In the upcoming decade and beyond, the Cooperative, Connected and Automated Mobility (CCAM) initiative will play a huge role in increasing road safety, traffic efficiency and comfort of driving in Europe. While several individual vehicular wireless communication technologies exist, there is still a lack of real flexible and modular platforms that can support the need for hybrid communication. In this paper, we propose a novel vehicular communication management framework (CAMINO), which incorporates flexible support for both short-range direct and long-range cellular technologies and offers built-in Cooperative Intelligent Transport Systems’ (C-ITS) services for experimental validation in real-life settings. Moreover, integration with vehicle and infrastructure sensors/actuators and external services is enabled using a Distributed Uniform Streaming (DUST) framework. The framework is implemented and evaluated in the Smart Highway test site for two targeted use cases, proofing the functional operation in realistic environments. The flexibility and the modular architecture of the hybrid CAMINO framework offers valuable research potential in the field of vehicular communications and CCAM services and can enable cross-technology vehicular connectivity.

The increase of Internet of Things devices and the rise of more computationally intense applications presents challenges for future Internet of Things architectures. We envision a future in which edge, fog, and cloud devices work together to execute future applications. Because the entire application cannot run on smaller edge or fog devices, we will need to split the application into smaller application components. These application components will send event messages to each other to create a single application from multiple application components. The execution location of the application components can be optimized to minimize the resource consumption. In this paper, we describe the Distributed Uniform Stream (DUST) framework that creates an abstraction between the application components and the middleware which is required to make the execution location transparent to the application component. We describe a real-world application that uses the DUST framework for platform transparency. Next to the DUST framework, we also describe the distributed DUST Coordinator, which will optimize the resource consumption by moving the application components to a different execution location. The coordinators will use an adapted version of the Contract Net Protocol to find local minima in resource consumption.

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Recent advances in the field of Neural Architecture Search (NAS) have made it possible to develop state-of-the-art deep learning systems without requiring extensive human expertise and hyperparameter tuning. In most previous research, little concern was given to the resources required to run the generated systems. In this paper, we present an improvement on a recent NAS method, Efficient Neural Architecture Search (ENAS). We adapt ENAS to not only take into account the network's performance, but also various constraints that would allow these networks to be ported to embedded devices. Our results show ENAS' ability to comply with these added constraints. In order to show the efficacy of our system, we demonstrate it by designing a Recurrent Neural Network that predicts words as they are spoken, and meets the constraints set out for operation on an embedded device, along with a Convolutional Neural Network, capable of classifying 32x32 RGB images at a rate of 1 FPS on an embedded device.