In the field of vehicular networks, multiple approaches have been proposed to share and reuse data acquired by participating hosts. In this context, vehicular micro clouds extend the concept of Mobile Edge Computing (MEC) and bring data storage and processing to the vehicles, solving application tasks that need to be done in real-time. A critical point in shared computing tasks and storage is to keep all nodes synchronized and to maintain consistency. For the first time, we study the relevance of data versions in micro clouds offering intersection management service at four-way stop intersections and we investigate how different versions of data affect both road traffic and wireless communications. Our results validate the intuition that an increase in the amount of different data versions in the micro cloud has negative effects on both road traffic and wireless channel usage. Yet, the choice of the data sharing algorithm can make a huge difference and reduce these effects on a large scale. We found that synchronicity of data versions can be increased by up to 20% through small changes, e.g., keeping small amounts of data history in the applied algorithm.

Vehicular Micro Clouds (VMCs) are an emerging development in the domain of vehicular networks posed to provide local services to users without the need for external infrastructure. This can significantly improve the user experience, in particular due to the low latencies that such systems can achieve. Due to the distributed nature of such a VMC, effective local coordination is important while using minimal communication resources. To this end, it is important to know, how long vehicles will be participating in, and contributing to a VMC. In this work, we investigate, how previous, heuristic-based approaches can be improved by incorporating local, learning-based techniques. Our analysis indicates a potential improvement of the accuracy of the prediction, and resulted in an improved simulation environment within which the learning-based approach can be deployed.

The continuous growth of the urban population and the high development and maintenance costs of infrastructure-based approaches make it necessary the utilization of distributed schemes. Among distributed systems, there is an increasing interest in edge cloud models, both for vehicular and pedestrian applications. In this paper, we developed a distributed application based on the micro cloud concept formulated in the field of vehicular edge computing for spreading content items in an indoor pedestrian environment. Results highlight how it is possible to reach both 100% content items spread and up to 66% reduction over channel collisions.

We present a flexible real-time software-based multi-band channel sounder, which can flexibly be used in a variety of measurement campaigns, and fills the gap of expensive commercial channel sounders with limitations of operation frequency, sounding bandwidth, and ease of use. In general, channel sounding is a very valuable tool for designing wireless communication systems. Even though commercial channel sounders allow for thorough and detailed measurements, they are typically very expensive. In contrast, existing software-based solutions are often limited in scope and complexity of measurements. Our system closes this gap. Using GNU Radio and USRP radio front-ends, the system provides a high degree of freedom with respect to supported carrier frequencies and bandwidth. We also support features such as automatic spectrum-sweeping and transmitter gain management for dynamic channel characterization. We can use the system for both live visualization of the channel characteristics, e.g., Channel Impulse Response, Channel Frequency Response, Power Delay Profile, and Channel Phase Response, as well as for fully automated and repeatable experiments.

Vehicular micro clouds are considered a prime building block for next generation Intelligent Transportation Systems (ITS)also supporting a variety of Information and Communication Systems (ICT)applications in smart cities. Such micro clouds are established by multiple cars equipped with communication, storage, and computational resources. We recently presented the concept of hierarchical vehicular cloud computing, which is meant to extend on Mobile Edge Computing (MEC). Based on clustering algorithms, we can set up and maintain such micro clouds and eventually make use of the distributed resources. Looking at the high mobility of cars, it is very difficult to reliably maintain data collected by cars at a given location in space that is geographically relevant, e.g., at intersections. In this paper, we propose a new protocol which encourages coordination between neighboring micro clouds to help keeping local data current, i.e., cars moving out of the micro cloud may take data to neighboring clouds, hand data over to cars moving towards the original micro cloud, and, thus, returning the data to its original geographical location. We evaluate the performance of the protocol with different vehicle densities in a Manhattan Grid scenario and our results show the benefits of our proposed inter micro cloud coordination protocol.

One of the most recent applications in the vehicular networking domain is distributed data processing using cars as sensors of information. In recent work, the concept of vehicular cloud computing has been explored to provide the necessary scalability and to improve the communication between clusters of cars being called vehicular micro clouds and other participants (cars, bicyclists, pedestrians). In order to provide a bigger picture and also to interconnect such micro clouds, data centers or cloud servers are considered bridging the gap. We study the uplink capabilities from connected cars to such data centers. Options include direct LTE uplinks from all cars, selected use of Roadside Units (RSUs) with back-end connectivity or LTE uplinks from the vehicular micro clouds, and finally hybrid solutions taking network quality and available channel resources into account. Our findings clearly show the advantages of such hybrid solutions both in terms of throughput as well as of optimizing operational costs.

We present LAN Radio, an Open Source prototyping system to couple simulation and real world for the wireless channel of IEEE 802.11p based communication. Our main focus is the use in a vehicular networking environment, where field testing and real world experimentation is becoming more relevant these days. Large Field Operational Tests (FOTs) are expensive and difficult to handle in terms of reproducibility and controllability -- particularly for application development and integration tests for automotive Electronic Control Units (ECUs). To support such experiments, we developed an approach to integrate a real system to test into a large scale simulation scenario without the need to change the physical and access layer parts of the communication stack of this system. We closely followed the Hardware In The Loop (HIL) simulation concept but also integrated a wireless communication channel into the picture. LAN Radio is building upon the well established and Open Source development platform OpenWRT to provide optimal extensibility.

We present OpenC2X, an Open Source approach to field testing of vehicular networking solutions. Field Operational Test (FOT) and real-world experimentation are becoming more relevant to our research community as well as to industry and regulation. Unfortunately, available commercial solutions make experimental modifications to the protocol stack time consuming or prohibit it completely. To overcome this limitation, we implemented the ETSI ITS-G5 stack on a standard embedded PC hardware and running Linux system. OpenC2X is the first complete Open Source experimentation and prototyping platform. Our system is fully interoperable to commercial solutions, yet easily extensible with new protocols and applications for vehicular networks.