257 research outputs found
Performance Characterization of Random Proximity Sensor Networks
In this paper, we characterize the localization performance
and connectivity of sensors networks consisting of
binary proximity sensors using a random sensor management
strategy. The sensors are deployed uniformly at random over
an area, and to limit the energy dissipation, each sensor node
switches between an active and idle state according to random
mechanisms regulated by a birth-and-death stochastic process.
We first develop an upper bound for the minimum transmitting
range which guarantees connectivity of the active nodes in the
network with a desired probability. Then, we derive an analytical
formula for predicting the mean-squared localization error of
the active nodes when assuming a centroid localization scheme.
Simulations are used to verify the theoretical claims for various
localization schemes that operate only over connected active
nodes
Probabilistic Logic Programming with Beta-Distributed Random Variables
We enable aProbLog---a probabilistic logical programming approach---to reason
in presence of uncertain probabilities represented as Beta-distributed random
variables. We achieve the same performance of state-of-the-art algorithms for
highly specified and engineered domains, while simultaneously we maintain the
flexibility offered by aProbLog in handling complex relational domains. Our
motivation is that faithfully capturing the distribution of probabilities is
necessary to compute an expected utility for effective decision making under
uncertainty: unfortunately, these probability distributions can be highly
uncertain due to sparse data. To understand and accurately manipulate such
probability distributions we need a well-defined theoretical framework that is
provided by the Beta distribution, which specifies a distribution of
probabilities representing all the possible values of a probability when the
exact value is unknown.Comment: Accepted for presentation at AAAI 201
Attack Detection in Sensor Network Target Localization Systems with Quantized Data
We consider a sensor network focused on target localization, where sensors
measure the signal strength emitted from the target. Each measurement is
quantized to one bit and sent to the fusion center. A general attack is
considered at some sensors that attempts to cause the fusion center to produce
an inaccurate estimation of the target location with a large mean-square-error.
The attack is a combination of man-in-the-middle, hacking, and spoofing attacks
that can effectively change both signals going into and coming out of the
sensor nodes in a realistic manner. We show that the essential effect of
attacks is to alter the estimated distance between the target and each attacked
sensor to a different extent, giving rise to a geometric inconsistency among
the attacked and unattacked sensors. Hence, with the help of two secure
sensors, a class of detectors are proposed to detect the attacked sensors by
scrutinizing the existence of the geometric inconsistency. We show that the
false alarm and miss probabilities of the proposed detectors decrease
exponentially as the number of measurement samples increases, which implies
that for sufficiently large number of samples, the proposed detectors can
identify the attacked and unattacked sensors with any required accuracy
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