Mobiscopes for Human Spaces
Tarek Abdelzaher1, Yaw Anokwa2, Péter Boda3, Jeff Burke4, Deborah Estrin5,
Leonidas Guibas6, Aman Kansal7, Sam Madden8, Jim Reich9
The proliferation of affordable mobile devices with processing and sensing capabilities, together with the
rapid growth in ubiquitous network connectivity, herald an era of Mobiscopes; networked sensing
applications that rely on multiple mobile sensors to accomplish global tasks. These distributed sensing
systems extend the model of traditional sensor networks, introducing challenges in data management, data
integrity, privacy, and network system design. While several applications that fit the above description
exist in prior literature, they provide tailored one-time solutions to what essentially is the same set of
problems. It is time to work towards a general architecture that identifies common challenges and
provides a generalizable methodology for the design of future Mobiscopes. Towards that end, this paper
surveys a variety of current and emerging mobile, networked, sensing applications; articulates their
common challenges; and provides architectural guidelines and design directions for this important
category of emerging distributed sensing systems.
A Mobiscope is a federation of distributed mobile sensors into a taskable sensing system that achieves
high-density sampling coverage over a wide area through mobility. Mobiscopes affordably extend into
regions that static sensors cannot, proving especially useful for applications in which data is required only
occasionally from each location. They represent a new type of infrastructure: ‘Virtual’ in the sense that a
given node may participate in forming more than one Mobiscope, but physically coupled to the
environment through carriers including people and vehicles. Public health epidemiological studies of
human exposure using mobile phones and real-time, fine-grained automobile traffic characterization using
sensors mounted on fleet vehicles are both examples of Mobiscope applications. While mobility has
proven critical in many scientific applications, such as NIMS for Science Observatories [Pon-etal05,
Rahimi-etal05], in this paper we focus on the particular challenges and opportunities posed by
Mobiscopes in human spaces.
Mobiscopes complement static sensing systems by addressing the fundamental limitations created by
fixed sensors. Sensing devices cannot always be placed with sufficiently high spatial density to accurately
sample the field of spatially-varying phenomena, making it impossible to satisfy spatial band-limiting
guarantees required by traditional sampling criteria. Coverage of large areas may be made challenging by
the need for long dwell times, the unavailability of wired power, the impracticality of battery replacement,
the inability of any one entity to install devices across the entire area, and the expense of purchasing and
maintaining a sufficient number of devices. Equally importantly, target sensor types may not be available
or affordable in the form of autonomous instruments, further motivating mobile, human-in-the-loop,
instruments. For example, city-scale measurements of air quality – which typically require the use of
1 University of Illinois at Urbana-Champaign, email@example.com
2 University of Washington, firstname.lastname@example.org
3 Nokia Research Center, Peter.email@example.com
4 University of California, Los Angeles, firstname.lastname@example.org
5 University of California, Los Angeles, email@example.com
6 Stanford University, firstname.lastname@example.org
7 Microsoft, email@example.com
8 Massachusetts Institute of Technology, firstname.lastname@example.org
9 Palo Alto Research Center, email@example.com
expensive mass spectrometers to measure pollutants – are very expensive when using fixed sensor
infrastructures, but could be made substantially cheaper and cover much larger areas if sensors were
mounted on mobile nodes (e.g., cars.)
This combination of application demand and increasingly powerful wireless and sensing technology
suggest that it is time to consider a general architecture for Mobiscopes. To understand what is needed to
build a unified system, we consider several broad classes of Mobiscope and their commonalities.
1.1 Classes of Mobiscopes
Early Mobiscopes will arise directly from widely available sensing modalities in otherwise networked
devices. Examples today include image sensors in mobile phones, GPS in both phones and vehicles, and
the increasingly diverse telemetry available in vehicles. Here, we consider these as representatives of two
broad categeories of Mobiscope:
Vehicular Mobiscopes: One broad class of Mobiscope is vehicular applications for traffic and
automotive monitoring, such as [Kerner-etal05], where a subset of equipped vehicles sense various
surrounding conditions such as traffic, road conditions, or weather. This takes advantage of the spatial
oversampling often provided by dense vehicle traffic to produce useful information before all vehicles are
equipped to send data, but even when nearly 100% market penetration is achieved, subsampling the data
in an intelligent way will prevent network congestion and saves storage and processing. Initial probe
applications have already been deployed commercially. For example, [inrix] uses anonymous GPS data to
provide real-time traffic measurements for both freeways and local streets. Vehicular mobiscopes can
alternatively query for certain traffic types. For example, the authors of [Zhou-etal05] present EZCab, a
cab-finder application that uses vehicle-to-vehicle communication to find available cabs. The probe-car
concept can be extended into other applications, such as augmenting the small number of NavTeq, or
TeleAtlas vehicles used for street mapping or increasing the update frequency of Microsoft’s street-level
imagery capture for Virtual Earth. Probe cars can also acquire high-density maps of roadways as well as
taking road condition, weather and pollution measurements using sensors built into cars and phones.
Handheld Mobiscopes: A second emerging category of Mobiscopes for human spaces lies in those that
use handheld devices. Course grained location information can inform studies ranging from health-
impacts related to highway-exposure or other spatially-situated toxins, and detailed information on
individuals use of transportation systems (waiting times, noise pollution, etc.) and other public spaces.
Automated image and acoustic capture have been proposed to provide user feedback on diet, exercise,
and personal interaction, as well as for identifying and sharing real time information about civic hazards
and hotspots. An interesting example is civic participation during a crisis [Parker-etal06]. In this case, a
loose form of control could be exercised over sensor placement. Users ranging from policemen to citizens
could utilize their cell phone cameras to capture images about trouble spots in their neighborhood. Such a
civic system may send out requests for policemen to document unexplored areas or intervene in trouble
spots. A similar concept of camera-based mapping can be used in tourism. For example, visitors of the
Taj Mahal might share their tourist pictures in virtual albums that can then be browsed by other potential
visitors to get a current view combining all the perspectives from which it was recently photographed.
Metadata management to facilitate such sharing has received special attention [Davis-etal05].
1.2 Common Requirements
The applications mentioned above share several important requirements that are of particular priority for
Mobiscope operation and acceptance. For example, data persistence must be assured even when sensing-
nodes leave the data collection area or when no mobile nodes are present. At the same time, data access
will tend to be spatially correlated with the users’ current location and may change rapidly, in a somewhat
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