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Creating Space-Time Affordances via anAutonomous Sensor Network
Norihiro MaruyamaThe University of Tokyo
3-8-1 Komaba
Tokyo, Japan
Email: maruyama@sacral.c.u-tokyo.ac.jp
Mizuki OkaCenter for Knowledge Structuring
The University of Tokyo
7-3-1 Hongo, Tokyo Japan
Email: mizuki@cks.u-tokyo.ac.jp
Takashi IkegamiThe University of Tokyo
3-8-1 Komaba
Tokyo, Japan
Email: ikeg@sacral.c.u-tokyo.ac.jp
Abstract—An autonomous sensor network (ASN), which iscomposed of many sensor units, is proposed and studied as anew challenge for artificial life in open-environment experiments.An aim of this study is to investigate how an artificial lifesystem behaves in a real, open, ever-changing environment withshort- and long-term environmental changes, including humanbehaviors. Compared to robot experiments in an open space, wesay that ASN will not move around in a space but can be testedfor a longer period of time with strongly autonomous behavior,without ignoring the messiness of the environment,.
The sensor network is unique because we employ artificialchemistry to enable it to control the sensitivity of each sensorunit (i.e., sensors are not simply reacting to environmentalchanges but are mutually affecting each other). Depending onthe environmental pattern and interaction conditions betweensensors, the system switches between a resonating state anda resting state. The resting state is the baseline activity ofthe network for a normal environmental condition, and theresonating state emerges when the environmental parameter (i.e.,the light condition) changes. We tested the nature of this behaviorby an emulator, showing the transition point in its samplingperiodicity from a resting state to a resonating state.
To ”experience” this switching behavior between a resting andresonating state, we organized a sound installation by translatingthe sensor states into sound patterns with unidirectional ultra-sonic speakers. Such art installation can be a good opportunityto test long-term adaptive behaviors of artificial life in the realworld.
I. INTRODUCTION
Research on artificial life involves attempts to produce
lifelike phenomena through simulations using computer mod-
els, robotics, and biochemistry. In this paper, we propose a
new approach to artificial life experimentation in an open
environment, with an autonomous sensor network (ASN). It
takes the form of an experimental sound art installation, aims
to explore behavior over a longer term in a messy environment.
Without having attractive affordance, any art system is doomed
to fail to attract people. Whereas people will often contemplate
natural scenes such as an oceanfront or a great valley for an
indefinite length of time, they rarely spend more than three
minutes in front of an art object, especially if it is demanding.
People quickly get tired if an object contains too many explicit
messages to digest or if it is just a simple reacting system.
Potentially, space designers or artists could attract people
and cause them to stay for relatively longer periods of time
with their innovative pieces, yet their innovations or methods
are generally not capable of being systematically prepared or
reproduced. The question is how to create a new space-time
affordance.
The idea of affordance was originally proposed by J. J. Gib-
son in the realm of ecological psychology and later imported
by D. Norman into the field of human-computer interaction.
“Affordance”has many definitions, depending on the field in
which the term is used. In this paper, we refer to affordance
as environmental information that is unconsciously acquired
and that is not attributed to a simple physical stimulus in the
environment. This definition gave us the idea of looking for a
systematic way to make an artificial object by creating a new
affordance.
Our main study principle for creating a new affordance is
autonomy in a system or environment, a concept that has been
fostered in the field of artificial life. Autonomy in a system
creates a pleasant distance between object and observer but
also arouses emotions which, like those we have for our pets,
can give rise to long-lasting relationships.
The system obtains sensor information from the environ-
ment, maintains a basic (artificial) metabolism, and changes
its behavior after a certain amount of information processing
has occurred (e.g., light and humidity sensor data). At the
sound installation, the sensor dynamics are then translated
into a soundscape by two automatically controlled parametric
speakers. By creating the new affordance in the form of a
soundscape, we are also proposing a new idea of how an
artificial system can resonate with a particular environmental
pattern, for example, dry weather with bright sunshine. We
believe such resonating behavior is a characteristic of an
autonomous system.
II. BACKGROUND
Artificial life has been intensively studied for the past 20
years, focusing on evolution, autonomy, and self-reproduction.
However, we have not yet succeeded in producing lifelike
phenomena anywhere. One of the reasons may be that artificial
life has been researched or designed in an environment that is
too ”clean”(i.e., inside computers). We need to bring artificial
life into the real world and test behaviors over longer durations
to produce lifelike phenomena.
