Excessive alcohol consumption causes disability and death. Digital interventions are promising means to promote behavioral change and thus prevent alcohol-related harm, especially in critical moments such as driving. This requires real-time information on a person’s blood alcohol concentration (BAC). Here, we develop an in-vehicle machine learning system to predict critical BAC levels. Our system leverages driver monitoring cameras mandated in numerous countries worldwide. We evaluate our system with n = 30 participants in an interventional simulator study. Our system reliably detects driving under any alcohol influence (area under the receiver operating characteristic curve [AUROC] 0.88) and driving above the WHO recommended limit of 0.05 g/dL BAC (AUROC 0.79). Model inspection reveals reliance on pathophysiological effects associated with alcohol consumption. To our knowledge, we are the first to rigorously evaluate the use of driver monitoring cameras for detecting drunk driving. Our results highlight the potential of driver monitoring cameras and enable next-generation drunk driver interaction preventing alcohol-related harm.

Driver status monitoring systems are a vital component of smart cars in the future, especially in the era when an increasing amount of time is spent in the vehicle. The heart rate (HR) is one of the most important physiological signals of driver status. To infer HR of drivers, the mainstream of the existing research focused on capturing subtle heartbeat-induced vibration of the torso or leveraged photoplethysmography (PPG) that detects cardiac cycle-related blood volume changes in the microvascular. However, existing approaches rely on dedicated sensors that are expensive and cumbersome to be integrated or are vulnerable to ambient noise. Moreover, their performance on the detection of HR does not guarantee a reliable computation of the HR variability (HRV) measure, which is a more applicable metric for inferring mental and physiological status. The accurate computation HRV measure is based on the precise measurement of the beat-to-beat interval, which can only be accomplished by medical-grade devices that attach electrodes to the body. Considering these existing challenges, we proposed a facial expression-based HRV estimation solution. The rationale is to establish a link between facial expression and heartbeat since both are controlled by the autonomic nervous system. To solve this problem, we developed a tree-based probabilistic fusion neural network approach, which significantly improved HRV estimation performance compared to conventional random forest or neural network methods and the measurements from smartwatches. The proposed solution relies only on commodity camera with a lightweighted algorithm, facilitating its ubiquitous deployment in current and future vehicles. Our experiments are based on 3400 km of driving data from nine drivers collected in a naturalistic field study.

Authentic and confidential, but at the same time traceable and transparent, data exchange among multiple stakeholders is a key challenge in Industrial Internet of Things (IIoT) applications. Specifically, smart service connectivity requires the secure and transparent acquisition of equipment status information, which we call snapshots, from globally distributed equipment instances at customer sites by the equipment vendor. Related work has proposed to use a Message Queue Telemetry Transport (MQTT) Broker and hardware-secured Transport Layer Security (TLS) with client authentication. However, this approach lacks strong cryptographic end-to-end protection of snapshots. Here we show a hardware-rooted snapshot protection system that utilizes a Broker-based messaging infrastructure, hybrid encryption and a single-pass Elliptic Curve Menezes-Qu-Vanstone (ECMQV) scheme. We evaluate our concept by means of a prototype implementation and discuss security and performance implications. Our approach provides strong end-to-end data protection, while at the same time enabling customers to trace what data has been transferred off their equipment. We believe that our concept can serve as a template for a multitude of Industrial Internet of Things applications, which by their very nature call for strong security.

Global competition exhibits a strong pressure on organizations to optimize production processes and increase production flexibility. For example, to improve maintenance, repair and overhaul (MRO) processes of industrial equipment, an Internet-based data exchange system and backend workflow processing is required. Thereby an equipment vendor gathers field intelligence from equipment on customer premises worldwide. With the acquired data, smart services can be offered. Here, equipment maintenance tasks can be anticipated and thus on-premise equipment maintenance can be scheduled proactively. However, related work has focused mainly on two aspects. On the one hand, how to enable Internet-connectivity for legacy equipment has been investigated. One the other hand, the secure data acquisition systems have been presented. To address both aspects, Broker-based systems have been proposed. But related work does not address possible Broker architectures or the backend processing of data. Thus, in this work we investigate Broker- and backend-related aspects. First, we discuss topic structuring and access restriction. Second, we elaborate on different Broker placement architectures. Finally, we discuss and evaluate the design and implementation of an overall system and detail two concrete backend workflows.

Increasing the efficiency of production and manufacturing processes is a key goal of initiatives like Industry 4.0. Within the context of the European research project ARROWHEAD, we enable and secure smart maintenance services. An overall goal is to proactively predict and optimize the Maintenance, Repair and Operations (MRO) processes carried out by a device maintainer, for industrial devices deployed at the customer. Therefore it is necessary to centrally acquire maintenance relevant equipment status data from remotely located devices over the Internet. Consequently, security and privacy issues arise from connecting devices to the Internet, and sending data from customer sites to the maintainer's back-end. In this paper we consider an exemplary automotive use case with an AVL Particle Counter (APC) as device. The APC transmits its status information by means of a fingerprint via the publish-subscribe protocol Message Queue Telemetry Transport (MQTT) to an MQTT Information Broker in the remotely located AVL back-end. In a threat analysis we focus on the MQTT routing information asset and identify two elementary security goals in regard to client authentication. Consequently we propose a system architecture incorporating a hardware security controller that processes the Transport Layer Security (TLS) client authentication step. We validate the feasibility of the concept by means of a prototype implementation. Experimental results indicate that no significant performance impact is imposed by the hardware security element. The security evaluation confirms the advanced security of our system, which we believe lays the foundation for security and privacy in future smart service infrastructures.