Brief descriptions of the projects follow:

SmartNurse is a FETLaunchpad winning proposal originated in the SmartSociety project that aims at developing (a) smart teaching assistant(s) for individualised training of student nurses. Such systems will offer information on demand (e.g. instant feedback, regulations or quick-check information, hints, …), and also provide feedback if the activities performed by the trainee during training sessions are not performed to required training goals. This application will allow student nurses to learn faster and get specific individual support.

 
AGENDA

12.30-13.30 : Light lunch & networking

13.30-13.35 : Welcome from EIT Digital (U. Bub)

13.35-13.45 : Blue sky talk: Beyond Sharing Economy: Service Provisioning in the Computational Humanism Era (F. Giunchiglia)

13.45-14.05 : Keynote: Towards a responsible sharing economy (A. Cañigueral)

14.05-14.35 : Startup Pitches – featuring WhiteRabbit, The Incentive Server & Augmented Collective Training

14.35-15.05 : High-impact verticals – panel with S. Anderson, S. Laepple.

15.05-15.35 : Coffee break & networking

15.35-15.55 : Keynote: The future of trust in the sharing economy (S. Green Brodersen)

15.55-16.05 : Open source corner: The SmartCollectives Toolkit (D. Miorandi)

16.05-16.30 : Innovation pilots and case studies – panel with L. Pannese, A. Grueberl, R. Chenu and K. Gal

16.30-17.00 : Fireside chat: Sharing economy, investors and a world without money – with D. Mazzella and R. van Kleji

17.00-19.00 : Beers, demo booths and networking

VENUE

EIT Digital Berlin CLC, Ernst-Reuter-Platz 7, 10587 Berlin, Germany

Directions and information: http://www.eitdigital.eu/about-us/locations/berlin-node/

Abstract: Web-based collaborative systems, where most computation is performed by human collectives, have distinctly different requirements from traditional workflow orchestration systems, as humans have to be mobilised to perform computations and the system has to adapt to their collective behaviour at runtime. In this paper, we present a social orchestration system called SmartOrch, which has been designed specifically for collective adaptive systems in which human participation is at the core of the overall distributed computation. SmartOrch provides a flexible and customisable workflow composition framework that has multi-level optimisation capabilities. These features allow us to manage the uncertainty that collective adaptive systems need to deal with in a principled way.
We demonstrate the benefits of SmartOrch with simulation experiments in a ridesharing domain. Our experiments show that SmartOrch is able to respond flexibly to variation
in collective human behaviour, and to adapt to observed behaviour at different levels. This is accomplished by learning how to propose and route human-based tasks, how to allocate computational resources when managing these tasks, and how to adapt the overall interaction model of the platform based on past performance. By proposing novel, solid engineering principles for these kinds of systems, SmartOrch addresses shortcomings of previous work that mostly focused on application-specific, non-adaptive solutions.

Citation: M. Rovatsos, D. Diochnos, Z. Wen, S. Ceppi, and P. Andreadis. SmartOrch: An Adaptive Orchestration System for Human-Machine Collectives. In Proceedings of the Special Track on Collective Adaptive Systems of the 32nd ACM Symposium on Applied Computing (SAC2017), Marrakech, Morocco, 2017. In Press

Download: http://bit.ly/2jBDYlu

Abstract: Large-scale IoT services such as healthcare, smart cities and marine monitoring are pervasive in Cyber-physical environments strongly supported by Internet technologies and Fog computing. Complex IoT services are increasingly composed of sensors, devices, and compute resources within Fog computing infrastructures. The orchestration of such applications can be leveraged to alleviate the difficulties of maintenance and enhance data security and system reliability. However, how to efficiently deal with dynamic variations and transient operational behavior is a crucial challenge within the context of choreographing complex services. Furthermore, with the rapid increase of the scale of IoT deployments, the heterogeneity, dynamicity, and uncertainty within Fog environments and increased computational complexity further dramatically aggravate this challenge. This article provides an overview of the core issues, challenges and future research directions in Fog-enabled orchestration for IoT services. Additionally, we present early experiences of an orchestration scenario, demonstrating the feasibility and initial results of using a distributed genetic algorithm in this context.

Citation: Z. Wen, R. Yang, P. Garraghan, T. Lin, J. Xu, and M. Rovatsos. Fog Orchestration for IoT Services: Issues, Challenges and Directions. IEEE Internet Computing, 2017. In press

Download: http://bit.ly/2iiyhfZ