67978-1-4673-5863-7/13/$31.00 c©2013 IEEE
Many robot experiments provided typical examples of arti-
ficial life in the real world. The first artificial life robot was
built by Grey Walter around 1950 [1]. Then, Rodney Brook
built several insect-type robots, including garbage collectors (
now known as robot vacuum cleaners) in the 1980s [2]. In the
1990s, Sony’s pet dog robot AIBO [3] was built, and in 2000s,
various humanoid robots such as Honda’s ASIMO [4] and
iCub [5] were designed. Walter’s robot tortoises, Elmer and
Elsie, showed playful dancing behaviors combined with simple
reflective actions, and the descendant robots were designed to
show more ”intelligent” behaviors by replacing vacuum tubes
with sophisticated IC chips. These days, so many automated
machines surround us, however, that Walter’s original idea of
emulating lifelike behavior seems was not developed enough.Therefore, we still need to understand and incorporate
autonomy into artificial systems. For the system to exhibit
autonomous behavior, it must incorporate an internal dynamic
that memorizes the system’s past experiences and learns envi-
ronmental affordances. We have to consider the environmental
complexity more seriously to study affordances in the environ-
ment. Affordance is all about the space-time pattern embedded
in the environment; a system must assimilate its behavior to the
environment to use the affordance. In other words, messiness
in the environment creates mind (or mindful behaviors) even
within artificial life (private conversation with Andy Clark).In this work, we are particularly interested in how a system
can perceive and generate space-time affordance in the real
world. Having the same philosophy in mind, Ikegami has
developed an autonomous artificial system in an open envi-
ronment, that is, public space, called the Mind Time Machine
(MTM) [6], [7]. MTM continuously and autonomously gener-
ates images using 15 video cameras and their feedback images
in real time. The type of information that is processed and
memorized, which is controlled by layered neural networks,
determines the system’s autonomous behavior. Similar to the
work that has been implemented in MTM, we are interested in
how the system decides when and where to sense information,
as well as how the sensor dynamics change to adapt to the
open environment over a long period of time.The unique idea with ASN is that, instead of using the visual
input images, we take various sensor information, obtained
from the real world and packed in a digital buffer, as chemical
species that diffuse through wireless connections. Multiple
sensor nodes are connected through a wireless network, and
the reaction proceeds based on sensor information taken from
an open environment. We will also organize an experimental
sound installation with this system to explore behavior over a
longer term in a messy environment.
III. AUTONOMOUS SENSOR NETWORK (ASN)
A. Outlook
We propose an ASN that is spatially distributed in the
real world. One node has two sensors, light and humidity,
that sense the corresponding environment information with an
adaptive sensing periodicity (or cycle). The sensor information
obtained by each node will be sent to other nodes (we set
(a) overview of sensor unit
(b) implemented sensor unit
Fig. 1. Overview of a sensor unit mechanism in the autonomous sensornetwork. The two buffers accumulate sensor information (light and humidity)from the sensors and other data sent from other nodes connected wirelesslyin the space. The reaction-diffusion module uses values in these buffers tochanges the length of sensing cycle. The below picture shows an implementedsensor unit.
TABLE ICOMPARISON BETWEEN CHEMICAL AND SENSOR NETWORKS
chemical network sensor network
chemical species sensor typechemical sensors digital sensors
chemical reaction tank digital sensor bufferdiffusion packet switching
the number of nodes at two) via wireless connections. In
other words, each sensor is attached to a buffer of each node
that accumulates sensor information from its own sensor. Two
kinds of buffers (one associated with light, and the other with
humidity) are associated with each node.
This system is unique in that we use a metaphor of spatially
extended chemical reaction schema. A modified Gray-Scott
reaction-diffusion model is used as a design for this sensor
network [8] [9]. This model is a translation of a spatially
extended chemical reaction into an active sensing and wireless
network system. The comparison is summarized in Table 1.
