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When designing and deploying multimedia systems, it is essential to accurately know about the necessary requirements and the Quality of Service (QoS) offered to the customers. This paper presents two open-source software tools that contribute to these key needs. The first tool is able to measure and register resources consumption metrics for any Windows program (i.e. process id), like the CPU, GPU and RAM usage. Unlike the Task Manager, which requires manual visual inspection for just a subset of these metrics, the developed tool runs on top of the Powershell to periodically measure these metrics, calculate statistics, and register them in log files. The second tool is able to measure QoS metrics from DASH streaming sessions by running on top of TShark, if a non-secure HTTP connection is used. For each DASH chunk, the tool registers: the round-trip time from request to download, the number of TCP segments and bytes, the effective bandwidth, the selected DASH representation, and the associated parameters in the MPD (e.g., resolution, bitrate). It also registers the MPD and the total amount of downloaded frames and bytes. The advantage of this second tool is that these metrics can be registered regardless of the player used, even from a device connected to the same network than the DASH player. CCS CONCEPTS • Information systems  Information systems applications • Software and its engineering  Software creation and its management

In educational context, a source of nuisance for students is carbon dioxide ($$CO_2$$ C O 2 ) concentration due to closed rooms and lack of ventilation or circulatory air. Also, in the pandemic context, ventilation in indoor environments has been proven as a good tool to control the COVID-19 infections. In this work, it is presented a low cost IoT-based open-hardware and open-software monitoring system to control ventilation, by measuring carbon dioxide ($$CO_2$$ C O 2 ), temperature and relative humidity. This system provides also support for automatic updating, auto-self calibration and adds some Cloud and Edge offloading of computational features for mapping functionalities. From the tests carried out, it is observed a good performance in terms of functionality, battery durability, compared to other measuring devices, more expensive than our proposal.

The Internet of Things (IoT) is a network widely used with the purpose of connecting almost everything, everywhere to the Internet. To cope with this goal, low cost nodes are being used; otherwise, it would be very expensive to expand so fast. These networks are set up with small distributed devices (nodes) that have a power supply, processing unit, memory, sensors, and wireless communications. In the market, we can find different alternatives for these devices, such as small board computers (SBCs), e.g., Raspberry Pi (RPi)), with different features. Usually these devices run a coarse version of a Linux operating system. Nevertheless, there are many scenarios that require enhanced computational power that these nodes alone are unable to provide. In this context, we need to introduce a kind of collaboration among the devices to overcome their constraints. We based our solution in a combination of clustering techniques (building a mesh network using their wireless capabilities); at the same time we try to orchestrate the resources in order to improve their processing capabilities in an elastic computing fashion. This paradigm is called fog computing on IoT. We propose in this paper the use of cloud computing technologies, such as Linux containers, based on Docker, and a container orchestration platform (COP) to run on the top of a cluster of these nodes, but adapted to the fog computing paradigm. Notice that these technologies are open source and developed for Linux operating system. As an example, in our results we show an IoT application for soundscape monitoring as a proof of concept that it will allow us to compare different alternatives in its design and implementation; in particular, with regard to the COP selection, between Docker Swarm and Kubernetes. We conclude that using and combining these techniques, we can improve the overall computation capabilities of these IoT nodes within a fog computing paradigm.