4,036 research outputs found
Tracking Human Mobility using WiFi signals
We study six months of human mobility data, including WiFi and GPS traces
recorded with high temporal resolution, and find that time series of WiFi scans
contain a strong latent location signal. In fact, due to inherent stability and
low entropy of human mobility, it is possible to assign location to WiFi access
points based on a very small number of GPS samples and then use these access
points as location beacons. Using just one GPS observation per day per person
allows us to estimate the location of, and subsequently use, WiFi access points
to account for 80\% of mobility across a population. These results reveal a
great opportunity for using ubiquitous WiFi routers for high-resolution outdoor
positioning, but also significant privacy implications of such side-channel
location tracking
Inferring Person-to-person Proximity Using WiFi Signals
Today's societies are enveloped in an ever-growing telecommunication
infrastructure. This infrastructure offers important opportunities for sensing
and recording a multitude of human behaviors. Human mobility patterns are a
prominent example of such a behavior which has been studied based on cell phone
towers, Bluetooth beacons, and WiFi networks as proxies for location. However,
while mobility is an important aspect of human behavior, understanding complex
social systems requires studying not only the movement of individuals, but also
their interactions. Sensing social interactions on a large scale is a technical
challenge and many commonly used approaches---including RFID badges or
Bluetooth scanning---offer only limited scalability. Here we show that it is
possible, in a scalable and robust way, to accurately infer person-to-person
physical proximity from the lists of WiFi access points measured by smartphones
carried by the two individuals. Based on a longitudinal dataset of
approximately 800 participants with ground-truth interactions collected over a
year, we show that our model performs better than the current state-of-the-art.
Our results demonstrate the value of WiFi signals in social sensing as well as
potential threats to privacy that they imply
ConXsense - Automated Context Classification for Context-Aware Access Control
We present ConXsense, the first framework for context-aware access control on
mobile devices based on context classification. Previous context-aware access
control systems often require users to laboriously specify detailed policies or
they rely on pre-defined policies not adequately reflecting the true
preferences of users. We present the design and implementation of a
context-aware framework that uses a probabilistic approach to overcome these
deficiencies. The framework utilizes context sensing and machine learning to
automatically classify contexts according to their security and privacy-related
properties. We apply the framework to two important smartphone-related use
cases: protection against device misuse using a dynamic device lock and
protection against sensory malware. We ground our analysis on a sociological
survey examining the perceptions and concerns of users related to contextual
smartphone security and analyze the effectiveness of our approach with
real-world context data. We also demonstrate the integration of our framework
with the FlaskDroid architecture for fine-grained access control enforcement on
the Android platform.Comment: Recipient of the Best Paper Awar
PinMe: Tracking a Smartphone User around the World
With the pervasive use of smartphones that sense, collect, and process
valuable information about the environment, ensuring location privacy has
become one of the most important concerns in the modern age. A few recent
research studies discuss the feasibility of processing data gathered by a
smartphone to locate the phone's owner, even when the user does not intend to
share his location information, e.g., when the Global Positioning System (GPS)
is off. Previous research efforts rely on at least one of the two following
fundamental requirements, which significantly limit the ability of the
adversary: (i) the attacker must accurately know either the user's initial
location or the set of routes through which the user travels and/or (ii) the
attacker must measure a set of features, e.g., the device's acceleration, for
potential routes in advance and construct a training dataset. In this paper, we
demonstrate that neither of the above-mentioned requirements is essential for
compromising the user's location privacy. We describe PinMe, a novel
user-location mechanism that exploits non-sensory/sensory data stored on the
smartphone, e.g., the environment's air pressure, along with publicly-available
auxiliary information, e.g., elevation maps, to estimate the user's location
when all location services, e.g., GPS, are turned off.Comment: This is the preprint version: the paper has been published in IEEE
Trans. Multi-Scale Computing Systems, DOI: 0.1109/TMSCS.2017.275146
Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions
Traditional power grids are being transformed into Smart Grids (SGs) to
address the issues in existing power system due to uni-directional information
flow, energy wastage, growing energy demand, reliability and security. SGs
offer bi-directional energy flow between service providers and consumers,
involving power generation, transmission, distribution and utilization systems.
SGs employ various devices for the monitoring, analysis and control of the
grid, deployed at power plants, distribution centers and in consumers' premises
in a very large number. Hence, an SG requires connectivity, automation and the
tracking of such devices. This is achieved with the help of Internet of Things
(IoT). IoT helps SG systems to support various network functions throughout the
generation, transmission, distribution and consumption of energy by
incorporating IoT devices (such as sensors, actuators and smart meters), as
well as by providing the connectivity, automation and tracking for such
devices. In this paper, we provide a comprehensive survey on IoT-aided SG
systems, which includes the existing architectures, applications and prototypes
of IoT-aided SG systems. This survey also highlights the open issues,
challenges and future research directions for IoT-aided SG systems
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