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In this paper, we study the problem of velocity estimation in vehicular networks by exploiting the sparsity of vehicle velocity trajectory. First, we exploit the sparsity of vehicular velocity trajectories to reduce the beaconing load on the channel. To this end, we propose a sublayer that reduces the number of transmitted packets through a superframe. At each superframe, each vehicle transmits a...
The array output for a distributed source can be approximated by the superposition of the array response to a large number of closely spaced point sources. In the limit, a distributed source corresponds to an infinite number of point sources. In this approximation, the number of free parameters increases with the number of point sources. In this paper, we show that if the point sources (approximation...
In this paper, we study the performance of sensing in mobile sensor networks with imperfect knowledge of neighborhood mobility. We examine the impact of exchanging incorrect mobility information on the cost of sensing and the required target coverage. The study is performed for two target coverage models: an independent coverage model and a Markovian one. We demonstrate via extensive simulations that...
There is a recent interest in developing algorithms for the reconstruction of jointly sparse signals, which arises in a large number of applications. In this work, we study the problem of wide-band spectrum sensing for cognitive radio networks using compressed sensing to exploit the underlying joint sparsity structure in a distributed setting. In particular, we use the recently proposed Approximate...
In this paper, we show that knowledge of mobility helps in sensing and coverage in vehicular sensor networks. First, we propose a mathematical formulation for the stationary sensing-coverage problem in terms of the maximization of a utility function. Then, we propose a method to solve the sensing problem for mobile sensors. We solve the two problems via the branch and bound approximation algorithm...
The sparse nature of location finding makes it desirable to exploit the theory of compressive sensing for indoor localization. In this paper, we propose a received signal strength (RSS)-based localization scheme in wireless local area networks (WLANs) using the theory of compressive sensing (CS), which offers accurate recovery of sparse signals from a small number of measurements by solving an l1-minimization...
In this paper, a novel compressive sensing for manifold learning protocol (CSML) is proposed for localization in wireless sensor networks (WSNs). Intersensor communication costs are reduced significantly by applying the theory of compressive sensing, which indicates that sparse signals can be recovered from far fewer samples than that needed by the Nyquist sampling theorem. We represent the pair-wise...
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