SmartSPEC is a smart space simulator and data generator that creates customizable smart space datasets using semantic models of spaces, people, events and sensors. We employ ML-based approaches to characterize and learn attributes of the embedded people and events in a sensorized space and apply an event-driven simulation strategy to generate realistic simulated data about the space (e.g., events, trajectories, sensor datasets, etc.).

SmartSPEC Architecture

The SmartSPEC architecture consists of two main components:

  • Scenario Learning, which uses input seed connectivity data and a priori models of the underlying space and sensors to learn higher-order concepts of events, people and trajectories, which we refer to as “metaevents”, “metapeople” and “metatrajectories”, respectively.

  • Scenario Generation, which takes SmartSPEC metamodels to generate a smart space dataset (i.e., trajectory dataset, sensor observation dataset). Variations of the data models are used to define various hypothetical scenarios, which drives the generation of new observable phenomena in the smart space.

SmartSPEC Architecture

Using SmartSPEC

SmartSPEC provides three modes of operation to generate synthetic data, varying in the level of user involvement/automation. The steps to use our system are as follows:

  • Define the simulated space and its embedded sensors (1).
  • Define MetaPeople and MetaEvents manually (2a) or automatically (2b).
  • Define specific people and events based on the previous metamodels manually (3a) or automatically (3b).
  • Configure the simulation and automatically generate synthetic datasets (4).
Please see the GUI Toolkit for help in manually defining/modifying the models necessary for SmartSPEC.

SmartSPEC Workflows