cilitate seamless interaction between
humans and cyber-infrastructure.
Worth emphasizing is that this line of
work is fundamentally different from
current research on human-in-the-loop cyber-physical systems that often
focuses on applications in which control is centralized and fully or mostly
automated while usually only a single
human is involved (such as in assistive robots and intelligent prosthetics). The synthesis of approaches from
social computing, citizen science, and
data science to advance integration,
management, and control of large and
variable numbers of human agents in
cyber-physical systems is potentially
transformative, addressing a crucial
bottleneck for the widespread adoption of similar methods in all kinds
of socio-technical systems, including
transportation networks, power grids,
smart buildings, environmental control, and smart cities.
Finally, SONYC uses New York
City, the largest, densest, noisiest city
in North America, as its test site. The
city has long been at the forefront of
discussions about noise pollution,
has an exemplary noise codeb and,
in 311, the most comprehensive citi-
zen noise-reporting system. Beyond
noise, the city collects vast amounts
of data about everything from public
b http://www.nyc.gov/html/dep/html/noise/
index.shtml
lack temporal dynamics and make
modeling assumptions that often
render them too inaccurate to sup-
port mitigation or action planning.
1
Few of these initiatives involve act-
ing on the sensed or modeled data
to affect noise emissions, and even
fewer have included participation from
local governments.
15
SONYC (Sounds of New York City),
our novel solution, as outlined in Figure 1, aims to address these limitations
through an integrated cyber-physical
systems’ approach to noise pollution.
First, it includes a low-cost, intelligent sensing platform capable of continuous, real-time, accurate, source-specific noise monitoring. It is scalable
in terms of coverage and power consumption, does not suffer from the
same biases as 311-style reporting, and
goes well beyond SPL-based measurements of the acoustic environment.
Second, SONYC adds new layers of
cutting-edge data-science methods for
large-scale noise analysis, including
predictive noise modeling in off-net-work locations using spatial statistics
and physical modeling, development
of interactive 3D visualizations of noise
activity across time and space to enable
better understanding of noise patterns,
and novel information-retrieval tools
that exploit the topology of noise events
to facilitate search and discovery. And
third, it uses this sensing and analysis
framework to improve mitigation in
two ways—first by enabling optimized,
data-driven planning and scheduling
of inspections by the local government,
thus making it more likely code violations will be detected and enforced; and
second, by increasing the flow of information to those in a position to control
emissions (such as building and con-struction-site managers, drivers, and
neighbors) thus providing credible incentives for self-regulation. Because the
system is constantly monitoring and
analyzing noise pollution, it generates
information that can be used to validate, and iteratively refine, any noise-mitigating strategy.
Consider a scenario in which a sys-
tem integrates information from the
sensor network and 311 to identify a
pattern of after-hours jackhammer
activity around a construction site.
This information triggers targeted in-
spections by the DEP that results in
an inspector issuing a violation. Sta-
tistical analysis can then be used by
researchers or city officials to validate
whether the action is short-lived in
time or whether its effect propagates
to neighboring construction sites or
distant ones by the same company. By
systematically monitoring interven-
tions, inspectors can understand how
often penalties need to be issued be-
fore the effect becomes long term. The
overarching goal is to understand how
to minimize the cost of interventions
while maximizing noise mitigation,
a classic resource-allocation prob-
lem that motivates much research in
smart-cities initiatives.
All this is made possible by formulating our solution in terms of a cyber-physical system. However, unlike most
cyber-physical systems covered in the
literature, the distributed and decentralized nature of the noise-pollution
problem requires multiple socioeconomic incentives (such as fines and
peer comparisons) to exercise indirect control over tens of thousands of
subsystems contributing noise emissions. It also calls for developing and
implementating a set of novel mechanisms for integrating humans in the
cyber-physical system loop at scale
and at multiple levels of the system’s
management hierarchy, including extensive use of human-computer interaction (HCI) research in, say, citizen
science and data visualization, to fa-
Figure 2. Acoustic sensing unit deployed on a New York City street.