Several issues related to Smart City development require the knowledge of accurate human mobility models, such as in the case of urban development planning or evacuation strategy definition. Nevertheless, the exploitation of real data about users' mobility results in severe threats to their privacy, since it allows to infer highly sensitive information. On the contrary, the adoption of simulation tools to handle mobility models allows to neglect privacy during the design of location-based services. In this work, we propose a simulation tool capable of generating synthetic datasets of human mobility traces; then, we exploit them to evaluate the effectiveness of algorithms which aim to detect Points of Interest visited by users of a Smart Campus. Our simulator exploits an activity-based mobility model, thus it is based on the assumption that mobility of campus users is motivated by the activities they plan to perform. It is capable of simulating the weekly repetitiveness of human behavior and to model different mobility profiles for each day of the week through a fifth-order Markov model.
|Titolo della pubblicazione ospite||Proceedings - 2019 IEEE/ACM 23rd International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2019|
|Numero di pagine||8|
|Stato di pubblicazione||Published - 2019|
|Nome||PROCEEDINGS IEEE INTERNATIONAL SYMPOSIUM ON DISTRIBUTED SIMULATION AND REAL-TIME APPLICATIONS|