Τhe project focuses on applications and services that lie in the broader area of sharing economy. The future of sharing economy
and its ultimate role in the world economic activities will depend on many factors, including the legal and
regulation policies applied to them. In any case, however, the related service platforms need to cope with
fundamental issues regarding the allocation and sharing of common resources, the provision of incentives,
and their business models. CRESCENDO responds to this need by drawing analysis tools from the areas of
network economics, queuing theory and (stochastic) optimization, but also from machine learning and numerical
analysis. It is structured along three discrete thematic areas: shared network connectivity, participatory sensing,
and crowdsourced transport. Common to all three thematic areas is the dimension of crowdsourcing and resource
sharing, as well as the sustainability challenges that relate to them.
From a scientific point of view, project contributions are expected in the areas of optimization (formulation of novel optimization problems under radically different assumptions about the decision-making process of human users), algorithms (design/analysis of algorithms for original theoretic problem instances), network economics and game theory. At socioeconomic level, the project research aims at better understanding, supporting, and enhancing activities that (a) have generated reasonable expectations for economic development and new jobs; and (b) are approached as the means towards higher market pluralism (market democratization), offering alternative solutions in otherwise mono/oligopolistic markets and reducing transaction costs that inflate, often disproportionately, the overall cost of producing and distributing goods.
The project involves synergies with research groups in the Singapore University of Technology and Design (SUTD) and NEC Labs Europe and will engage two PhD candidates in its activities. Its duration is 36 months
This project has received funding from the Hellenic Foundation for Research and Innovation (HFRI) and the General Secretariat for Research and Technology (GSRT), under grant agreement No 892