Taming the interoperability challenges of complex IoT systems

of communication protocols and data formats; hence ensuring diverse devices can interoperate with one another remains a significant challenge. Model-driven development and testing solutions have been proposed as methods to aid software developers achieve interoperability compliance in the face of this increasing complexity. However, current approaches often involve complicated and domain specific models (e.g. web services described by WSDL). In this paper, we explore a lightweight, middleware independent, model-driven development framework to help developers tame the challenges of composing IoT services that interoperate with one another. The framework is based upon two key contributions: i) patterns of interoperability behaviour, and ii) a software framework to monitor and reason about interoperability success or failure. We show using a case-study from the FI-WARE Future Internet Service domain that this interoperability framework can support non-expert developers address interoperability challenges. We also deployed tools built atop the framework and made them available in the XIFI large-scale FI-PPP test environment

HUMANE external case study: eVACUATE #2

This case study was conducted in September to October 2016 with the purpose of providing an external validation of the HUMANE typology and method. This eVACUATE case-study comprises four different engagements in order to ensure a comprehensive evaluation: a quantitative online survey on the HUMANE design patterns; a quantitative survey on the HUMANE typology used for characterising Human-Machine Networks (HMNs); and two focus groups evaluating the HUMANE method (covering the profiling process, network diagramming, implication analysis, and design pattern approach). A summary of results, along with focus group transcripts, surveys and survey results are included here

HUMANE internal case study: eVACUATE #1

This case study was conducted on 14 December 2015. The purpose was to evaluate the usefulness of the HUMANE approach as perceived by relevant developers (software engineers), and additionally ask if the HUMANE typology facilitates cross-disciplinary understanding. The files included here provide a summary of the analysis and the transcript from a semi-structured focus group

An estimate of the stratospheric contribution to springtime tropospheric ozone maxima using TOPSE measurements and beryllium-7 simulations

Measurements of tropospheric ozone (O3) between 30°N and 70°N show springtime maxima at remote locations. The contribution of seasonal changes in stratosphere–troposphere exchange (STE) to these maxima was investigated using measurements from the Tropospheric Ozone Production about the Spring Equinox Experiment (TOPSE) campaign and the beryllium-7 (7Be) distribution from a calculation driven by fields from the Goddard Earth Observing System Data Assimilation System (GEOS DAS). Comparison with TOPSE measurements revealed that upper tropospheric model-calculated 7Be mixing ratios were reasonable (a change from previous calculations) but that lower tropospheric mixing ratios were too low most likely due to an overestimation of scavenging. Temporal fluctuations were well captured although their amplitudes were often underestimated. Analysis of O3measurements indicated that O3 mixing ratios increased by 5–10% month−1 for θ \u3c 300 K (the underworld) and by 10–15% month−1 for θ \u3e 300 K (the tropospheric middleworld). 7Be mixing ratios decreased with time for θ \u3c 290 K and increased with time for θ \u3e 300 K. Model-calculated middleworld increases of 7Be were a factor of 2 less than measured increases. 7Be with a stratospheric source (strat-7Be) increased by 4.6–8.8% month−1 along TOPSE flight paths within the tropospheric middleworld. Increases in strat-7Be were not seen along TOPSE flight paths in the underworld. Assuming changes in tropospheric O3 with a stratospheric source are the same as changes in strat-7Be and that 50% of O3 in the region of interest is produced in the stratosphere, changes in STE explain 20–60% of O3 increases in the tropospheric middleworld and less than 33% of O3 increases in the underworld

Cross-disciplinary lessons for the future internet

There are many societal concerns that emerge as a consequence of Future Internet (FI) research and development. A survey identified six key social and economic issues deemed most relevant to European FI projects. During a SESERV-organized workshop, experts in Future Internet technology engaged with social scientists (including economists), policy experts and other stakeholders in analyzing the socio-economic barriers and challenges that affect the Future Internet, and conversely, how the Future Internet will affect society, government, and business. The workshop aimed to bridge the gap between those who study and those who build the Internet. This chapter describes the socio-economic barriers seen by the community itself related to the Future Internet and suggests their resolution, as well as investigating how relevant the EU Digital Agenda is to Future Internet technologists

Automation in Human-Machine Networks: How Increasing Machine Agency Affects Human Agency

Efficient human-machine networks require productive interaction between human and machine actors. In this study, we address how a strengthening of machine agency, for example through increasing levels of automation, affect the human actors of the networks. Findings from case studies within air traffic management, crisis management, and crowd evacuation are presented, exemplifying how automation may strengthen the agency of human actors in the network through responsibility sharing and task allocation, and serve as a needed prerequisite of innovation and change

Model-driven interoperability: engineering heterogeneous IoT systems

Interoperability remains a significant burden to the developers of Internet of Things systems. This is because resources and APIs are dynamically composed; they are highly heterogeneous in terms of their underlying communication technologies, protocols and data formats, and interoperability tools remain limited to enforcing standards-based approaches. In this paper, we propose model-based engineering methods to reduce the development effort towards ensuring that complex software systems interoperate with one another. Lightweight interoperability models can be specified in order to monitor and test the execution of running software so that interoperability problems can be quickly identified, and solutions put in place. A graphical model editor and testing tool are also presented to highlight how a visual model improves upon textual specifications. We show using case-studies from the FIWARE Future Internet Service domain that the software framework can support non-expert developers to address interoperability challenges

Identifying privacy risks in distributed data services:A model-driven approach

Online services are becoming increasingly data-centric; they collect, process, analyze and anonymously disclose growing amounts of personal data. It is crucial that such systems are engineered in a privacy-aware manner in order to satisfy both the privacy requirements of the user, and the legal privacy regulations that the system operates under. How can system developers be better supported to create privacy-aware systems and help them to understand and identify privacy risks? Model-Driven Engineering (MDE) offers a principled approach to engineer systems software. The capture of shared domain knowledge in models and corresponding tool support can increase the developers' understanding. In this paper, we argue for the application of MDE approaches to engineer privacy-aware systems. We present a general purpose privacy model and methodology that can be used to analyse and identify privacy risks in systems that comprise both access control and data pseudonymization enforcement technologies. We evaluate this method using a case-study based approach and show how the model can be applied to engineer privacy-aware systems and privacy policies that reduce the risk of unintended disclosure