How many times have ever asked yourself: "Can I trust my satellite experiments' outcome?". Performing experiments on real satellite system can either be (1) costly, as the radio resource may be scarce or (2) not possible, as you can hardly change the waveforms transmitted by the satellite platform. Moreover, assessing user applications QoE can hardly be done using only simulated environments while the QoS modeling of a satellite system can often lead to non-conclusive or ambiguous results. The aim of this paper is to bring out representative solutions allowing the networking community to drive consistent experiments using open-source tools. To this end, we compare Mininet and OpenSAND satellite emulator to a real satellite access provided by CNES. We consider VoIP traffic to analyze the trade-off between reliability of the results, ease of use and reproducibility of the experiments. Pros and cons Result fidelity Recommended VoIP use cases Satellite QoE Satellite QoS Real satellite access [2] Pros: realism, transparency, advanced satellite features (e.g., performance-enhancing proxies) Cons: infrastructure access, shared access for some commercial offers, cost, complex architecture Mean Opinion Score Packets dropped Average jitter Max. one-way delay Performance evaluation, tests prior to commercial deployments Mininet [3] Pros: easy deployment on a single machine, shared user space with virtualized network Cons: user should create satellite topology from scratch using hosts, switches and Mininet's API Mean Opinion Score Packets dropped Average jitter Max. one-way delay Stress tests OpenSAND [4] Pros: fine-grained configuration (e.g., carriers, modulation types), emulation of end-to-end satellite communication system with resource allocation Cons: several machines required (3), manual management of the whole system Mean Opinion Score Packets dropped Average jitter Max. one-way delay Performance evaluation, system dimensioning

Abstract-Built upon the Internet of Things (IoT), the Internet of Everything (IoE) acknowledges the importance of data quality within sensor-based systems, alongside with people, processes and Things. Nevertheless, the impact of many technologies and paradigms that pertain to the IoE is still unknown regarding Quality of Observation (QoO).This paper proposes to study experimental results from three IoE-related deployment scenarios in order to promote the QoO notion and raise awareness about the need for characterizing observation quality within sensor-based systems. We specifically tailor the definition of QoO attributes to each use case, assessing observation accuracy within Smart Cities, observation rate for virtual sensors and observation freshness within post-disaster areas. To emulate these different experiments, we rely on a custom-developed integration platform for the assessment of QoO as a service called iQAS.We show that QoO attributes should be used to specify what is an observation of "good quality", that virtual sensors may have specific and limiting capabilities impacting QoO and that network QoS and QoO are two complementary quality dimensions that should be used together to improve the overall service provided to end-users.

While reducing costs and improving sustainability, a common goal for Smart Cities is to become more "liveable" for their citizens. By taking advantage of new information sources offered by the Internet of Things (IoT), cities can rely on sensing platforms to improve their service offer. These sensing platforms, however, raise new research challenges, in particular regarding Quality of Information (QoI). To cope with this issue, common platforms generally provide quality-oriented internal mechanisms. Nevertheless, the configuration of such platforms is complex, especially for Smart City stakeholders that may have various skill levels and different areas of expertise. As a result, QoI assessment is often delegated to end applications where developers have to implement their own adaptation mechanisms. This paper proposes and describes iQAS, an integration platform for QoI Assessment as a Service for Smart Cities. iQAS is autonomic, extensible and configurable, allowing Smart City stakeholders to collaboratively assess and improve (when possible) QoI in real-time. While the platform development is at its early stages, we illustrate within a concrete case study the need for QoI assessment and the benefits to implement adaptation mechanisms.

Observation streams can be considered as a special case of data streams produced by sensors. With the growth of the Internet of Things (IoT), more and more connected sensors will produce unbounded observation streams. In order to bridge the gap between sensors and observation consumers, we have witnessed the design and the development of Cloudbased IoT platforms. Such systems raise new research challenges, in particular regarding observation collection, processing and consumption. These new research challenges are related to observation streams and should be addressed from the implementation phase by developers to build platforms able to meet other non-functional requirements later. Unlike existing surveys, this paper is intended for developers that would like to design and implement a Cloud-based IoT platform capable of handling sensor observation streams. It provides a comprehensive way to understand main observation-related challenges, as well as non-functional requirements of IoT platforms such as platform adaptation, scalability and availability. Last but not the least, it gives recommendations and compares some relevant open-source software that can speed up the development process.