Wireless sensor networks, often adhering to a single gateway architecture, constitute the communication backbone for many modern cyber-physical systems. Consequently, faulttolerance in CPS becomes a challenging task, especially when accounting for failures (potentially malicious) that incapacitate the gateway or disrupt the nodes-gateway communication, not to mention the energy, timeliness, and security constraints demanded by CPS domains. This paper aims at ameliorating the fault-tolerance of WSN based CPS to increase system and data availability. To this end, we propose a replicated gateway architecture augmented with energy-efficient real-time Byzantineresilient data communication protocols. At the sensors level, we introduce FT-TSTP, a geographic routing protocol capable of delivering messages in an energy-efficient and timely manner to multiple gateways, even in the presence of voids caused by faulty and malicious sensor nodes. At the gateway level, we propose a multi-gateway synchronization protocol, which we call ByzCast, that delivers timely correct data to CPS applications, despite the failure or maliciousness of a number of gateways. We show, through extensive simulations, that our protocols provide better system robustness yielding an increased system and data availability while meeting CPS energy, timeliness, and security demands. Index Terms-Fault Tolerance. Algorithm/protocol design and analysis. Routing Protocols. Wireless Sensor Networks. A. A. Fröhlich and R. M. Scheffel where with the Software/Hardware

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Online process control is a crucial task in modern production systems that use digital twin technology. The data acquisition from machines must provide reliable and on-the-fly data, reflecting the exact status of the ongoing process. This work presents an architecture to acquire data for an Additive Manufacturing (3D printer) process, using a set of consolidated Internet of Things (IoT) technologies to collect, verify and store these data in a trustful and secure way. The need for online monitoring and fault detection is addressed by the development of a classifier using Convolutional Neural Networks. This deep learning approach, using temporally aligned vibration data provided by the underlying architecture, allows raw data processing to detect patterns without signal preprocessing and without domain-specific knowledge for model building.

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