• M. Tavares, S. Dattagupta, R. Sofia. Relative Distance Measurement with NSense. 2017.11
    We have carried out a set of experiments with our middleware NSense installed on three devices and carried by three different individuals. Each experimental scene involved 2 devices (e.g., User1 to User2 or User3 to User1 readings) positioned in different locations, indoor or outdoor, on the same floor or in different floors, within and without LoS.
  • M. Tavares, P.Mendes, R. Brito. Nearness and Interests Traces. 2017.04
    This experiment has been carried out with the COPELABS sensing middleware NSense in the context of an experiment performed with psychology students, in two different institutions, where the purpose was to study interest influence in psychological proximity. NSense has been installed in Android smartphones carried by a population of 50 students of the two different universities, numbered User1 to User50. The students carried NSense around during their daily routines, for 2 days: 05.04.2017-06.04.2017. The readings obtained show a total of 15 students out of the original universe of 50.
  • S. Firdose, L. Lopes, W. Moreira, R. Sofia, P. Mendes. Data concerning social interaction and propinquity based on wireless and bluetooth. 2017.01.27

    This dataset comprises experiments carried out with the open-source middleware NSense (fomerly named as USense), available at https://github.com/COPELABS-SITI/NSense. The data has been collected based on four sensors: bluetooth; Wi-Fi; microphone; accelerometer. NSense then relies on four different pipelines to compute aspects such as relative distance (Wi-Fi); social strength (based on bluetooth contact duration); sound activity level; motion.
  • S. Firdose, L. Lopes, W. Moreira, R. Sofia, P. Mendes. Data concerning social interaction and propinquity. 2016.03.17
    This data set comprises experiments carried out considering four Android devices, each named Usense 2, 3, 4, and 5, respectively. These devices were carried by people sharing the same affiliation during their daily routines (commuting between home and office, going to leisure activities, attending meetings in the office). All the data was collected each and every one minute.
  • Saeik Firdose, Luis Amaral Lopes, Waldir Moreira, Paulo Mendes and Rute C. Sofia, NSense traces, social interaction and propinquity analysis, 2015