Intelligent Transportation Systems (ITSs) providing vehicle-related statistical data are one of the key components for future smart cities. In this context, knowledge about the current traffic flow is used for travel time reduction and proactive jam avoidance by intelligent traffic control mechanisms. In addition, the monitoring and classification of vehicles can be used in the field of smart parking systems. The required data is measured using networks with a wide range of sensors. Nevertheless, in the context of smart cities no existing solution for traffic flow detection and vehicle classification is able to guarantee high classification accuracy, low deployment and maintenance costs, low power consumption and a weather-independent operation while respecting privacy. In this paper, we propose a radiobased approach for traffic flow detection and vehicle classification using signal attenuation measurements and machine learning algorithms. The results of comprehensive measurements in the field prove its high classification success rate of about 99%.

Future industrial control systems face the need for being highly adaptive, productive, and efficient, yet providing a high level of safety towards operating staff, environment, and machinery. These demands call for the joint consideration of resilience and mixed criticality to exploit previously untapped redundancy potentials. Hereby, resilience combines detection, decision-making, adaption to, and recovery from unforeseeable or malicious events in an autonomous manner. Enabling the consideration of functionalities with different criticalities, mixed criticality allows prioritizing safety-relevant over uncritical functions. While both concepts on their own feature a huge research branch throughout various disciplines of engineering-related fields, the synergies of both paradigms in a multi-disciplinary context are commonly overlooked. In industrial control, consolidating these mechanisms while preserving functional safety requirements under limited resources is a significant challenge. In this contribution, we provide a multidisciplinary perspective of the concepts and mechanisms that enable criticality-aware resilience, in particular with respect to system design, communication, control, and security. Thereby, we envision a highly flexible, autonomous, and scalable paradigm for industrial control systems, identify potentials along the different domains, and identify future research directions. Our results indicate that jointly employing mixed criticality and resilience has the potential to increase the overall systems efficiency, reliability, and flexibility, even against unanticipated or malicious events. Thus, for future industrial systems, mixed criticality-aware resilience is a crucial factor towards autonomy and increasing the overall system performance.

The measurement and provision of precise and up-to-date traffic-related key performance indicators is a key element and crucial factor for intelligent traffic control systems in upcoming smart cities. The street network is considered as a highly-dynamic Cyber Physical System (CPS) where measured information forms the foundation for dynamic control methods aiming to optimize the overall system state. Apart from global system parameters like traffic flow and density, specific data, such as velocity of individual vehicles as well as vehicle type information, can be leveraged for highly sophisticated traffic control methods like dynamic type-specific lane assignments. Consequently, solutions for acquiring these kinds of information are required and have to comply with strict requirements ranging from accuracy over cost-efficiency to privacy preservation. In this paper, we present a system for classifying vehicles based on their radio-fingerprint. In contrast to other approaches, the proposed system is able to provide real-time capable and precise vehicle classification as well as cost-efficient installation and maintenance, privacy preservation and weather independence. The system performance in terms of accuracy and resource-efficiency is evaluated in the field using comprehensive measurements. Using a machine learning based approach, the resulting success ratio for classifying cars and trucks is above 99%.

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