Sensory data in each unit are put onto the buffer, and an
assumed reaction will take place in that buffer. Suppose that
sensor values are A and B received by the corresponding
sensor. For example, we use the reaction A + B2 → C to
change the sampling periodicity (cycle) of the sensor, whose
68 2013 IEEE Symposium on Artificial Life (ALife)
reaction speed is proportional to [A][B]2. The cycle length is
defined as how often a sensor receives the sensory value from
the environment. Namely, the cycle length will be increased or
decreased, proportional to the reaction rate. It should be noted
that the sensor values will not be affected by the reaction but,
only the cycle length will be updated. After computing the
reaction rates, those sensor values will be sent to the other
wirelessly connected sensor nodes. As a result, the sensing
data will be circulating in the network through a wireless
connection.Fig. 1 illustrates the rough circuit of Arduino, XBee, and the
main processor that implements the virtual chemical network
in one unit, which has four inputs from and four outputs to
other sensor nodes connected by a wireless connection. They
are controlled by two XBees and two Arduinos. 1) The XBee
is used to receive sensor data from other nodes and to send
the data to the main processor. 2) The Arduino is used to
send sensor data obtained from the sensor itself to the main
processor when requested. 3) All of the data is eventually
received at the input controller, a module implemented in the
main processor. The main thread in the system emulates the
chemical reaction thread. The buffers store sensor values in
this thread, and the sensor data sent from the input controller
are saved in these buffers. If a buffer becomes full, the input
data are not received and are lost. The reaction thread is
periodically executed by using the sensor data saved in the
buffers for every 1,000 msec. As a result, the sensor sampling
cycle will be updated.
B. Detailed Schema
i) Storing Sensor ValuesEach sensor updates the sensory state every 1000 msec
cycle, depending on the filling rate of the corresponding buffer.
We define a filling rate of a sensor type j in a buffer as Fj
using a sensory state sji at the buffer site i(= 0, 1, 2, . . . , 255),as follows:
Fj =
∑N−1i=0 sji
NVmax(1)
Each sensor state sj , (j = 1, , , 256) , ranging from 0 to
Vmax, is stored in the buffer of the size 256. If the buffer is
fully occupied, Fj takes the maximal size 1. If the buffer is
empty, Fj takes 0.Two kinds of sensors (u and v) are used here: the light
sensor and the humidity sensor, respectively. We refer to the
filling rates for these sensors as Fu and Fv , respectively.ii) Updating Sensing CyclesIn addition to this, we define Tu and Tv as the sensing cycle
for each sensor; namely, each sensor samples every Tu or Tv
second, and the cycle length will also be updated as,
kuΔTu = −u · v2 + k1 · u (2)
kvΔTv = u · v2 − k0 · v (3)
where ΔTu and ΔTv are the changes of the cycle lengths.
The parameters k0 and k1 are given arbitrary values (in
our experiments, k0 and k1 are set to 0.045 and 0.025,
respectively). In both equations, the first term represents a
reaction, and the second terms represent in-flow in case of
u (light) and out-flow in case of v (humidity).
This formula can be related to the artificial chemical reac-
tion, that is, if u and v are created through a reaction u·v2. We
adopted the formula to update the sensory sampling rate; for
example, the sensory sampling cycle will be decreased when
chemicals are produced too fast.
Additionally, every buffer discard the most oldest packet
every updating cycle.
iii) Switching Sensor Values with Other Nodes
The sensor node sends packets to other nodes every Td
period, where we assume each node has two other nodes to
communicate with. Some packets containing sensor values will
be lost during transmission, which is an interesting difference
between the sensor network and the chemical network.
IV. EXPERIMENTAL SETUP
The sensor system was tested both in the university labo-
ratory room (February, 2012) and the public bookstore over
a two-week period (February 13th through 26th, 2012, from
10am to 10pm). We put the sensor network in those two
locations by distributing sensor units, equipped with both light
and humidity sensors, and maintaining a diary of daily sensor
data. The room had a sunlight from windows, and the system
was exposed to completely different conditions depending on
whether it was day or night.
The laboratory room and the bookstore have different light
conditions and traffic conditions (i.e., the number of people
coming in and out). i) The university laboratory room stays
quiet with 4 to 5 people at the most coming through per day.
It has wide windows on a wall, so that the room is bright at
the window side. ii) The bookstore is a bit more quiet, but
many more people come through. It is also much darker than
the laboratory room and shows little difference in night and
day.
V. RESULTS AND ANALYSIS
By setting the sensor network in two spaces and letting it
run for more than a week, we noticed that at least two dynamic
modes emerged in this network. We will further analyze the
modes by setting up a simulator of the network.
A. Resonating and Resting State
The sensor data in our system changed significantly, de-
pending on the environmental condition. Both sensor values
usually decreased in the evenings and then activated during the
daytime. In Fig. 2, we compare the sensor data obtained over
24 hours between the two spaces: one in a bookstore, which
is a larger space remote from windows and many visitors all
day long, and the other one is a laboratory room, which is a
relatively smaller space with wide windows and a few people
coming through per day.
