Scenario selection Sample Clauses

Scenario selection. As a result of the ranking of the scenarios we have selected a subset of them that will be further investigated for implementation. This selection takes into account the total scores of the scenarios in terms of their characteristics combined with the total number of votes from the partners. The subset of the scenarios, are as follows: UC11 – Energy efficiency contest Federated testbeds link UC2 – Public reporting and participatory sensing Core app (crowdsensing and crowdsourcing by events)+questionnaires UC12 - Energy Efficiency and user comfort in HOBNET enabled environments, using mobile crowdsensing hints Comfort hints UC13 - HEPIA sustainable week - crowdsourcing Event organisation for new ideas UC1 – eCROWD Core platform UC5 – Business and market studies Business angle For the above listed scenarios we can identify synergies between some of the them, leading us to further investigate the possiblity of merging similar scenarios. On the other hand, the degree of complexity of a scenario should be kept in a comprehensive level, meaning that each scenario should include a representativenumber of functionalities. All these considerations led us to identify two different scenarios for implementation: one scenario focused on the physical testbeds interaction and the other one focused on smart city. For the testbed related use case we will consider UC11, UC12, UC13 and UC5, whereas for the smart city scenario we will consider the scenarios UC1, UC2 and UC5. For the business and market studies use case we decided to extend the scenario’s description in order for it to be transversal to both use cases. The description is the following: IoT Lab technology is uniquely placed to measure the relationship between environmental conditions and consumer decisions. Academic research by psychologists and behavioural economists clearly shows that humans are not as objective or clinical in their decision making as many people might like to think and that environmental conditions can strongly bias the decisions that people make. Nobel prize (in Economics, 2002) winning research by ▇▇▇▇▇▇▇▇ and ▇▇▇▇▇▇▇ showed that prior exposure of an audience to a high or a low number had a measurable influence on how that audience subsequently estimated a variable that most of them would not know the answer to (e.g. an American audience was asked what percentage of African countries are members of the UN). This bias has been shown to extend to people’s estimation of the value of an object...