simulations are necessary for smart house developers to test and verify their ideas
before building a real one.
Developers typically conduct simulations using the following three steps.
(1) Manually create a simulation scenario by first building a house and resident body
models and defining the activity schedules and movement routes of the virtual resident
or controlling the virtual resident manually. (2) Place virtual sensors, devices, or robots
to record data and/or operation performances. (3) Analyze recorded data or operation
performances and evaluate simulation design. As a typical simulation constructed in
step (1) requires a lot of time, developers can only prepare a limited number of
scenarios. Moreover, the developers may use multiple simulators for different purposes,
e.g., using CST Microwave Studio to test the communication of a wireless sensor
network, OpenSHS [9] to collect virtual sensor records for sensor arrangement optimization, and Stage [10] to plan the operation policies of mobile robots. When the
developers use another simulator, they must repeat steps (1) even if they use the same
simulation scenario.
We propose a simulation tool that provides diverse simulation scenarios and can
support smart house developers to complete step (1) automatically in multiple simulation platforms [11]. This simulator consists of generators and interfaces as show in
Fig. 1. The proposed generators produce diverse information such as indoor spatial
attributes and resident travel patterns. This information is used to create a scenario that
can run on different simulation platforms through various interfaces. We proposed a
spatial attribute generator [12] and travel pattern generator [11], and used two interfaces
[11] to transfer the data generated by them to models and virtual sensor records of the
simulators.
As an essential part of our simulator, we propose an activity schedule generator.
With generated travel patterns, these schedules are converted to simulated real-time
location data, which can be used in simulations with interfaces. The rest of this paper is
organized as follows. In Sect. 2, we review related work of daily activity schedule
generation. Section 3 describes the methodology to generate activity schedules. Section 4 details the performance of this generator. Section 5 introduces how the generated
data can be used in simulations.
2 Related Works
A number of scholars generated daily activity schedules as intermediate results to
generate sensor records in a virtual smart house, which are essential for simulations.
Renoux et al. [13] generated activity schedules with a constraint-based planning
method. The constraints include that the start time and duration of each activity are
over reasonable intervals, and a number of activities need to be performed within
certain time intervals before their corresponding activities, e.g., preparing lunch for 0 to
5 min before having lunch.
Bouchard et al. [14] generated activity schedules using behavior trees (BTs) as
intermediate results to generate the simulated evolution of signal strength between
RFID readers and tags. However, designing BTs is complicate, and the authors only
showed an example of generating the schedule for making coffee or tea.
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C. Jiang and A. Mita
before building a real one.
Developers typically conduct simulations using the following three steps.
(1) Manually create a simulation scenario by first building a house and resident body
models and defining the activity schedules and movement routes of the virtual resident
or controlling the virtual resident manually. (2) Place virtual sensors, devices, or robots
to record data and/or operation performances. (3) Analyze recorded data or operation
performances and evaluate simulation design. As a typical simulation constructed in
step (1) requires a lot of time, developers can only prepare a limited number of
scenarios. Moreover, the developers may use multiple simulators for different purposes,
e.g., using CST Microwave Studio to test the communication of a wireless sensor
network, OpenSHS [9] to collect virtual sensor records for sensor arrangement optimization, and Stage [10] to plan the operation policies of mobile robots. When the
developers use another simulator, they must repeat steps (1) even if they use the same
simulation scenario.
We propose a simulation tool that provides diverse simulation scenarios and can
support smart house developers to complete step (1) automatically in multiple simulation platforms [11]. This simulator consists of generators and interfaces as show in
Fig. 1. The proposed generators produce diverse information such as indoor spatial
attributes and resident travel patterns. This information is used to create a scenario that
can run on different simulation platforms through various interfaces. We proposed a
spatial attribute generator [12] and travel pattern generator [11], and used two interfaces
[11] to transfer the data generated by them to models and virtual sensor records of the
simulators.
As an essential part of our simulator, we propose an activity schedule generator.
With generated travel patterns, these schedules are converted to simulated real-time
location data, which can be used in simulations with interfaces. The rest of this paper is
organized as follows. In Sect. 2, we review related work of daily activity schedule
generation. Section 3 describes the methodology to generate activity schedules. Section 4 details the performance of this generator. Section 5 introduces how the generated
data can be used in simulations.
2 Related Works
A number of scholars generated daily activity schedules as intermediate results to
generate sensor records in a virtual smart house, which are essential for simulations.
Renoux et al. [13] generated activity schedules with a constraint-based planning
method. The constraints include that the start time and duration of each activity are
over reasonable intervals, and a number of activities need to be performed within
certain time intervals before their corresponding activities, e.g., preparing lunch for 0 to
5 min before having lunch.
Bouchard et al. [14] generated activity schedules using behavior trees (BTs) as
intermediate results to generate the simulated evolution of signal strength between
RFID readers and tags. However, designing BTs is complicate, and the authors only
showed an example of generating the schedule for making coffee or tea.
172
C. Jiang and A. Mita