Wireless mobile devices such as cell phones, PDAs, and Wi-Fi
laptops become ubiquitous in our daily lives and guide us into the
era of pervasive computing. For instance, clothes and cars equipped
with such devices are going to seamlessly give us helpful
information when we are traveling a new city or shopping. Not only
do such devices enrich our daily activities, but also create an
environment such that epidemics of cooperation can thrive, e.g.,
among rescue workers or pedestrians, and thus people can cooperate
in ways that were never possible before. Futurist Howard Rheingold
first named these kinds of activities as "smart mobs," where
people with shared interests/goals can pervasively cooperate using
wireless mobile devices.
Reflecting on tragedies such as 9/11 and London Bombing, we envision
that such smart mobs may actually help relieve losses or investigate
accidents if they were properly organized beforehand. For example,
in the London Bombing, the police were able to track some of the
suspects in the underground using closed-circuit TV cameras in the
subway, but they had a hard time finding helpful evidence from
double-decker bus, in spite of the abundance of pictures taken by
shutterbugs using their camera phones. This incident convinced the
British police to install more cameras that can read license plates
to track vehicles. Yet in such a scenario, a smart mob approach is
more desirable than an extensive deployment of cameras/sensors: in
fact, the vision of smart mobs that exploit completely distributed
epidemic cooperation makes it hard for potential attackers to
disable surveillance. Let us briefly note that people are willing to
sacrifice privacy and to accept a reasonable level of surveillance
when monitored data can be collected and processed only by
recognized authorities, e.g., for forensic purposes, to counteract
terrorism and common crime.
The reconstruction of a crime and, more generally, the a posteriori
investigation of an event potentially monitored by distributed
mobile sensors, e.g., the nodes of a Vehicular Sensor Network (VSN),
require the collection, storage, and retrieval of
massive amounts of sensed data. This is a major departure from
conventional sensor network operations where data are collected,
examined, and dispatched to a "sink" under predefined conditions
such as alarm thresholds. In this scenario, it is impossible to
deliver all the data detected to the sink (for instance, the police
authority) because of size of streaming data (e.g., from video
sensors). Moreover, sensing nodes usually cannot determine a priori
whether their data will be of any use for future investigations.
Then, the problem becomes that of searching for sensed data in a
massive, mobile, and completely decentralized storage, by ensuring
low intrusiveness to other services, good scalability up to
thousands of nodes, and disruption tolerance against
mobility/terrorist attacks by promoting completely decentralized
cooperation networks.
To tackle such a problem, we have developed MobEyes, a system to
form smart mobs for VSN-based proactive urban monitoring. MobEyes
exploits wireless-enabled vehicles equipped with video cameras and a
variety of sensors to perform event sensing, processing/filtering of
sensed data, and ad hoc message routing to other vehicles. Since the
sheer amount of data will be generated from those sensors, directly
reporting raw data to the authority is infeasible. Thus, MobEyes
proposes that: sensed data stay with monitoring mobile nodes (i.e.,
mobile storage); vehicle-local processing capabilities are used to
extract features of interest, e.g., license plates from traffic
monitoring images; mobile nodes periodically generate data summaries
with extracted features and context information such as timestamps
and positioning coordinates; mobile agents, such as police
patrolling cars, move and opportunistically harvest summaries from
neighbor vehicles. The harvesting agents are interested in the
following data: where was a certain vehicle at a certain time; which
vehicles were at a given time in a given place, and: what data/video
did the vehicle(s) collect? To access the data later, one needs to
get to the actual vehicles (based on summary reports) and pump out
the data.
The original protocols exploited by MobEyes nodes for summary
diffusion/harvesting exploit intrinsic vehicle mobility and
single-hop communications among nodes. As thoroughly demonstrated by
the reported experimental results, in common deployment scenarios,
opportunistic summary diffusion/harvesting permits to build complete
indexing in feasible time, with good scalability, and with limited
overhead, by maintaining a completely decentralized
disruption-tolerant organization. Note that the use of MobEyes is
not confined to forensic data collection, yet it can be used in a
wide spectrum of applications: for instance, vehicles can measure
pollution levels and collect traffic-related data, such as
congestion and road conditions. The MobEyes modular architecture
facilitates its exploitation in different fields, by clearly
confining and limiting the support modifications depending on the
specific application logic.
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updated on Sunday, 25-Mar-2007 23:00:50 CET
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