A more dynamic pattern was observed in the laboratory
room, and relatively smaller changes were observed in the
2013 IEEE Symposium on Artificial Life (ALife) 69
Fig. 2. The sensor values for both light and humidity and the sensing cycle values are depicted as a function of time (in hours). The upper-left figureshows the sensor patterns at the bookstore, while the bottom-left figure shows those in the laboratory. Similarly, the upper-right figure shows the sensing cyclepatterns at the bookstore, and the bottom-right figure shows those in the laboratory.
Fig. 3. Time-series of the sensor values obtained for each node during a one-week period. The four figures at the left show the input sensor patterns of thefour nodes at the bookstore. The four figures at the right show those in the laboratory. A week periodicity with a rectangle form is found in the bookstoreand a week periodicity with an irregular form is found in the laboratory room.
bookstore. To underline this difference, we plotted the sam-
pling cycles of the sensors in the right figure of Fig. 2.
Comparing the two locations, we see that the length of
sensing cycles often diverges in the laboratory setting with
a number of outstanding large peaks; however, no such peaks
are found in the bookstore. The peaks are caused by the excess
light coming from the window in the laboratory room. These
spontaneous temporal oscillations with the large amplitude are
termed the resonating state of a network. On the other hand, a
relatively stable but noisy oscillation is termed the resting stateof the network. From the morning to the evening, a network
changes from the resting state to the resonating state and then
comes back to the resting state in the evening.
This resonating state is a characteristic of the present sensor
network caused by periodic/chaotic switching between the
filling up and exhausting of sensor data states. We made a
simple simulator to emulate the behavior of the actual sensor
network with actual/virtual sensing data.
B. A Long-Term Observation
Fig.3 shows temporal behaviors of four sensor units, and
the corresponding sensing cycle is shown in Fig.4 in the two
locations. First, we notice that even if the sensing value shows
a perfect periodicity of 24 hours, the corresponding sensing
cycle doesn’t always show such periodicity in a bookstore. In
the lab room, we have very different oscillating patterns from
day to day in sensing values as well as the sensing cycle. Due
to the large windows in the lab, this difference is probably
caused by the weather conditions, but it is also affected by
the human behavior (how many people are in the lab in that
70 2013 IEEE Symposium on Artificial Life (ALife)
Fig. 4. The sensing cycle shows a periodic motion over a week. Just as in Fig. 3, the left four figures show the result at the bookstore, and the right one isin the laboratory.
Fig. 5. Phase diagram of the state of the light sensor (left) and that of thehumidity sensor (right), which is characterized by the number of peaks per24 hours, computed with the simulator under the recorded sensor data. Thetotal number of peaks at each point is shown in grading scales. The brightercolor has the more number of peaks. For each diagram, the horizontal axisis the proportional coefficient and the vertical axis is the amount of diffusionper one cycle.
day).It is also interesting that the humidity can also show a
periodicity of 24 hours in a bookstore, since the humidity
should be equal irrespective of the weather condition. This
periodicity in the humidity sensor can be caused by the
coupling with the light sensor of the same unit, but it also
can be caused by the other sensor units wirelessly connected
and distributed in the bookstore. Because some of the nodes
are put in the deepest corner of the bookstore, no environ-
mental changes were expected for the nodes. Therefore, the
spontaneous oscillation of those nodes must be affected by the
connected sensor nodes.As we will study in the next subsection, even if the
environment has no temporal changes, the sensor values and
the cycle do oscillate. This is proved by a simulator run within
a single computer.
C. Emulating the Experiments with a Simulator
A simulation of this sensor network is done by equating
one cycle in a computer to 1 msec of the real experiment. For
each one simulation cycle, we execute reaction and diffusion
if it satisfies the condition, and we update the buffer states and
sampling cycles every one second. At the same time, sensory
data will be taken in from the recorded inputs can be applied
(or we use constant inputs for testing). Corresponding to the
real-world simulation, we recorded the internal states of the
system per every 1,000 msec.
We organized a phase diagram of the network by changing
the proportionality coefficients ku and kv in equations (2) (3)
and the amount of diffusion per one cycle.
The number of local peaks of the sensing cycle is depicted
in Fig. 5. In the diagram, we put light sensors on the left
and humidity sensors on the right. The numbers of peaks are
bursting in the upper-middle region of the diagram of the light
sensors.
