The amendment that regulated the BAYNAN consortium update, with the addition of IMDEA as the third partner, was prepared by the partners and informally approved by the project officer during Jun 2020. The new consortium began the ESR hiring after October 2020 when the amendment was approved.
1. Multiple types of social media accounts have been created to generate further outreach impact, including the BANYAN website that presents the project's progress, achievements, and other news to the wider public. Meanwhile, the project has also presented the related news via LinkedIn and Twitter accounts.
2. All 5 ESRs were recruited and enrolled in a Ph.D. program. ESRs have been working on their personal development plan (PDP), they also attended post-graduate courses at their PhD hosting university on topics related to their research program, along with a number of local training courses on complementary skills, including IT, research methods, oral and written communications, project management, public engagement, and personal development. ESRs also attended seminars given by local researchers and internationally recognized experts, as well as actively interact with local staff members.
3. Three research reports have been delivered describing the outputs of tasks 1.1 1.2 and 2.1.
-D1.1 Report on mobile traffic demand baseline analytics. The objective of this deliverable is to understand and forecast mobile services demands at a macroscopic scale, that is, citywide. It is motivated by the fact that network slicing, technology that allows the creation of multiple logical network instances on the same physical network, demands a service-oriented approach to managing mobile networks.
-D1.2 Report on mobile traffic demand multi-scale analytics. The deliverable aims at developing analytics that allows characterizing the demand generated by mobile services over time and space. Specifically, the goal is to capture hidden traffic structures emerging within urban areas.
-D2.1 Report on algorithms to geo-localized traffic to buildings. A new deep learning model for time series classification, exploiting the concept of self-attention for the first time in the context of indoor-outdoor detection. It shows that self-attention can significantly enhance the IOD accuracy, as it allows a sequence model to focus on the parts of the sensor reading sequence that are more important for the environment classification.
4. Training plan report of D4.1 was delivered, and the D4.2 personal development plan has been delivered.
5. dissemination and outreach plan D5.1 has been delivered, which pre-planned the dissemination and outreach in the next few years. The postponed training school was scheduled after all ESRs were recruited, and the originally scheduled training school will be held on time.
