IEEE Communications Magazine • November 2011
32
0163-6804/11/$25.00 © 2011 IEEE
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The notion that crowd-
sensing spans a spectrum
from participatory to
opportunistic sensing was
suggested by our colleague
Thomas Erickson.
INTRODUCTION
The integration of sensing and embedded every-
day computing devices at the edge of the Inter-
net will result in the evolution of an embedded
Internet or the Internet of Things (IoT). Typical
IoT devices include physical items tagged/embed-
ded with sensors (e.g., chemical containers with
temperature sensors), scissors with integrated
circuit (IC) tags, and smart meters to remotely
monitor energy consumption. An emerging cate-
gory of edge devices that we believe will result in
the evolution of the IoT are consumer-centric
mobile sensing and computing devices, which are
connected to the Internet. These include smart-
phones (iPhone, Google Nexus), music players
(iPods), sensor embedded gaming systems (Wii,
XboX Kinect), and in-vehicle sensing devices
(GPS, OBD-II). They have become extremely
popular recently and are potentially important
sources of sensor data. They are typically
equipped with various sensing faculties and wire-
less capabilities that allow them to produce data
and upload the data to the Internet. As an exam-
ple, a sample list of mobile devices and their
corresponding sensing capabilities are provided
in Table 1. Future sensing capabilities on smart-
phones include ECG (for medical purposes, e.g.,
ithlete, H’andy Sana), poisonous chemical detec-
tion (e.g., cell-all), and air quality sensors (e.g.,
Intel’s EPIC, Fig. 1).
Different from the “typical” everyday IoT
objects (e.g., coffee machines) that traditionally
lack computing capabilities, these mobile devices
have a variety of sensing, computing, and com-
munication faculties. They can either serve as a
bridge to other everyday objects, or generate
information about the environment themselves.
We believe they will drive a plethora of IoT
applications that elaborate our knowledge of the
physical world.
These applications can be broadly classified
into two categories, personal and community
sensing, based on the type of phenomena being
monitored. In personal sensing applications, the
phenomena pertain to an individual; for exam-
ple, the monitoring of movement patterns (e.g.,
running, walking, exercising) of an individual for
personal record-keeping or healthcare reasons.
Another example of personal sensing is one that
monitors the transportation modes of an individ-
ual to determine his or her carbon footprint.
On the other hand, community sensing per-
tains to the monitoring of large-scale phenome-
na that cannot easily be measured by a single
individual. For example, intelligent transporta-
tion systems may require traffic congestion mon-
itoring and air pollution level monitoring. These
phenomena can be measured accurately only
when many individuals provide speed and air
quality information from their daily commutes,
which are then aggregated spatio-temporally to
determine congestion and pollution levels in
cities.
Community sensing is also popularly called
participatory sensing [1] or opportunistic sensing
[2]. Participatory sensing requires the active
involvement of individuals to contribute sensor
data (e.g., taking a picture, reporting a road clo-
sure) related to a large-scale phenomenon.
Opportunistic sensing is more autonomous, and
user involvement is minimal (e.g., continuous
location sampling without the explicit action of
the user). We take the position that community
sensing spans a wide spectrum of user involve-
ment, with participatory sensing and opportunis-
tic sensing at the two ends. We therefore coin
the term mobile crowdsensing (MCS) to refer to
a broad range of community sensing paradigms.
1
In the rest of this article, we survey existing
crowdsensing (both participatory and oppor-
tunistic) applications, identify unique character-
istics of MCS applications, and discuss the
ABSTRACT
An emerging category of devices at the edge
of the Internet are consumer-centric mobile
sensing and computing devices, such as smart-
phones, music players, and in-vehicle sensors.
These devices will fuel the evolution of the
Internet of Things as they feed sensor data to
the Internet at a societal scale. In this article,
we examine a category of applications that we
term mobile crowdsensing, where individuals
with sensing and computing devices collectively
share data and extract information to measure
and map phenomena of common interest. We
present a brief overview of existing mobile
crowdsensing applications, explain their unique
characteristics, illustrate various research chal-
lenges, and discuss possible solutions. Finally, we
argue the need for a unified architecture and
envision the requirements it must satisfy.
THE INTERNET OF THINGS
Raghu K. Ganti, Fan Ye, and Hui Lei, IBM T. J. Watson Research Center
Mobile Crowdsensing:
Current State and Future Challenges
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