The Role of the Internet of Things in Network Resilience

Disasters lead to devastating structural damage not only to buildings and transport infrastructure, but also to other critical infrastructure, such as the power grid and communication backbones. Following such an event, the availability of minimal communication services is however crucial to allow efficient and coordinated disaster response, to enable timely public information, or to provide individuals in need with a default mechanism to post emergency messages. The Internet of Things consists in the massive deployment of heterogeneous devices, most of which battery-powered, and interconnected via wireless network interfaces. Typical IoT communication architectures enables such IoT devices to not only connect to the communication backbone (i.e. the Internet) using an infrastructure-based wireless network paradigm, but also to communicate with one another autonomously, without the help of any infrastructure, using a spontaneous wireless network paradigm. In this paper, we argue that the vast deployment of IoT-enabled devices could bring benefits in terms of data network resilience in face of disaster. Leveraging their spontaneous wireless networking capabilities, IoT devices could enable minimal communication services (e.g. emergency micro-message delivery) while the conventional communication infrastructure is out of service. We identify the main challenges that must be addressed in order to realize this potential in practice. These challenges concern various technical aspects, including physical connectivity requirements, network protocol stack enhancements, data traffic prioritization schemes, as well as social and political aspects

Through-space interactions in charge-transfer reactions

Considering the importance of electron-transfer reactions in chemistry and nature, especially regarding light-to-energy conversion by (dye-sensitized) solar-cells and the production of solar-fuels, a fundamental understanding of their mechanisms is necessary for the design of efficient systems. This thesis has its focus on the through-space interaction of donor-acceptor pairs, which was investigated in two fundamentally different ways – photoinduced electron-transfer and organic mixed-valency. A short perspective is outlined in Chapter I and brief overviews over the relevant aspects of electron-transfer reactions and mixed-valence systems are presented in Chapters II and IV, respectively. In Chapter III, the synthesis and spectroscopic measurements of a right-angled and linear series of homologous complexes, bearing a [Ru(bpy)3]2+ photosensitizer and a triarylamine electron-donor connected by fluorene bridges with different lengths, are described. Their behavior upon photoexcitaion, with and without an external quencher, was investigated in terms of intramolecular electron-transfer. Electron-transfer rate constants were determined and an unexpectedly weak distance-dependence was observed for the right-angled series. Molecular mechanics calculations indicate that this is caused by the flexibility of the fluorene bridge leading to small through-space donoracceptor separations. The very weak distance-dependence suggests a mainly throughspace pathway for the electron-transfer. For the linear complexes, electron-transfer was too fast to be detected with the employed method. In Chapter V, the synthesis of compounds with a "triple-decker" geometry, based on phenothiazine and carbazole as redox-active moieties, is presented. Analysis of electrochemical and spectroscopic data, obtained for the compounds in their singly-oxidized mixed-valence state, revealedweak interaction for the phenothiazine-based compounds. The through-space charge-transfer pathway may be explained by an n-pi-n- or pi-pi-interaction. The carbazole-based mixed-valence compounds suffered from inconclusive data that were obtained from the spectroscopic measurements

Security for the Industrial IoT: The Case for Information-Centric Networking

Industrial production plants traditionally include sensors for monitoring or documenting processes, and actuators for enabling corrective actions in cases of misconfigurations, failures, or dangerous events. With the advent of the IoT, embedded controllers link these `things' to local networks that often are of low power wireless kind, and are interconnected via gateways to some cloud from the global Internet. Inter-networked sensors and actuators in the industrial IoT form a critical subsystem while frequently operating under harsh conditions. It is currently under debate how to approach inter-networking of critical industrial components in a safe and secure manner. In this paper, we analyze the potentials of ICN for providing a secure and robust networking solution for constrained controllers in industrial safety systems. We showcase hazardous gas sensing in widespread industrial environments, such as refineries, and compare with IP-based approaches such as CoAP and MQTT. Our findings indicate that the content-centric security model, as well as enhanced DoS resistance are important arguments for deploying Information Centric Networking in a safety-critical industrial IoT. Evaluation of the crypto efforts on the RIOT operating system for content security reveal its feasibility for common deployment scenarios.Comment: To be published at IEEE WF-IoT 201

