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.

We assess the feasibility of a reusable neural architecture search agent aimed at amortizing the initial time-investment in building a good search strategy. We do this through the use of Reinforcement Learning, where an agent learns to iteratively select the best way to modify a given neural network architecture. This is achieved using a transformer-based agent design trained using the Ape-X algorithm. We consider both the NAS-Bench-101 and NAS-Bench-301 settings, and compare against various known strong baselines, such as local search and random search. While achieving competitive performance on both benchmarks, the amount of training required for the much larger NAS-Bench-301 is only marginally greater than NAS-Bench-101, illustrating the strong scaling properties of our agent. Our agent is able to achieve strong performance, but the choice of values for certain parameters are crucial to ensuring the succesful training of the agent. We provide some guidance for the selection of appropriate values for hyperparameters through a detailed description of our experimental setup and several ablation studies.

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.