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This article deals with the problem of wireless synchronization between onboard computing devices of small-sized unmanned aerial vehicles (SUAV) equipped with integrated wireless chips (IWC). Accurate synchronization between several devices requires the precise timestamping of batches transmitting and receiving on each of them. The best precision is demonstrated by those solutions where timestamping is performed on the PHY level, right after modulation/demodulation of the batch. Nowadays, most of the currently produced IWC are Systems-on-a-Chip (SoC) that include both PHY and MAC, implemented with one or several processor cores application. SoC allows create more cost and energy efficient wireless devices. At the same time, it limits the developers direct access to the internal signals and significantly complicates precise timestamping for sent and received batches, required for mutual synchronization of industrial devices. Some modern IEEE 802.11 IWCs have inbuilt functions that use internal chip clock to register timestamps. However, high jitter of the interfaces between the external device and IWC degrades the comparison of the timestamps from the internal clock to those registered by external devices. To solve this problem, the article proposes a novel approach to the synchronization, based on the analysis of IWC receiver input potential. The benefit of this approach is that there is no need to demodulate and decode the received batches, thus allowing it implementation with low-cost IWCs. In this araticle, Cypress CYW43438 was taken as an example for designing hardware and software solutions for synchronization between two SUAV onboard computing devices, equipped with IWC. The results of the performed experimental studies reveal that mutual synchronization error of the proposed method does not exceed 10 μs.
The IEEE 1588 precision time protocol (PTP) is a time synchronization protocol with sub-microsecond precision primarily designed for wired networks. In this letter, we propose wireless precision time protocol (WPTP) as an extension to PTP for multi-hop wireless networks. WPTP significantly reduces the convergence time and the number of packets required for synchronization without compromising on the synchronization accuracy.
Die Vielfalt der Protokolle, die praktisch auf allen Ebenen der Netzwerkkommunikation zu berücksichtigen ist, stellt eine der großen Herausforderungen bei der fortschreitenden Automatisierung des intelligenten Hauses dar. Unter dem Überbegriff Internet der Dinge (Internet of Things) entstehen gegenwärtig zahlreiche neue Entwicklungen, Standards, Allianzen und so genannte Ökosysteme. Diese haben die Absicht einer horizontalen Integration gewerkeübergreifender Anwendungen und verfolgen fast alle das Ziel, die Situation zu vereinfachen, die Entwicklungen zu beschleunigen und Markterfolge zu erreichen. Leider macht diese Vielfalt momentan die Welt aber eher noch komplexer und bringt damit das Risiko mit sich, genau das Gegenteil der ursprünglichen Absichten zu erreichen. Dieser Beitrag versucht, die Entwicklungen möglichst systematisch zu kategorisieren und mögliche Lösungsansätze zu beschreiben.
A novel approach of a test environment for embedded networking nodes has been conceptualized and implemented. Its basis is the use of virtual nodes in a PC environment, where each node executes the original embedded code. Different nodes run in parallel, connected via so-called virtual channels. The environment allows to modifying the behavior of the virtual channels as well as the overall topology during runtime to virtualize real-life networking scenarios. The presented approach is very efficient and allows a simple description of test cases without the need of a network simulator. Furthermore, it speeds up the process of developing new features as well as it supports the identification of bugs in wireless communication stacks. In combination with powerful test execution systems, it is possible to create a continuous development and integration flow.
The importance of machine learning (ML) has been increasing dramatically for years. From assistance systems to production optimisation to healthcare support, almost every area of daily life and industry is coming into contact with machine learning. Besides all the benefits ML brings, the lack of transparency and difficulty in creating traceability pose major risks. While solutions exist to make the training of machine learning models more transparent, traceability is still a major challenge. Ensuring the identity of a model is another challenge, as unnoticed modification of a model is also a danger when using ML. This paper proposes to create an ML Birth Certificate and ML Family Tree secured by blockchain technology. Important information about training and changes to the model through retraining can be stored in a blockchain and accessed by any user to create more security and traceability about an ML model.
Die Erfindung betrifft ein Verfahren zum Maximieren der von einer analogen Entropiequelle abgeleiteten Entropie, wobei das Verfahren folgende Schritte aufweist:- Bereitstellen von Eingabedaten für die analoge Entropiequelle (2);- Erzeugen von Rückgabewerten durch die analoge Entropiequelle basierend auf den Eingabedaten (3); und- Gruppieren der Rückgabewerte, wobei das Gruppieren der Rückgabewerte ein Anwenden von Versätzen auf Rückgabewerte aufweist (4).