Connecting the World of Embedded Mobiles: The RIOT Approach to Ubiquitous Networking for the Internet of Things

The Internet of Things (IoT) is rapidly evolving based on low-power compliant protocol standards that extend the Internet into the embedded world. Pioneering implementations have proven it is feasible to inter-network very constrained devices, but had to rely on peculiar cross-layered designs and offer a minimalistic set of features. In the long run, however, professional use and massive deployment of IoT devices require full-featured, cleanly composed, and flexible network stacks. This paper introduces the networking architecture that turns RIOT into a powerful IoT system, to enable low-power wireless scenarios. RIOT networking offers (i) a modular architecture with generic interfaces for plugging in drivers, protocols, or entire stacks, (ii) support for multiple heterogeneous interfaces and stacks that can concurrently operate, and (iii) GNRC, its cleanly layered, recursively composed default network stack. We contribute an in-depth analysis of the communication performance and resource efficiency of RIOT, both on a micro-benchmarking level as well as by comparing IoT communication across different platforms. Our findings show that, though it is based on significantly different design trade-offs, the networking subsystem of RIOT achieves a performance equivalent to that of Contiki and TinyOS, the two operating systems which pioneered IoT software platforms

Detectability of Artificial Ocean Alkalinization and Stratospheric Aerosol Injection in MPI‐ESM

To monitor the success of carbon dioxide removal (CDR) or solar radiation management (SRM) that offset anthropogenic climate change, the forced response to any external forcing is required to be detectable against internal variability. Thus far, only the detectability of SRM has been examined using both a stationary and nonstationary detection and attribution method. Here, the spatiotemporal detectability of the forced response to artificial ocean alkalinization (AOA) and stratospheric aerosol injection (SAI) as exemplary methods for CDR and SRM, respectively, is compared in Max Planck Institute Earth System Model (MPI-ESM) experiments using regularized optimal fingerprinting and single-model estimates of internal variability, while working under a stationary or nonstationary null hypothesis. Although both experiments are forced by emissions according to the Representative Concentration Pathway 8.5 (RCP8.5) and target the climate of the RCP4.5 scenario using AOA or SAI, detection timescales reflect the fundamentally different forcing agents. Moreover, detectability timescales are sensitive to the choice of null hypothesis. Globally, changes in the CO2 system in seawater are detected earlier than the response in temperature to AOA but later in the case of SAI. Locally, the detection time scales depend on the physical, chemical, and radiative impacts of CDR and SRM forcing on the climate system, as well as patterns of internal variability, which is highlighted for oceanic heat and carbon storage

Stratosphere troposphere coupling: the influence of volcanic eruptions

Stratospheric sulfate aerosols produced by major volcanic eruptions modify the radiative and dynamical properties of the troposphere and stratosphere through their reflection of solar radiation and absorption of infrared radiation. At the Earth's surface, the primary consequence of a large eruption is cooling, however, it has long been known that major tropical eruptions tend to be followed by warmer than usual winters over the Northern Hemisphere (NH) continents. This volcanic "winter-warming" effect in the NH is understood to be the result of changes in atmospheric circulation patterns resulting from heating in the stratosphere, and is often described as positive anomalies of the Northern Annular Mode (NAM) that propagate downward from the stratosphere to the troposphere. In the southern hemisphere, climate models tend to also predict a positive Southern Annular Mode (SAM) response to volcanic eruptions, but this is generally inconsistent with post-eruption observations during the 20th century. We review present understanding of the influence of volcanic eruptions on the large scale modes of atmospheric variability in both the Northern and Southern Hemispheres. Using models of varying complexity, including an aerosol-climate model, an Earth system model, and CMIP5 simulations, we assess the ability of climate models to reproduce the observed post-eruption climatic and dynamical anomalies. We will also address the parametrization of volcanic eruptions in simulations of the past climate, and identify possibilities for improvemen

Hepatitis C in Special Patient Cohorts: New Opportunities in Decompensated Liver Cirrhosis, End-Stage Renal Disease and Transplant Medicine

