We develop a new Location Verification System (LVS) focussed on network-based
Intelligent Transport Systems and vehicular ad hoc networks. The algorithm we
develop is based on an information-theoretic framework which uses the received
signal strength (RSS) from a network of base-stations and the claimed position.
Based on this information we derive the optimal decision regarding the
verification of the user's location. Our algorithm is optimal in the sense of
maximizing the mutual information between its input and output data. Our
approach is based on the practical scenario in which a non-colluding malicious
user some distance from a highway optimally boosts his transmit power in an
attempt to fool the LVS that he is on the highway. We develop a practical
threat model for this attack scenario, and investigate in detail the
performance of the LVS in terms of its input/output mutual information. We show
how our LVS decision rule can be implemented straightforwardly with a
performance that delivers near-optimality under realistic threat conditions,
with information-theoretic optimality approached as the malicious user moves
further from the highway. The practical advantages our new
information-theoretic scheme delivers relative to more traditional Bayesian
verification frameworks are discussed.Comment: Corrected typos and introduced new threat model
International audienceWe develop new algorithms for spatial field re- construction, exceedance level estimation and classification in heterogeneous (mixed analog & digital sensors) Wireless Sensor Networks (WSNs). We consider spatial physical phenomena which are observed by a heterogeneous WSN, meaning that it consists partially of sparsely deployed high-quality sensors and partially of low-quality sensors. The high-quality sensors transmit their (continuous) noisy observations to the Fusion Centre (FC), while the low-quality sensors first perform a simple thresholding operation and then transmit their binary values over imperfect wireless channels to the FC. The resulting observations are mixed continuous and discrete (1-bit decisions) observations, and are combined in the FC to solve the inference problems. We first formulate the problem of spatial field reconstruction, exceedance level estimation and classification in such heterogeneous networks. We show that the resulting posterior predictive distribution, which is key in fusing such disparate observations, involves intractable integrals. To overcome this problem, we develop an algorithm that is based on a multivariate series expansion approach resulting in a Saddle-point type approximation. We then present comprehensive study of the performance gain that can be obtained by augmenting the high-quality sensors with low-quality sensors using real data of insurance storm surge database known as the Extreme Wind Storms Catalogue
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