF BASIS DES DNK Berichtsrahmen nachhaltige Kommune auf Basis des DNK / Pahl, Marc-Oliver (Rights reserved) ( -

Clock Error Analysis of Common Time of Flight based Positioning Methods

Today, many applications such as production or rescue settings rely on highly accurate entity positioning. Advanced Time of Flight (ToF) based positioning methods provide highaccuracy localization of entities. A key challenge for ToF based positioning is to synchronize the clocks between the participating entities. This paper summarizes and analyzes ToA and TDoA methods with respect to clock error robustness. The focus is on synchronization-less methods, i.e. methods which reduce the infrastructure requirement significantly. We introduce a unified notation to survey and compare the relevant work from literature. Then we apply a clock error model and compute worst case location-accuracy errors. Our analysis reveals a superior error robustness against clock errors for so called Double-Pulse methods when applied to radio based ToF positioningComment: Published in IEEEXplore: https://ieeexplore.ieee.org/abstract/document/891177

A Generalized TDoA/ToA Model for ToF Positioning

Many applications require positioning. Time of Flight (ToF) methods calculate distances by measuring the propagation time of signals. We present a novel ToF localization method. Our new approach works infrastructure-less, without pre-defined roles like Anchors or Tags. It generalizes existing synchronization-less Time Difference of Arrival (TDoA) and Time of Arrival (ToA) algorithms. We show how known algorithms can be derived from our new method. A major advantage of our approach is that it provides a comparable or better clock error robustness, i.e. the typical errors of crystal oscillators have negligible impact for TDoA and ToA measurements. We show that our channel usage is for most cases superior compared to the state-of-the art.Comment: Published in IEEEXplore: https://ieeexplore.ieee.org/abstract/document/891174

Cybercopters Swarm: Immersive analytics for alerts classification based on periodic data

This paper assesses the usefulness of an interactive and navigable 3D environment to help decision-making in cybersecurity. Malware programs frequently emit periodic signals in network logs; however, normal periodical network activities, such as software updates and data collection activities, mask them. Thus, if automatic systems use periodicity to successfully detect malware, they also detect ordinary activities as suspicious ones and raise false positives. Hence, there is a need to provide tools to sort the alerts raised by such software. Data visualizations can make it easier to categorize these alerts, as proven by previous research. However, traditional visualization tools can struggle to display a large amount of data that needs to be treated in cybersecurity in a clear way. In response, this paper explores the use of Immersive Analytics to interact with complex dataset representations and collect cues for alert classification. We created a prototype that uses a helical representation to underline periodicity in the distribution of one variable of a dataset. We tested this prototype in an alert triage scenario and compared it with a state-of-the-art 2D visualization with regard to the visualization efficiency, usability, workload, and flow induced

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Data-centric service-oriented management of things

Abstract-With the Internet of Things, more and more devices become remotely manageable. The amount and heterogeneity of managed devices make the task of implementing management functionality challenging. Future Pervasive Computing scenarios require implementing a plethora of services to provide management functionality. With growing demand on services, reducing the emerging complexity becomes increasingly important. A simple-to-use programming model for implementing complex management scenarios is essential to enable developers to create the growing amount of required management software at high quality. The paper presents how data-centric mechanisms, as known from network management, can be utilized to create a serviceoriented architecture (SOA) for management services. The resulting shift of complexity from access functionality towards data structures introduces new flexibility and facilitates the programming of management applications significantly. This is evaluated with a user study on the reference implementation

Training and Data Analysis use cases for Cybersecurity through Mixed Reality Applications

Chaire Cyber CNI – Cybersecurity for Critical Networked InfrastructuresInternational audienceIn this paper, we will discuss our point of view of the use of Mixed Environments for Cybersecurity, especially for training and data analysis purposes. We will argue that Collaborative Mixed Environments could merge training and analysis approaches by providing users with several points of view on cyber situations