Worldwide, hepatitis C virus (HCV) is a common infection. Due to new antiviral approaches and the approval of direct-acting antiviral agents (DAA), HCV therapy has become more comfortable. Nevertheless, there are special patient groups, in whom treatment of HCV is still challenging. Due to only few data available, tolerability and efficacy of DAAs in special patient cohorts still remain unclear. Such special patient cohorts comprise HCV in patients with decompensated liver disease (Child-Pugh Class B or C), patients with chronic kidney disease, and patients on waiting lists to renal/liver transplantation or those with HCV recurrence after liver transplantation. HCV infection in these patient cohorts has been shown to be associated with increased morbidity and mortality and may lead to reduced graft survival after transplantation. Successful eradication of HCV results in a better outcome concerning liver-related complications and in a better clinical outcome of these patients. In this review, we analyze available data and results from recently published literature and provide an overview of current recommendations of HCV-therapy regimen in these special patient cohorts

Error induced by neglecting subgrid chemical segregation due to inefficient turbulent mixing in regional chemical-transport models in urban environments

We employed direct numerical simulations to esti- mate the error on chemical calculation in simulations with re- gional chemical-transport models induced by neglecting sub- grid chemical segregation due to inefficient turbulent mixing in an urban boundary layer with strong and heterogeneously distributed surface emissions. In simulations of initially seg- regated reactive species with an entrainment-emission con- figuration with an A–B–C second-order chemical scheme, urban surface emission fluxes of the homogeneously emit- ted tracer A result in a very large segregation between the tracers and hence a very large overestimation of the effec- tive chemical reaction rate in a complete-mixing model.The article processing charges for this open- access publication were covered by the Max Planck SocietyPostprint (published version

Mechanically induced silyl ester cleavage under acidic conditions investigated by AFM-based single-molecule force spectroscopy in the force-ramp mode

AFM-based dynamic single-molecule force spectroscopy was used to stretch carboxymethylated amylose (CMA) polymers, which have been covalently tethered between a silanized glass substrate and a silanized AFM tip via acid-catalyzed ester condensation at pH 2.0. Rupture forces were measured as a function of temperature and force loading rate in the force-ramp mode. The data exhibit significant statistical scattering, which is fitted with a maximum likelihood estimation (MLE) algorithm. Bond rupture is described with a Morse potential based Arrhenius kinetics model. The fit yields a bond dissociation energy De = 35 kJ mol−1 and an Arrhenius pre-factor A = 6.6 × 104 s−1. The bond dissociation energy is consistent with previous experiments under identical conditions, where the force-clamp mode was employed. However, the bi-exponential decay kinetics, which the force-clamp results unambiguously revealed, are not evident in the force-ramp data. While it is possible to fit the force-ramp data with a bi-exponential model, the fit parameters differ from the force-clamp experiments. Overall, single-molecule force spectroscopy in the force-ramp mode yields data whose information content is more limited than force-clamp data. It may, however, still be necessary and advantageous to perform force-ramp experiments. The number of successful events is often higher in the force-ramp mode, and competing reaction pathways may make force-clamp experiments impossible

Inferring Topology of Networks With Hidden Dynamic Variables

Inferring the network topology from the dynamics of interacting units constitutes a topical challenge that drives research on its theory and applications across physics, mathematics, biology, and engineering. Most current inference methods rely on time series data recorded from all dynamical variables in the system. In applications, often only some of these time series are accessible, while other units or variables of all units are hidden, i.e. inaccessible or unobserved. For instance, in AC power grids, frequency measurements often are easily available whereas determining the phase relations among the oscillatory units requires much more effort. Here, we propose a network inference method that allows to reconstruct the full network topology even if all units exhibit hidden variables. We illustrate the approach in terms of a basic AC power grid model with two variables per node, the local phase angle and the local instantaneous frequency. Based solely on frequency measurements, we infer the underlying network topology as well as the relative phases that are inaccessible to measurement. The presented method may be enhanced to include systems with more complex coupling functions and additional parameters such as losses in power grid models. These results may thus contribute towards developing and applying novel network inference approaches in engineering, biology and beyond