This paper proposes and evaluates a modular architecture of an autonomous unmanned aerial vehicle (UAV) system for search and rescue missions. Multiple multicopters are coordinated using a distributed control system. The system is implemented in the Robot Operating System (ROS) and is capable of providing a real-time video stream from a UAV to one or more base stations using a wireless communications infrastructure. The system supports a heterogeneous set of UAVs and camera sensors. If necessary, an operator can interfere and reduce the autonomy. The system has been tested in an outdoor mission serving as a proof of concept. Some insights from these tests are described in the paper.

Networks of micro aerial vehicles (MAVs) equipped with various sensors are increasingly used for civil applications, such as monitoring, surveillance, and disaster management. In this article, we discuss the communication requirements raised by applications in MAV networks. We propose a novel system representation that can be used to specify different application demands. To this end, we extract key functionalities expected in an MAV network. We map these functionalities into building blocks to characterize the expected communication needs. Based on insights from our own and related real-world experiments, we discuss the capabilities of existing communications technologies and their limitations to implement the proposed building blocks. Our findings indicate that while certain requirements of MAV applications are met with available technologies, further research and development is needed to address the scalability, heterogeneity, safety, quality of service, and security aspects of multi- MAV systems.

The computing continuum extends the high-performance cloud data centers with energy-efficient and low-latency devices close to the data sources located at the edge of the network. However, the heterogeneity of the computing continuum raises multiple challenges related to application management. These include where to offload an application—from the cloud to the edge—to meet its computation and communication requirements. To support these decisions, we provide, in this article, a detailed performance and carbon footprint analysis of a selection of use case applications with complementary resource requirements across the computing continuum over a real-life evaluation testbed.

Emergency management is about assessing critical situations, followed by decision making as a key step. Clearly, information is crucial in this two-step process. The technology of social (multi)media turns out to be an interesting source for collecting information about an emergency situation. In particular, situational information can be captured in form of pictures, videos, or text messages. The present paper investigates the application of multimedia metadata to identify the set of sub-events related to an emergency situation. The used metadata is compiled from Flickr and YouTube during an emergency situation, where the identification of the events relies on clustering. Initial results presented in this paper show how social media data can be used to detect different sub-events in a critical situation.

With video streaming becoming more and more popular, the number of devices that are capable of streaming videos over the Internet is growing. This leads to a heterogeneous device landscape with varying demands. Dynamic Adaptive Streaming over HTTP (DASH) offers an elegant solution to these demands. Smart adaptation logics are able to adjust the clients' streaming quality according to several (local) parameters. Recent research indicated benefits of blending Scalable Video Coding (SVC) with DASH, especially considering Future Internet architectures. However, except for a DASH dataset with a single SVC encoded video, no other datasets are publicly available. The contribution of this paper is two-fold. First, a DASH/SVC dataset, containing multiple videos at varying bitrates and spatial resolutions including 1080p, is presented. Second, a toolchain for multiplexing SVC encoded videos is provided, therefore making our results reproducible and allowing researchers to generate their own datasets.