By changing either the space location or the parameter sets,
we found that the resonating state of the network emerged.
Using the simulator with constant inputs from the environ-
ment, we examined how the network state changes by the
different inputs. In Fig.6 and Fig.7, we tested how the sensing
cycle changes by increasing the input values. This bifurcation
diagram was organized by superimposing the local peaks of the
dynamics for each input value. When the points are scattered,
the oscillation at that input value shows aperiodic motion. Note
that when the input values are lower than a critical value,
the sensing cycle (of its local peak values) simply shows a
fixed-point behavior. Above the critical point, it bifurcates into
other attractors. The attractor behaves differently for humidity
and light sensors as shown in Fig. 6. A major attractor is a
period 3 or a weakly chaotic oscillation. A similar behavior
is obtained in Fig.7, where the peaks of humidity states were
superimposed by fixing the humidity input and changing the
light inputs. We see a broadened period 2 oscillation that
becomes chaotic then to a fixed point and again becomes a
chaotic oscillation above a critical point.
2013 IEEE Symposium on Artificial Life (ALife) 71
Fig. 6. Bifurcation diagram of the sampling cycles under the constantenvironment. The horizontal axis shows the input sensor value (same as lightsensor and humidity sensor), and the local peaks of the time-series of thesampling cycle (top is the light sensor, and bottom is the humidity sensor)are superimposed in the vertical axis.
Fig. 7. Bifurcation diagram of the sampling cycles under the constanthumidity and the light values. The horizontal axis shows the light input valueand peaks of time-series of the sampling cycle superimposed in the verticalaxis.
VI. DISCUSSIONS
When we brought the system to a bookstore, we wanted to
create a new digital media art using the ASN. In particular,
we aimed to synthesize a new soundscape with this network.
When the amount of input sensor data in the buffer is ac-
cumulated over a threshold (5,000 for light sensor data, and
80 for humidity sensor data), the sound is generated from the
data point. A sound is a direct transformation of the amplified
sensor data, so it sounds like white noise. The point of making
this sound installation is to create a space-time affordance.
Specifically, unidirectional ultrasonic speakers are controlled
to move their faces periodically, and sound is emitted from
them via 32bit/48K Hz. Thanks to the nature of the speakers,
people experience the sound images as if they are whispered in
one ear. The main reason for using this type of speaker was to
Fig. 8. Installation of the ASN system, as a sound installation in a bookstorein Tokyo. The picture is an outlook of the installation. By walking aroundthe store, visitors experience different soundscapes generated by the twoautomatically controlled parametric speakers.
have people listening to the soundscape without detecting the
sound sources. People who come to this installation experience
a sense of ”here and now” by virtue of the undiffused nature
of the sound from the speakers. By walking around the
store, visitors experience soundscapes from a flow of abstract
information. Fig. 8 illustrates the display at the store.Creating new kinds of affordances with artificial life is
presented as a form of sound art installation; the system
provides a new type of affordance (to people who visit the
space) using a soundscape. So-called ambient music is usually
composed by a person and makes the same set of sounds
whenever it is played. By contrast, our system constantly
generates different sounds in a different space and time. Thus,
the system adapts itself to the place and time in which it
is installed. Sensitivity to place and time patterns is a new
feature of the artificial soundscape. We believe that auditory
rather than visual perception changes the way in which we
perceive space.“Surround-ness,”a unique nature of“ space-
ness,”has more to do with auditory than visual perception.
Therefore, this installation exemplifies how we can design a
space affordance with sounds.
VII. CONCLUSION
The significance of this work is twofold. First, artificial life
is shown as an autonomous chemical network that is translated
into a digital sensor network system. Second, this work shows
how an artificial life system behaves in an open environment
as opposed to a closed, simulated environment for relatively
longer periods of time.A most interesting finding here is that the network spon-
taneously generates a resonating state (and a resting state)
to a particular set of parameters or to the space and time
context, without having predefined conditions or functions. In
this study, we used only two kinds of sensors to organize
a chemical reaction, but, as with our human body sensors,
many sensors can be installed and integrated to give other
resonating states. We expect this to become a new method of
experimentation with artificial life in an open environment.
Acknowledgement This research work was supported by
the Grant-in-Aid for Scientific Research ”Theoretical Studies
72 2013 IEEE Symposium on Artificial Life (ALife)
of Diversity in Space Time Perception by Using a VR System”
(#24650117).
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