Starling: A Blockchain-based System for Coordinated Obstacle Mapping in Dynamic Vehicular Environments

Current Vehicle-to-Vehicle solutions cannot ensure the authenticity of safety-critical vehicle and traffic data. Moreover, they do not allow malicious vehicles to be detected and eliminated. However, this is becoming mandatory, as more and more vehicles are on the road and communicating with each other. We propose a system called Starling, which focuses on trusted coordinated obstacle mapping using blockchain technology and a distributed database. Starling enables vehicles to share detected obstacles with other vehicles in a secure and verifiable manner, thus improving road safety. It ensures that data was not manipulated, changed, or deleted and is based on an open protocol so that vehicles can exchange data regardless of their manufacturer. In a case study, we demonstrate how a consensus is reached among vehicles and conduct a comprehensive evaluation of the Starling system using Ethereum and the InterPlanetary File System

Modeling IT Availability Risks in Smart Factories

In the course of the ongoing digitalization of production, production environments have become increasingly intertwined with information and communication technology. As a consequence, physical production processes depend more and more on the availability of information networks. Threats such as attacks and errors can compromise the components of information networks. Due to the numerous interconnections, these threats can cause cascading failures and even cause entire smart factories to fail due to propagation effects. The resulting complex dependencies between physical production processes and information network components in smart factories complicate the detection and analysis of threats. Based on generalized stochastic Petri nets, the paper presents an approach that enables the modeling, simulation, and analysis of threats in information networks in the area of connected production environments. Different worst-case threat scenarios regarding their impact on the operational capability of a close-to-reality information network are investigated to demonstrate the feasibility and usability of the approach. Furthermore, expert interviews with an academic Petri net expert and two global leading companies from the automation and packaging industry complement the evaluation from a practical perspective. The results indicate that the developed artifact offers a promising approach to better analyze and understand availability risks, cascading failures, and propagation effects in information networks in connected production environments

The DLPS: A New Framework for Benchmarking Blockchains

Distributed Ledger Technologies (DLT) promise to revolutionize business ecosystems by permitting secure transactions without intermediaries. A widely recognized challenge that inhibits the uptake of DLT is scalability and performance. Hence, quantifying key metrics such as throughput and latency is crucial for designing DLT-based infrastructures, applications, and ecosystems. However, current benchmarking frameworks for blockchains do not cover the whole benchmarking process; impeding transparent comparisons of different DLT networks. In this paper, we present the Distributed Ledger Performance Scan (DLPS), an open-source framework for end-to-end performance characterizations of blockchains, addressing the need to transparently and automatically evaluate the performance of highly customizable configurations. We describe our new framework and argue that it significantly improves existing DLT benchmarking solutions. To demonstrate the capabilities of the DLPS, we also summarize the main results obtained from a series of experiments that we have conducted with it, giving a first comprehensive comparison of essential scalability properties of several commonly used enterprise blockchains

DEVELOPMENT OF DYNAMIC KEY FIGURES FOR THE IDENTIFICATION OF CRITICAL COMPONENTS IN SMART FACTORY INFORMATION NETWORKS

Informational risks in smart factories arise from the growing interconnection of its components, the increasing importance of real-time accessibility and exchange of information, and highly dynamic and complex information networks. Thereby, physical production more and more depends on functioning information networks due to increasing informational dependencies. Accordingly, the operational capability of smart factories and their ability to create economic value heavily depend on its information network. Thus, information networks of smart factories have to be evaluated regarding informational risks as a first prerequisite for subsequent steps regarding the management of a smart factory. In this paper, we focus on the identification of critical components in information networks based on key figures that quantitatively depict the availability of the information network. To enable analyses regarding dynamic effects, the developed key figures cover dynamic propagation and recovery effects. To demonstrate their applicability, we investigate two possible threat scenarios in an exemplary information network. Further, we integrated the insights of two expert interviews of two global companies in the automation and packaging industry. The results indicate that the developed key figures offer a promising approach to better analyse and understand informational risks in smart factory information networks

The next step: intelligent digital assistance for clinical operating rooms

With the emergence of new technologies, the surgical working environment becomes increasingly complex and comprises many medical devices that have to be taken cared of. However, the goal is to reduce the workload of the surgical team to allow them to fully focus on the actual surgical procedure. Therefore, new strategies are needed to keep the working environment manageable. Existing research projects in the field of intelligent medical environments mostly concentrate on workflow modeling or single smart features rather than building up a complete intelligent environment. In this article, we present the concept of intelligent digital assistance for clinical operating rooms (IDACO), providing the surgeon assistance in many different situations before and during an ongoing procedure using natural spoken language. The speech interface enables the surgeon to concentrate on the surgery and control the technical environment at the same time, without taking care of how to interact with the system. Furthermore, the system observes the context of the surgery and controls several devices autonomously at the appropriate time during the procedure

Two independent estimations of stand-level root respiration on clonal Eucalyptus stands in Congo: up scaling of direct measurements on roots versus the trenched-plot technique

International audienceRoot respiration at the level of a forest stand, an important component of ecosystem carbon balance, has been estimated in the past using various methods, most of them being indirect and relying on soil respiration measurements. On a 3-yr-old Eucalyptus stand in Congo-Brazzaville, a method involving the upscaling of direct measurements made on roots in situ, was compared with an independent approach using soil respiration measurements conducted on control and trenched plots (i.e. without living roots). The first estimation was based on the knowledge of root-diameter distribution and on a relationship between root diameter and specific respiration rates. The direct technique involving the upscaling of direct measurements on roots resulted in an estimation of 1.53 μmol m-2 s-1, c. 50% higher than the mean estimation obtained with the indirect technique (1.05 μmol m-2 s-1). Monte-Carlo simulations showed that the results carried high uncertainty, but this uncertainty was no higher for the direct method than for the trenched-plot method. The reduction of the uncertainties on upscaled results requires more extensive knowledge of temperature sensitivity and more confidence and precision on the respiration rates and biomasses of fine roots